849 results about "Prediction algorithms" patented technology

The invention relates to the technical field of intelligent power grids, in particular to a power distribution network battery digital dynamic management system based on digital twinning. Comprising a data acquisition unit; the digital twinborn modeling unit is used for constructing a battery-power grid-environment multi-dimensional dynamic twinborn body and realizing virtual-real bidirectional mapping and adaptive updating by combining a multi-physics field coupling model and a long-short-term memory network time sequence prediction algorithm; a dynamic optimization unit; and executing the feedback unit. Through a distributed heterogeneous sensing network of a data acquisition unit, multi-dimensional operation data of a battery pack and a key node of a power distribution network are acquired, and a high-fidelity data set containing four-dimensional labels of a battery state, a power grid parameter, time and a position is generated in combination with a spatial-temporal feature extraction technology; the deep fusion of the full life cycle state of the battery and the global operation data of the power distribution network is realized, and the comprehensive data support covering the global is provided for the optimization decision.

The invention discloses a visual language navigation method for cross-modal alignment in a dynamic shielding environment, and the method comprises the steps: collecting multi-modal data through a visual sensor, an inertial measurement unit, a laser radar and the like, and carrying out the preprocessing and time synchronization; sensing the dynamic shielding object through a model composed of a convolutional neural network and a long-short-term memory network, and estimating the future change of the dynamic shielding object in combination with a space-time sequence prediction algorithm; a double-branch convolutional neural network and a Transform based on a dynamic attention mechanism are adopted to respectively extract visual and semantic features and fuse the visual and semantic features; on the basis of occlusion prediction, potential occlusion region features are extracted in advance from a time dimension, an occluded image is repaired by using a generative adversarial network and geometric constraints in a space dimension, and cross-modal feature alignment is optimized through an attention mechanism; planning a path by using a hybrid reinforcement learning algorithm based on a deep Q network-space and a fast exploration random tree, and dynamically adjusting according to real-time shielding; according to the method, the accuracy, adaptability and reliability of visual language navigation in a dynamic shielding environment are improved.

The invention relates to an active splitting and isolated island operation method based on load importance under a disaster condition, and belongs to the technical field of power systems and automation thereof. On the basis of disaster types and power supply area characteristics, constructing a load importance dynamic grading system, optimizing the weight through an analytic hierarchy process-index weight optimization collaborative algorithm, and combining with real-time updating to generate a grading result; the method comprises the following steps: deploying wide-area and local monitoring devices to collect power grid data, setting a safety threshold, triggering a splitting decision when the safety threshold exceeds the limit and the trend deteriorates, calculating an instability rate by using a multi-parameter collaborative instability pre-judgment algorithm, and starting pre-splitting preparation; and the main control center calls a target splitting section optimization algorithm to determine a section in combination with the grading result and the power grid data, generates a splitting instruction after verifying the stability of the island through load flow calculation, and performs classified stability control on the split island. The method can realize accurate load grading, early instability pre-judgment, guarantee of important load power supply such as medical treatment and the like, reduces catastrophe loss, and is suitable for power grid management and control under various disasters.

The invention relates to a standardized project management and intelligent professional scheduling system, and belongs to the technical field of project management and human resource intelligent scheduling. The project standardization module is disassembled into standardization task steps through a business process association rule mining algorithm, and project complexity and resource tensity are adapted by using a task attribute dynamic weight algorithm; the archive matching module constructs professional archives, extracts feature vectors through a multi-criterion decision-weighted bipartite graph matching algorithm, and generates optimal matching pairs in combination with adaptive weight adjustment and an integer linear programming model; the dynamic scheduling module plans a task execution scheme according to a resource constraint scheduling mechanism, and realizes efficient scheduling in combination with a skill supply and demand prediction algorithm and calendar integration; and the quality optimization module adopts a deliverable anomaly detection algorithm to monitor compliance, feeds back an iterative matching and scheduling strategy through a time sequence prediction optimization algorithm, and perfects a skill map based on a map increment updating algorithm. The system realizes a project full-process closed loop.

The invention discloses automatic complete equipment for intelligent predictive maintenance of a primary and secondary fusion circuit breaker. The automatic complete equipment comprises a multi-sensor fusion unit, an edge calculation and analysis module; a parameter interaction module; a predictive maintenance decision unit; the primary and secondary converged communication architecture is used for managing control information and state information on the basis of an IEC61850 (International Electrotechnical Commission 61850) standard; wherein a noise covariance matrix and a feature weight coefficient of the adaptive Kalman filtering health assessment algorithm are dynamically adjusted according to a data quality index and prediction error feedback, and input features of the residual life prediction algorithm based on the LSTM comprise a health index, a change rate and component-level health state information from the health assessment algorithm. Accurate evaluation of the health state of the circuit breaker and accurate prediction of the residual life are achieved, the optimal maintenance strategy is generated, the operation reliability of the circuit breaker is improved, and the maintenance cost is reduced.

The invention discloses a dust removal mechanism and a dust removal method of an inverted laser etching machine for LOW-E glass, relates to the field of dust removal for LOW-E glass laser etching, and solves the problems of dust dissipation, interference between an air curtain and negative pressure airflow, poor adaptability of adsorption force and easy blockage of a filter membrane in the etching process. The mechanism comprises a dynamic sealing dust removal cover, an annular air curtain guiding module, a negative pressure dust collection module, a multi-stage filtering module, a composite adsorption positioning module and an intelligent control module. The method comprises six steps of pretreatment positioning, air curtain-negative pressure collaborative initialization and the like. According to the scheme, airflow interference is eliminated through an air curtain-negative pressure collaborative PID algorithm, an adsorption force two-factor adjustment algorithm is matched with glass characteristics, pre-dedusting is achieved through a dust concentration LSTM prediction algorithm, and the filtering efficiency is guaranteed by combining a multi-stage filtering module with a filtering membrane self-cleaning function; dust concentration can be effectively controlled, glass film layer damage is avoided, dedusting stability is improved, and filtering life is prolonged.

The invention relates to the field of intelligent logistics transportation carbon emission real-time tracking, in particular to an intelligent logistics transportation carbon emission real-time tracking system and method. The method comprises the following steps: firstly, dividing a logistics transportation path into path sections, and generating a candidate path section set at the starting point of each path section to obtain candidate path sections; then, on the basis of the obtained traffic data, vehicle parameters and scheduling plans of the candidate path segments, the predicted carbon emission of the candidate path segments is calculated through a path segment carbon emission prediction algorithm; constructing an optimal path set based on the predicted carbon emissions of the candidate path segments; and finally, on the basis of the path segments in the optimal path set, obtaining actual operation data of the vehicle, and on the basis of the actual operation data of the vehicle, calculating the actual carbon emission. The technical problems that carbon emission prediction and optimization lack pertinence, obvious influences of speed change behaviors such as acceleration, deceleration and idling on energy consumption in actual driving are ignored, and comprehensive influences of vehicle load weight, driving speed and traffic conditions cannot be dynamically reflected are solved.

The invention relates to an intelligent power grid cooperative scheduling method for automobile access, and relates to the field of electric automobile charging scheduling and intelligent power grid optimization. The method comprises the following steps: acquiring multi-source real-time data of an electric vehicle, a power grid and a charging station, constructing a collaborative scheduling graph structure, and performing multi-target optimization to generate an initial charging guide strategy and a charging station resource allocation scheme; then, reinforcement learning iteratively optimizes the guide strategy to dynamically adapt to environmental changes; further optimizing charging time and power parameters through a dynamic adaptive optimization algorithm, and realizing charging station congestion early warning and selection suggestions in combination with a probability prediction algorithm; a charging station power distribution strategy is optimized based on early warning information, fine optimization is carried out by adopting a genetic algorithm, and a scheduling scheme is evaluated and adjusted in real time through a feedback control algorithm. According to the invention, dynamic, refined and global optimization of electric vehicle charging scheduling is realized, the stability of a power grid, the operation efficiency of a charging station and the charging experience of a user are improved, and new energy consumption is promoted.

The invention relates to the technical field of intelligent detection, and discloses a building quality evaluation method and system based on concrete nondestructive testing and a storage medium. The method comprises the steps that a piezoelectric ceramic sensor array is arranged to collect micro-vibration response signals, and an original vibration data set is obtained; extracting an energy distribution coefficient of each frequency band by using a wavelet packet decomposition algorithm, and constructing a damage feature vector matrix; establishing a physical constraint neural network model, and outputting a damage variable time sequence; fusing the damage variable with ultrasonic and rebound data, and calculating comprehensive strength and damage degree indexes; and calculating the remaining service life by using a time sequence prediction algorithm, and generating an evaluation report. According to the method, the technical problem that the existing concrete nondestructive testing technology cannot realize microstructure damage evolution dynamic monitoring and residual life prediction is solved, and the accuracy of building quality evaluation and the scientificity of predictive maintenance decision are improved.

The invention provides a method for rapidly detecting the content of nitrogen oxides in air. The method comprises the following steps: acquiring original signal data of nitrogen oxides in air in a target detection area in real time through a plurality of nitrogen oxide sensors; performing noise reduction filtering processing on the original signal data to generate target signal data; performing signal drift compensation correction on the target signal data based on a preset sensor calibration model to generate standardized concentration data; performing space-time correlation analysis on the standardized concentration data through a nitrogen oxide concentration prediction algorithm model in combination with environment parameter data collected in real time to generate a target prediction concentration value; and comparing the target predicted concentration value with a preset concentration standard threshold value based on a fuzzy logic judgment algorithm, and constructing a detection report. The whole process from data acquisition to report generation is intelligent, and the detection flexibility is remarkably improved.

The embodiment of the invention discloses a distributed data transfer method and device based on fault prediction and a medium, belongs to the technical field of data migration, and solves the problem that when a distributed system breaks down, the timeliness of task completion is seriously influenced. Comprising the following steps: performing health degree evaluation and stable operation duration prediction on nodes in a distributed system through a preset fault prediction algorithm to obtain a node fault prediction result; wherein the fault prediction result at least comprises a node in which a fault is predicted to occur, fault occurrence time and a to-be-transferred task corresponding to the node in which the fault is predicted to occur; performing priority analysis on the to-be-transferred tasks based on the time sequence diagram neural network to obtain task priorities; determining a target node based on the multi-dimensional features corresponding to the nodes and the association relationship between the nodes; and transferring the task to be transferred to a target node through a preset multi-stage progressive transfer strategy according to the node fault prediction result and the task priority.

The invention discloses an intelligent charging priority distribution system and method based on multi-device identification, and particularly relates to the technical field of charging priority distribution. Equipment identity and charging demand information are obtained through an intelligent identification technology, a priority score is calculated in combination with a preset weight parameter, the real-time performance of priority distribution is ensured by adopting a dynamic threshold adjustment strategy, power demand fluctuation in a short time in the future is predicted based on a load prediction algorithm of machine learning, power distribution is optimized in advance, and the power distribution efficiency is improved. The method comprises the steps of reducing instantaneous load impact, monitoring equipment power in real time in the charging process, comparing the equipment power with the maximum distributable power, and dynamically adjusting the charging power or switching the charging sequence through a self-adaptive power adjustment mechanism if abnormality is detected, so as to guarantee the stability of a power grid and the safety of the equipment, and the method can effectively improve the utilization efficiency of charging resources and reduce the energy consumption. Overload of a power grid is prevented, multi-device charging scheduling is optimized, and safety, reliability and intelligence of the charging process are ensured.

The invention provides a method for extracting electrical characteristic signals of electrical equipment in a complex working condition environment, and belongs to the technical field of electrical equipment detection.The method comprises the steps that a multi-channel ultrasonic sensor array is arranged to collect partial discharge signals, background noise is eliminated through adaptive noise cancellation processing, and an ultra-sparse frequency band energy distribution vector is constructed; the method comprises the following steps: calling a self-adaptive time-frequency analysis model to extract instantaneous frequency, amplitude and phase parameters to form a micro-hour-frequency characteristic matrix, separating independent source signals through independent component analysis, calculating a kurtosis value and a skewness value, fusing multi-domain characteristics to construct a transient stationary comprehensive characteristic vector, matching with a standard discharge characteristic vector library to identify the discharge type and intensity, and calculating the discharge intensity. A corresponding prediction algorithm is selected according to the discharge mode, multi-parameter coupling optimization adjustment is started under a certain condition, an electrical characteristic signal description vector is finally constructed, and the technical problem that the partial discharge signal of the power equipment is difficult to accurately extract under a complex working condition environment is solved.

The invention relates to the technical field of tunnel engineering monitoring and construction, and discloses a shield construction tunnel full deformation prediction method based on artificial intelligence. The method comprises the following steps: acquiring a geological sensing data stream, a construction operation data stream and a historical deformation case library; performing multi-modal data fusion on the geological sensing data flow and the construction operation data flow to generate a construction environment map with consistent time and space; searching a case sequence matched with the current construction environment map from a historical deformation case library, performing feature significance evaluation through the case sequence, and identifying a dominant feature group of full deformation prediction; constructing an algorithm preferential engine based on the dominant feature group; activating an algorithm preferential engine to perform parallel processing on the construction environment map, and generating an adaptability measurement set of each algorithm; integrating the adaptability measurement set and real-time construction limiting conditions, and selecting an optimal prediction algorithm by adopting a trade-off decision-making mechanism; and generating a full deformation prediction model, and integrating the model to a shield construction monitoring platform.

The invention provides a sea area digital twinning method and system based on buoy and station data fusion, belongs to the related technical field of sea area digital twinning, and establishes a data quality evaluation mechanism and a similarity data compression mechanism by constructing a multi-source heterogeneous ocean data acquisition system. Intelligent fusion processing of buoy data and station data is achieved, and the problem that in the prior art, the data fusion precision is low is solved; through the self-organizing growth network structure of the ocean dynamic response enhancement model, neuron connection can be automatically adjusted according to the spatial-temporal variation degree of training data, an optimal topological structure is formed, and the defect that a traditional fixed parameter model cannot adaptively process a complex ocean environment is effectively overcome; by establishing a dynamic parameter adjustment mechanism and a multi-algorithm adaptive switching strategy, the most suitable prediction algorithm is automatically selected according to the correlation characteristics of the ocean data sequence, and high-precision digital reproduction of the complex ocean environment is realized.

The invention discloses a burying point analysis system for customer behavior analysis, and relates to the field of customer behavior analysis, the system comprises the following components: a data acquisition module, a digital twin model construction module, a business scene simulation module and a behavior prediction and decision support module; according to the invention, the digital twinborn model which accurately reflects customer behavior characteristics is constructed by using the digital twinborn model construction module and combining a deep learning algorithm and a data mining technology, and the model is imported into a diversified virtual business scene for behavior simulation through the business scene simulation module, so that the customer behavior simulation efficiency is improved. And finally, the behavior prediction and decision support module accurately predicts customer behaviors and generates a personalized decision plan by applying a space-time correlation prediction algorithm and an intelligent decision recommendation algorithm based on a knowledge graph, so that the customer behaviors can be accurately predicted. The functions jointly enhance the insight ability of enterprises for customer behaviors.

Source code for a type of malware is received. For example, the source code may be source code from a type of computer virus. An Artificial Intelligence (AI) algorithm is identified. For example, the AI algorithm may be ChatGPT. The source code of the type of malware is run through the AI algorithm to produce mutated source code for the type of malware. A prediction algorithm is used to predict a signature of the mutated source code for the type of malware. For example, the prediction algorithm is trained using existing source code of different types of malware to generate a prediction model. The signature of the mutated source code for the type of malware is then compared to a signature of a potentially new type of malware to determine if the signatures are similar.

The invention relates to a power load spatio-temporal dynamic knowledge graph construction and load prediction method, which comprises the following steps of: constructing a text and digital sequence hybrid vector coding module, providing a hierarchical entity relationship joint extraction framework oriented to power system load data, constructing a Multi-Encoder-Bi-GRU-CRF power load entity recognition model, and constructing a power load entity model. Constructing a power load spatio-temporal dynamic knowledge graph in combination with a predefined relation rule base; meanwhile, time-space sub-graphs are divided, a space-time coupling self-adaptive adjacency matrix is constructed, and the space-time dependency relationship between nodes is quantified; and finally, combining the knowledge graph node embedded vector and the adjacency relation embedded vector, and jointly extracting the spatial feature and the time feature of the power load by adopting a space-time diagram convolutional neural network. Therefore, the load prediction algorithm provided by the invention not only can give full play to the advantages of multi-modal semantic integration and space-time modeling capability of the knowledge graph, but also can improve the load prediction precision, assist in realizing refined energy management of the power system and assist in making an optimal scheduling strategy, and has a good engineering application prospect.

The invention provides a trajectory prediction method and device based on behavior intention of an unmanned aerial vehicle, a device and a medium, and the method comprises the steps: employing a random forest model to screen multi-source state information of the unmanned aerial vehicle, obtaining flight state key features, and completing feature importance evaluation and precise screening; building a multi-classification maneuvering intention recognition model by adopting a support vector machine based on the flight state key features, and obtaining probability distribution of various maneuvering actions of the unmanned aerial vehicle based on the multi-classification maneuvering intention recognition model; a neural network trajectory prediction model of a coding-decoding structure with a bidirectional gating circulation unit is constructed, probability distribution of various maneuvering actions of the unmanned aerial vehicle is introduced, the probability distribution is used as key semantic constraints to be embedded into a trajectory prediction algorithm, a technical link of multi-link collaborative optimization is formed, and a prediction result of a future trajectory of the unmanned aerial vehicle is obtained. And high-precision prediction of the track of the unmanned aerial vehicle in a complex environment is effectively realized.

The invention provides a high and low voltage power distribution cabinet remote operation and maintenance management and control system based on cloud computing, relates to the field of remote operation and maintenance management, and improves the accuracy and operation and maintenance efficiency of power distribution cabinet fault early warning. The method comprises the following steps: firstly, acquiring sensing data through an edge intelligent sensing module, performing multi-dimensional correlation analysis based on a static safety threshold, identifying an abnormal trend with spatial-temporal correlation, and generating a preliminary early warning; then, a cloud platform prediction decision module drives the digital twin model to carry out dynamic simulation, and a predictive maintenance strategy and a dynamic safety threshold are generated in combination with a prediction algorithm of a historical performance decline curve; and finally, the adaptive control execution module optimizes subsequent analysis by using a dynamic threshold value, and triggers a differential control action from monitoring adjustment to emergency isolation according to a strategy type. According to the invention, by constructing the closed-loop intelligent operation and maintenance architecture of cloud-side cooperation, the operation and maintenance mode transformation from passive response to active prediction is realized, and the reliability and safety of the operation of the power distribution system are significantly improved.

The invention discloses a machine tool fault prediction system based on digital twinning, and the system comprises a data collection module which is used for collecting multi-source heterogeneous operation data of a machine tool; the edge calculation module is used for data preprocessing and feature extraction; the dynamic twinborn model module is used for establishing and updating a digital twinborn model and realizing incremental learning and multi-time scale fusion; the working condition self-adaptive module is used for identifying working conditions and carrying out cross-working-condition knowledge migration; the fault prediction module is used for predicting faults and evaluating cascade influences; the maintenance optimization module is used for generating an optimized maintenance strategy; the visual monitoring module is used for displaying a prediction result and pushing early warning; the data storage module is used for managing historical data and a knowledge base; and the model verification module is used for evaluating and calibrating the model precision. Through the dynamically updated digital twinborn model and the intelligent fault prediction algorithm, accurate prediction of the machine tool fault and optimization of the maintenance decision are realized, and the equipment reliability and the production efficiency are effectively improved.

The invention discloses a smart city gas pipeline safety intelligent monitoring method based on digital twinning, and relates to the technical field of gas pipeline monitoring, and the method comprises the steps: building a gas pipe network digital twinning body of a target city gas pipeline based on digital twinning technology simulation; performing pipeline state calculation on a monitoring blind area pipeline section in the gas pipe network by using an interpolation prediction algorithm and a fluid simulation technology to generate a global monitoring data sequence network; performing pipeline leakage risk prediction on the plurality of pipeline sections according to the global monitoring data sequence network, outputting a plurality of pipeline leakage risk indexes, and constructing a pipeline leakage risk thermodynamic diagram; and identifying and marking a high-risk leakage area as a priority inspection area in the pipeline leakage risk thermodynamic diagram, generating an inspection work order based on the priority inspection area, and assigning the inspection work order to an inspection personnel terminal for manual inspection of pipeline leakage. The technical problems of low efficiency and poor early warning effect caused by blindness of gas pipeline inspection in the prior art are solved.

The invention discloses a method for identifying micro-leakage of a shaft by using distributed temperature gradient sensing data, which comprises the following steps of: acquiring original temperature data of a continuous time sequence of different depths of the shaft through a distributed temperature sensing device, and transmitting the original temperature data to a Modin distributed sensor data processing platform; the platform divides and distributes the data and screens abnormal data; calling a temperature field dynamic evolution prediction algorithm to calculate a predicted temperature value of each depth at each moment; starting a temperature gradient space-time correlation detection algorithm, comparing the predicted temperature with the actual temperature to obtain a deviation sequence, and analyzing and extracting temperature gradient change characteristics; an ultrasonic excitation thermoelastic effect recognition model is applied, ultrasonic excitation is applied, temperature response data are collected, and whether micro leakage exists in the shaft or not and the specific position are determined through comparison and analysis of the incidence relation between the thermoelastic effect and the temperature gradient change. According to the method, an efficient and reliable solution is provided for shaft micro-leakage identification, safe operation of the shaft is guaranteed, and stratum pollution and resource waste are reduced.

The invention provides a deviation rectification prediction method and device for the tail of a battery core material head and electronic equipment. The deviation rectification prediction method comprises the steps that the current initial offset of a to-be-predicted position on a material line of the tail of a target battery core head relative to a preset reference position before winding is started is collected; obtaining a historical initial offset of a to-be-predicted position on a historical battery cell stock line relative to a preset reference position before winding is started, a corresponding historical deviation correction amount after winding is finished and an actual offset of the historical stock line; determining a target deviation correction influence coefficient according to the current initial offset and the historical initial offset; and the target deviation correction influence coefficient acts on the historical deviation correction amount and the actual offset of the historical stockline, and the predicted offset of the to-be-predicted position on the target stockline is determined. The stockline deviation value can be obtained through a prediction algorithm before winding is started, the deviation correction mechanism is driven to complete prediction compensation in advance, error accumulation caused by lagging response afterwards is avoided, and interference caused by error drift and inaccurate calibration of equipment is eliminated.

The invention provides a high-performance virtual scrolling method and system supporting a large-scale tree structure, and the method comprises the steps: constructing a dual-mapping data table structure of the tree structure, which comprises a node mapping table and a sublevel relation mapping table; when it is detected that the node height changes, the virtual rolling rendering engine generates a flattened rendering list in real time based on the unfolding state of the corresponding node in the node mapping table; the virtual rolling rendering engine calculates rendering positions of nodes in the visual area through a dynamic position prediction algorithm; in response to the rendering location and the device type, the virtual scrolling rendering engine dynamically adjusts the virtual scrolling window and the tree structure node hierarchical indentation pitch, and renders in the virtual scrolling window according to the flattened rendering list. According to the application, the data relationship and the rendering position are decoupled through the double-mapping data table structure, the smooth rolling of ten thousand-level nodes can be realized, the memory occupancy rate is reduced, the interaction response speed and the rendering performance are improved, and the method has rolling stability, cross-device display consistency and deployment flexibility.

The invention relates to the technical field of capacitive touch, and discloses a capacitive touch signal self-adaptive compensation method and device. The method comprises the following steps: collecting original capacitance signal grid data in an initial contact stage, and constructing a dynamic contact quadtree; on the basis of the quadtree, a first point position prediction algorithm of contact dynamics perception is operated, and stable coordinate prediction mapping with confidence is constructed; and executing multi-mode first point decision and touch event generation based on the mapping, outputting a touch event and feeding back the touch event to an operating system. According to the method, initial contact signal features are captured through the dynamic contact quadtree, stable coordinate prediction mapping and multi-modal decision are combined, the problem of jumping coordinates in the initial touch stage of an existing system is solved, the touch accuracy of scenes such as vehicle-mounted center control and industrial HMI is improved, the operation risk is reduced, and the application value of the capacitive touch technology is expanded.

The invention discloses an intelligent control method and system for dynamic balance of three floating bodies for crude oil transfer in deep and far sea. The method comprises the following steps: (1) data acquisition and state vector construction; (2) constructing a dynamic response prediction model: constructing a floating body historical motion database based on the initial motion data set, fusing hydrodynamic interference coefficient matrix calculation and multi-body motion mixed constraint analysis, and designing a space-time prediction algorithm based on a graph neural network and a recurrent nerve process to obtain a dynamic response prediction model; establishing a dynamic response prediction model under close-range coupling interference of the three floating bodies; (3) evaluating and correcting data confidence; (4) self-adaptive thrust control and early warning are carried out; and (5) visual monitoring and log management. The problems that in the prior art, positioning precision is low, response lags behind, the abnormal rate of sensor data is high, hydrodynamic coupling interference is not fully considered, and visual monitoring and intelligent early warning are lacked are solved, and the safety, stability and operation efficiency of the deep and far sea crude oil transfer process are effectively improved.

The invention provides an energy-saving compressor intelligent self-adaptive control system based on multi-sensor fusion, which belongs to the technical field of industrial compressor intelligent control and comprises a data acquisition module, a signal conditioning module, a multi-sensor information fusion module, an intelligent control decision module, an execution driving module and a system power supply management module. A state display alarm and remote communication module can be additionally arranged. Multiple sensors collect operation and environment parameters of the compressor, and after conditioning, a comprehensive state evaluation result is generated through D-S evidence theory or fuzzy logic fusion; the intelligent module generates a control instruction based on a load prediction algorithm and a fuzzy PID self-tuning strategy, and the execution module drives the compressor to act; the power supply module supports solar power supply and multi-power supply switching. The system overcomes the limitation that a traditional compressor is single in sensing, rigid in control and the like, comprehensive sensing, accurate evaluation and self-adaptive energy-saving control are achieved, the operation stability is improved, and the energy consumption and the operation and maintenance cost are reduced.

The invention discloses a robot fruit grabbing method based on a multi-mode time sequence collaborative prediction algorithm, and aims to solve the problems that the short-time future trajectory is difficult to accurately predict and the grabbing opportunity is difficult to determine due to fruit and branch swinging caused by wind, branch elasticity and platform advancing. According to the method, a multi-modal time sequence is unified through micro-time alignment, uncertainty is quantified, a three-dimensional special Euclidean group and other variable graph converters are constructed, a spiral shaft constraint projection layer is arranged on the edge between the fruit and a fruit stem, and conditional diffusion short-time trajectory prediction of plum algebra parameterization and uncertainty gating triggering are combined; motion planning and closed-loop control of time delay compensation and collision avoidance constraint are executed in a linkage mode, and the technical effects of high-precision short-time three-dimensional pose prediction, grabbing opportunity self-adaptive triggering and high-success-rate stable grabbing are achieved.

The invention discloses a real-time adjustment camera focusing method and system, and aims to solve the problem of poor anti-interference capability of an existing focusing mode. The method comprises the following steps: off-line construction of an optimization lookup table and on-line real-time focusing: in an off-line stage, constructing a fixed-focus platform, generating an original lookup table through light spot anti-interference preprocessing and centroid calculation, and then constructing an efficient retrieval data structure to obtain the optimization lookup table; in the online stage, images are collected in real time, the center of mass is obtained through anti-interference processing, compensation is conducted through a preset prediction algorithm during shielding, target reference points are matched through an efficient retrieval data structure, and focusing is triggered according to deviation of the center of mass. The system comprises a hardware module and a software module which cooperatively realize the method. The method improves the anti-interference capability of light spot processing and the matching efficiency of the lookup table, adapts to the interference of illumination fluctuation, shielding and the like of an industrial environment, meets the real-time focusing requirements of high-speed production, is compatible with existing hardware, and is convenient to deploy.