95results about How to "Enter exactly" patented technology

The invention discloses a multi-terminal vehicle management system based on an intelligent algorithm, relates to the technical field of vehicle management, and is used for solving the problem of insufficient real-time performance and cooperative efficiency of vehicle management. According to the method, a multi-modal sensor fusion technology is combined with Gaussian process regression, complex environment data is modeled in real time, dynamic target and static background features are accurately extracted, environment changes are analyzed and predicted in combination with a time sequence, accurate input is provided for path planning and task allocation, a path is dynamically adjusted through an incremental optimization algorithm, emergencies are coped, and the path planning and task allocation efficiency is improved. According to a distributed path optimization mechanism, multi-vehicle task path conflicts are eliminated, the cooperation efficiency is improved, a priority model is constructed based on the task emergency degree, the time constraint and the resource matching degree, the weight is dynamically adjusted, vehicle and task matching is optimized in combination with reinforcement learning, the task execution efficiency is ensured, resource competition and path conflicts are solved, and the task execution efficiency is improved. Efficient cooperation in a complex dynamic environment is realized, and the real-time performance and management efficiency of vehicle management are improved.

The invention discloses a roadway rock burst prediction method and device and electronic equipment. The method comprises the steps that a target roadway in a mine is monitored, micro-seismic monitoring data are obtained, and the micro-seismic monitoring data are used for representing the degree of a vibration event of a coal seam and a rock stratum located around the target roadway; based on the micro-seismic monitoring data, rock burst monitoring indexes of the target roadway are determined, the rock burst monitoring indexes are used for representing precursor characteristics and evolution rules of rock burst in the target roadway, and the rock burst is formed by accumulation of internal energy of a coal seam and internal energy of a rock stratum in the vibration event process; the impact strength grade of the rock burst is predicted based on the rock burst monitoring indexes through a prediction model, and the prediction model is obtained by training a neural network model through rock burst monitoring index samples and impact strength grade samples. The technical problem that the prediction accuracy of the rock burst of the roadway is low is solved.

The invention relates to the technical field of natural language processing, and discloses a multi-round dialogue optimization method based on a large language model.The method comprises the steps that before the large language model outputs answers according to current questions input by a user, the current questions input by the user are subjected to question preference alignment, answer preference alignment and preferred item preference alignment in a memory list; and in combination with a self-attention reward iteration function and a model updating rule based on a strategy gradient, an optimal matching historical question and an optimal matching historical answer of the current question are found, an optimal historical question and answer pair of the current question is formed, context window limitation of a large language model can be overcome, accurate historical memory can be provided for the large language model, and the user experience is improved. The context understanding ability and reasoning ability of the large language model in multiple rounds of dialogues are greatly improved, so that the large language model can accurately output coherent and high-quality answers, the quality of the multiple rounds of dialogues is improved, and the user experience is ensured.

The invention provides a gas turbine IGV control optimization method combining a PID controller and a single neuron. The method comprises the steps of obtaining a first system error of the PID controller in a kth control period, a second system error of the PID controller in a (k-1) th control period and a third system error of the PID controller in a (k-2) th control period; generating a proportional control input item, an integral control input item and a differential control input item of the PID controller according to the first system error, the second system error and the third system error; the single neuron determines a first control increment of the PID controller in the kth control period based on a first weight coefficient, a proportional control input item, an integral control input item and a differential control input item, and the first weight coefficient comprises a first proportional coefficient, a first integral coefficient and a first differential coefficient in the kth control period; and adjusting the first weight coefficient according to the first system error and the first control increment to obtain a new weight coefficient. Therefore, online adaptive adjustment of the PID parameters can be realized.

The invention relates to the technical field of wind generating set control, and discloses a three-order semi-tangent type wind generating set position and rotating speed estimation method and system, and the method comprises the steps: converting three-phase power data into rotating two-phase counter electromotive force under rotating coordinates through two times of coordinate transformation and a preset counter electromotive force observer; and then error data of the wind generating set is calculated based on a semi-tangent algorithm, the error data is combined with set operation data to serve as the input of an improved third-order loop filter, the precision of estimation information is improved, more accurate target rotating speed data is obtained, the accurate target rotating speed data serves as the input of a position estimator, and the accuracy of the position estimator is improved. Therefore, more accurate target position data can be obtained; the technical problem of how to accurately estimate the position and the rotating speed of the wind generating set is solved.

The invention discloses a signal enhancement method and device in an industrial plant area, and relates to the field of wireless communication, and the method comprises the steps: firstly, obtaining complete channel state information, and guaranteeing that all decisions are based on real channel characteristics instead of theoretical assumptions; and secondly, if the complete channel state information does not meet the target QoS demand quantization parameter, maximizing a receiving signal to interference plus noise ratio and meeting the target QoS demand are taken as optimization targets, an optimization result is obtained based on channel field intensity distribution information of a preset digital twin model, and finally, the optimization result is controlled and executed to form closed-loop feedback. According to the method, optimization is carried out based on channel field intensity distribution information of a unit space in a digital twin model, and a complex problem needing to be solved by a high-dimensional iterative solution algorithm in related technologies is converted into table lookup and interpolation operation based on space coordinates, so that the calculation complexity is remarkably reduced, the time for obtaining an optimization result is greatly shortened, and the calculation efficiency is improved. Therefore, the time when the terminal is in a poor-quality signal state is greatly reduced.

The invention discloses an AI-based viral pneumonia auxiliary diagnosis method and system, and relates to the field of pneumonia diagnos.The method comprises the steps that abnormal state monitoring is conducted on target people in a target area, and when it is detected that a target human body is in the abnormal state within a preset time period and the occurrence frequency of abnormal behaviors exceeds a preset threshold value, the target human body is judged to be in the abnormal state; mapping the occurrence position, the moving track and the real-time people flow density into a three-dimensional building model to obtain space environment associated data; inputting the space environment associated data into a propagation prediction model to obtain a propagation risk probability of each target sub-region in the target region; and marking the target sub-region of which the propagation risk probability is greater than a preset risk threshold as a target risk region, and generating first early warning information containing the target risk region and the target human body. By implementing the method, the accuracy of the viral pneumonia early warning information can be improved.

The invention relates to the technical field of robot autonomous following, in particular to a robot autonomous following method based on a single-line laser radar. Comprising the steps of collecting laser radar data, performing intelligent processing on the laser radar data to obtain accurate laser radar data, identifying contour features of a following target by using a simultaneous positioning and mapping technology, forming a reference point of the following target, and constructing a map; the constructed map is updated and optimized, and an optimized map is obtained; based on the optimized map and the accurate laser radar data, performing dynamic target tracking optimization and target motion prediction by using a dynamic adaptive path planning and target tracking algorithm to obtain a preliminary dynamic path plan; and based on the initial dynamic path planning, introducing a priority-based task scheduling mechanism to obtain the optimal path planning. The technical problems of low environmental adaptability and low tracing accuracy in the autonomous tracing implementation process of the robot in a closed environment are solved.

The invention relates to a business resource retrieval method and device based on an AI large model, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of obtaining a business file, and determining business file content in the business file; the business file content comprises at least two content elements with different content formats; matching the business file content with a preset business keyword according to the matching priority of the content format corresponding to each content element; key information matched with the business keyword in the business file content is extracted, and the key information is stored in a business resource question and answer database; in response to an input business resource search text, outputting a business resource search result used for responding to the business resource search text based on business file content corresponding to the target key information; the target key information is key information matched with the business resource search text in the business resource question and answer database. By adopting the method, the retrieval result accuracy of the business resources can be improved.

The invention discloses a new energy power grid virtual inertia estimation method and system and a readable storage medium, and belongs to the technical field of power grid control. The method comprises the following steps: acquiring input and output data of a power grid in real time through a PMU; constructing a transfer function model based on the input and output data by using a system identification method so as to determine orders of a pole and a zero point of the transfer function model; setting the order of the AR part as the pole order of the transfer function model, and setting the order of the X part as the zero order of the transfer function model, so as to construct an ARMAX model based on the input and output data; and calculating the virtual inertia of the power grid by using the ARMAX model. According to the invention, the PMU data is collected in real time, so that the system can accurately capture the frequency fluctuation of the power grid. Besides, a transfer function pole and a zero point identified by the system are mapped to the ARMAX model, so that a virtual inertia response strategy can be adjusted in real time, and the frequency stability of the power grid is ensured to be kept under frequency disturbance.

The invention discloses an identification model optimization method based on fruit and vegetable characteristics, an image processing method and equipment, and relates to the field of fruit and vegetable sorting. The core of the scheme lies in innovative application of a deep learning model, sensitivity of the model to different errors is dynamically adjusted by introducing a sensitive weight into a loss function, and prediction accuracy is remarkably improved. The image processing method utilizes a joint mask technology, combines RGB and depth images, accurately extracts fruit and vegetable features, and enhances the recognition capability. And the sorting equipment automatically adjusts the grabbing strategy of the mechanical arm according to the three-dimensional shape and position information of the fruits and vegetables, and precise sorting is achieved. According to the whole scheme, the automation and intelligence level of fruit and vegetable sorting is improved, the labor cost is reduced, fruit and vegetable damage is reduced, the operation efficiency and accuracy are improved, and technical innovation is brought to the fruit and vegetable processing industry.

The present application relates to a kind of user side heterogeneous energy electric carbon collaborative control method and device, and it is related to the technical field of user side energy management and control of integrated energy system.First, the method is characterized by energy grade classification to the user side heterogeneous energy unit in full working condition.Second, the electric-carbon dual-dimension operation demand and flexible adjustment potential of user side are analyzed.Then, a multi-objective optimization model considering electric-carbon collaboration is constructed and solved.The time-sharing scheduling instruction and interface control strategy are generated.Finally, the regulation and control are executed through the multi-port adaptation device, and the standardized access and electric-carbon collaborative optimization operation of user side multi-class energy are realized.The present application can be compatible with multi-voltage grade, heterogeneous response characteristic energy unit plug and play, effectively improve energy utilization efficiency, reduce energy cost and carbon emission, adapt to new power system and double carbon target management and control demand.

The embodiment of the invention provides a sepsis prediction method, electronic equipment and a medium, and the method comprises the steps: inputting expression data corresponding to a plurality of different target genes of a patient into a sepsis prediction model, and obtaining a sepsis prediction result of the patient; wherein the sepsis prediction model is obtained through the following steps that a plurality of first training samples are obtained, and the first training samples comprise sample expression data of all target genes; for each first training sample, inputting the first training sample into a pre-trained analytical model to obtain a training prediction result of the first training sample and contribution relationship information between each piece of sample expression data in the first training sample and the training prediction result; and adjusting the initial sepsis prediction model according to the multiple pieces of contribution relationship information corresponding to the first training samples to obtain the sepsis prediction model. According to the embodiment of the invention, the accuracy and reliability of sepsis prediction can be improved.

The invention provides a photovoltaic measurement and control system, and relates to the technical field of photovoltaic measurement and control, and the system comprises a signal collection unit which is used for collecting sensing data generated by a sensor; the main control unit is used for controlling the whole test unit and summarizing the acquired sensing data into a programmable controller for display and storage; and the upper computer software unit is used for analyzing and processing the acquired data and outputting an operation control command to the lower computer. Sensing data stored in the main control unit are analyzed and processed through the upper computer, monitoring of the state of each module in the photovoltaic system is achieved, and the operation efficiency of the photovoltaic system is improved.

The application discloses an IBCU plunger cavity rigidity self-learning algorithm and relates to the technical field of hydraulic brake-by-wire systems, and comprises the following steps: triggering a plunger cavity sealing detection state in the IBCU power-on process; when the plunger cavity pressure and the plunger position meet the credible condition, acquiring the pressure data of the plunger cavity in the pressure building process and the corresponding plunger cavity liquid consumption volume data. The application acquires the plunger cavity rigidity parameter through self-learning, realizes individual modeling, and improves the parameter accuracy; optimizes the liquid quantity estimation based on the corrected rigidity parameter, improves the pressure building speed and the control accuracy, especially improves the single round pressure building performance; simultaneously compensates the individual differences, enhances the system consistency, makes the theoretical model and the actuality more matched, and thus improves the overall braking performance and stability.

The invention discloses a ship stern bearing interference press-fitting quality prediction method considering complex meteorological conditions. The method comprises the following steps: constructing a full-size multi-physical field simulation model; establishing a fluid calculation environment considering solar radiation; loading temperature field data for structural calculation; simulating a nonlinear contact interference press-fitting process; the press fitting curve, the press fitting contact area, the energy consumption in the press fitting process and the cylindricity error of the stern bearing after press fitting are used as evaluation indexes, and the press fitting failure risk is subjected to multi-dimensional quantitative evaluation. According to the method, the variable characteristics of the real environment temperature and the wind speed serve as the time-varying boundary conditions to be dynamically applied to the model, and quantitative evaluation of the dynamic change of the wind speed on the press-fitting quality is achieved; a solar radiation model and a DO radiation model are set in fluid calculation to realize accurate loading of non-uniform radiation heat flow, so that temperature field distribution of the stern shaft tube is accurately predicted; by constructing a multi-dimensional quantitative evaluation system, various failure risks in the press-fitting process are comprehensively identified.

The invention provides a distributed photovoltaic cluster cooperative scheduling method, system and device based on 5G, and relates to the technical field of photovoltaic clusters, and the method comprises the steps: for each distributed photovoltaic unit in a photovoltaic cluster, determining a mutation unit through distributed mutation point detection, and updating a meteorological-power space-time atlas; analyzing a spatio-temporal variation mode of the power data, tracking a disturbance front causing power fluctuation in real time, predicting a propagation path, a propagation speed and a power influence on a downstream region, and generating disturbance prediction information; performing fluctuation suppression analysis to generate a coordinated regulation instruction; and issuing to a downstream disturbance area through a 5G network, and executing an adjustment action when a disturbance front arrives. The technical problem that in the prior art, scheduling of a distributed photovoltaic cluster is usually locally optimized, a global view angle based on space-time coupling and cognition on space correlation of photovoltaic units are lacked, effective cooperative adjustment cannot be achieved, and then the stability of a power grid is poor is solved.

The application provides a multi-agent collaborative decision-making and disambiguation method and system. The method comprises the following steps: registering the dependency relationship between the task context and the event trigger condition concerned by the agent to the coordination center, updating the dynamic dependency view through the coordination center, analyzing the knowledge update time through the coordination center, and generating a task context identifier set of the agent; constructing a fact set based on the task context identifier set, inputting the fact set into a rule reasoning engine, executing the rules in the collaborative rule base to generate a strategy, and obtaining a collaborative strategy; and distributing the collaborative strategy to the corresponding agent and executing a cleaning process at the end of the task generation period. The application solves the efficiency bottleneck of event routing in a distributed intelligent system from the system architecture level, greatly improves the real-time response capability and scalability of the system in a high-concurrency scenario while ensuring decision consistency.

This invention provides a stable transmission method, system, and medium for high-concurrency industrial data streams, relating to the field of data transmission technology. The method includes: dynamically grouping industrial field devices to obtain multiple groups of edge autonomous devices, constructing multiple local self-organizing cell network topologies; forming a globally optimized topology based on inter-group logical coupling relationships; performing distributed collaborative verification to obtain multiple valid data streams; performing cross-cell network data association verification to obtain W verified compliant data packets; after time-series packet encoding, reassembling them into an asynchronous transmission channel sequence according to deterministic transmission delay requirements, transmitting them to a central computing platform, and then performing time-series packet decoding, restoration, and storage. This invention solves the technical problem in existing technologies where industrial data transmission typically employs a centralized architecture, which easily creates bottlenecks in the data transmission path, leading to increased data transmission latency or even data loss, thereby reducing the overall system's response speed and security.

The invention discloses a system and a method for designing cultural relic and creative products of a cultural relic unit based on artificial intelligence, and the system comprises a login module, a user interface module, an input processing module, a cultural relic database module, a selection module, a generation module, a suggestion module and a storage management module. The system can effectively support the whole cultural and creative product design process from conception to implementation of the user, can enable the user to design by himself / herself to select personal requirements, can improve the interaction and experience of the user, and improves the satisfaction of the user.

The invention relates to the technical field of vehicle navigation, and discloses an airborne closed cabin vehicle path dynamic navigation planning method, which comprises the following steps: S1, navigation initialization, S2, real-time perception data acquisition and correction, and upward navigation reference based on a target established in the step S1, S3, map-free error correction and fusion, S4, dynamic path planning, and S5, accurate correction before landing. And S6, outputting a navigation control instruction based on the real-time navigation track in the step S4. Complete autonomous navigation of an airborne closed cabin vehicle in the whole process is achieved, dependence on external visual reference, a pre-built map or a ground guide signal is not needed, an absolute direction anchor point is provided through a target upward navigation benchmark, multi-source sensor decoupling correction and adaptive Kalman filtering fusion are combined, airflow disturbance compensation is synchronously brought in, and the navigation precision is improved. The inertial navigation accumulative error is effectively eliminated, the method can be adapted to an extreme airborne environment without reference, map and signal, and the independence and reliability of the navigation process are guaranteed.

The invention provides an explosion discharge disaster intelligent evaluation method based on a convolutional neural network, and relates to the technical field of combustible gas explosion protection. The method comprises the following steps: acquiring an explosion discharge disaster assessment multi-dimensional data set; according to the obtained multi-dimensional data set, determining an explosion discharge disaster evaluation model based on a convolutional neural network model; wherein the convolutional neural network model is used for learning and internalizing a high-dimensional mapping relationship between a multi-source working condition parameter and a disaster intensity field in an explosion discharge physical process; acquiring multi-source working condition parameters of a to-be-evaluated environment; and inputting the multi-source working condition parameters of the environment to be evaluated into the explosion discharge disaster evaluation model to obtain the on-way distribution of the explosion discharge peak overpressure, the peak temperature and the peak wind speed, thereby realizing the efficient and accurate prediction of the disaster peak at any position on the whole propagation path after the explosion discharge.

The utility model discloses a self-service naming machine, which relates to the technical field of self-service terminal equipment and comprises a naming machine main body, a sealing plate, a keyboard and a positioning structure, the lower end of the sealing plate is hinged to the naming machine body. The keyboard is located between the sealing plate and the naming machine body, one end of the keyboard is hinged to the upper end of the sealing plate, and the other end of the keyboard is vertically and slidably connected with the naming machine body. The positioning structure is located at the hinged position of the sealing plate and the keyboard and enables the keyboard to be positioned in a horizontal state and a vertical state respectively. The self-service naming machine is provided with the keyboard, so that names can be conveniently and accurately input; the keyboard can be hidden, so that pollution of dust to the keyboard can be reduced; the keyboard is small in thickness after being hidden, and meets the light and thin requirements of a terminal.

The utility model discloses a multi-strand twisting processing device for high-elasticity polyester yarns, and relates to the technical field of twisting processing. Comprising a mounting bracket, a first guide ring on the inner wall of a vertical rod and a second guide ring on the inner wall of an inclined rod are used for preliminarily guiding silk threads led out of a polyester silk winding drum, so that the silk threads move according to a preset path, shaking and winding of the silk threads are reduced, and the stability of the silk threads entering a subsequent processing link is ensured; the guide shell is of a cone-shaped structure, the lower end of the guide shell is open, the rubber ring is fixed to the edge of the guide shell, six strands of dispersed silk threads can be concentrated, the rubber ring plays a role in buffering and protecting the silk threads, and the silk threads are prevented from being damaged by friction with the edge of the guide shell; the guide pipe of the arc-shaped structure conducts secondary guiding on the concentrated silk threads, so that the silk threads enter the positioning ring more accurately, the position accuracy of the silk threads in the twisting process is further guaranteed, and the twisting stability and quality are improved.

The present application relates to the technical field of temperature control, in particular to a temperature control method and device for seedling greenhouse cultivation environment and a storage medium. The method comprises the following steps: establishing a greenhouse temperature influence model, the greenhouse temperature influence model can predict the greenhouse air temperature according to the greenhouse soil temperature, and the influence of the outside air temperature on the greenhouse air temperature is considered for optimization; obtaining the target soil temperature range and the target air temperature range of the current seedling growth stage, when it is monitored that the real-time greenhouse air temperature is not in the target greenhouse air temperature range, the air temperature adjustment strategy is started, and whether the greenhouse soil temperature is in the target greenhouse soil temperature range is judged, if the greenhouse soil temperature is not in the target soil temperature range, the soil temperature is adjusted. The method greatly improves the response speed and adaptability to environmental changes, and provides more stable and suitable growth conditions for seedlings.

The invention relates to the technical field of oil quality analysis, in particular to an aviation oil property analysis method and system based on spectral oblique projection. According to the method, when the oblique projection operator is constructed, the limitation of a traditional oblique projection operator is broken through through dynamic multi-component cooperation and self-adaptive residual error separation, key information which is in complex association with properties in the aviation oil spectrum is extracted more accurately, the pertinence and robustness of feature extraction are improved, and the accuracy of feature extraction is improved. Higher-quality input variables are provided for an aviation oil property prediction model, and the method is a core technical support for realizing high-precision aviation oil property analysis.

This invention discloses an intelligent projection positioning and drilling accuracy control system for blast holes at tunnel faces, belonging to the field of tunnel drilling and blasting construction technology. It aims to solve the technical problems of inaccurate blast hole positioning and large drilling direction deviations in existing tunnel face systems, leading to low construction efficiency. The key technical points are: the control system includes a structured light projector mounted on a tunnel drilling and blasting trolley, at least two industrial cameras, a fiber optic shape demodulation module, and a controller. A fiber optic shape sensing unit is installed inside the drill rod at the end of the drill arm of the drilling and blasting trolley; the structured light projector projects structured light, the industrial cameras acquire binocular images, the fiber optic shape demodulation module calculates the curvature and torsion angle sequence of the sensing unit, and the controller controls the drill arm drive mechanism to correct the drill bit position and direction through 3D reconstruction, coordinate comparison, and real-time deviation adjustment. This invention achieves intelligent positioning and drilling accuracy control of blast holes at tunnel faces, improving the accuracy and efficiency of tunnel drilling and blasting construction.

This invention relates to the field of medical technology, specifically to a method for monitoring anesthesia depth based on a neural network model. It includes the following steps: collecting physiological indicator data of patients under different degrees of anesthesia; preprocessing the physiological indicator data to obtain interference-free physiological indicator data; and transmitting the processed physiological indicator data to a data management system; constructing a neural network model; inputting the physiological indicator data into the neural network model through the data management system; and training the neural network model. This invention, by collecting physiological indicator data of patients under different degrees of anesthesia to establish a training dataset, and training the neural network model, enables the neural network to accurately predict the patient's anesthesia depth. This method of collecting and processing the training dataset provides sufficient data samples, allowing the neural network model to be trained on diverse data, thus improving the model's generalization ability.

This invention discloses an intelligent control method and system for BFB biomass boilers based on DCS, relating to the interdisciplinary fields of industrial process automatic control and artificial intelligence. It includes: a multi-source heterogeneous data synchronous acquisition module for acquiring data; an intelligent fusion and construction module for operating condition features that dynamically aligns, adaptively normalizes, and constructs combustion state features based on process delay mechanisms to form a unified operating condition feature vector; a digital twin-driven multi-objective collaborative optimization decision-making module that uses a dynamic digital twin model constructed from a physical information neural network as a predictor and employs a multi-agent optimization framework based on a two-layer interactive collaborative evolution algorithm to solve in real time a collaborative control instruction set that simultaneously optimizes combustion efficiency, initial pollutant concentration, and operational economy; and an instruction secure execution and adaptive fault-tolerant control module that securely issues and executes the instructions.

The invention discloses an energy flow distribution method and device for a fuel cell combined cooling heating and power system. The method comprises the following steps: acquiring power side data, cold and hot side data and global data of the fuel cell combined cooling heating and power system; respectively predicting a power supply and demand difference corresponding to the power side data, a minute-level load change corresponding to the cold and hot side data and a next-day load curve corresponding to the global data through a multi-time scale distributed prediction algorithm; adjusting the battery operation power of the fuel battery combined cooling heating and power system in real time according to the power supply-demand difference; adjusting the running state of a cooling and heating device of the fuel cell cooling, heating and power combined supply system according to the minute-level load change and the cell running power; a multi-target optimization model is constructed according to the next-day load curve, and the running state of a hydrogen production device of the fuel cell combined cooling heating and power system is adjusted in combination with the multi-target optimization model; the overall energy utilization rate of the fuel cell combined cooling heating and power supply system is improved through overall cooperation, and the real-time performance of power adjustment is guaranteed.