596 results about "Online optimization" patented technology

The invention discloses an energy acquisition and monitoring system based on big data, particularly relates to the field of energy monitoring, is used for solving the problem of energy consumption abnormity identification and management optimization in steel production, and is characterized in that energy parameters are acquired in a production process, and an energy signature reference model of multi-dimensional energy consumption characteristics is constructed based on historical data and equipment operation characteristics; an accurate reference is provided for dynamic comparison and anomaly detection; potential anomalies are accurately identified by utilizing comprehensive analysis of energy consumption deviation distribution and load characteristics, and key influence factors and root causes are deeply mined through a multivariable machine learning algorithm; in combination with a predictive scheduling engine and an online optimization algorithm, process parameters, equipment running states and task schedules are dynamically adjusted, abnormities are effectively eliminated, and energy consumption balance is recovered; therefore, the real-time monitoring and intelligent anomaly detection of the process energy consumption are realized, the refinement level of energy management is improved, the energy utilization efficiency and production stability of iron and steel enterprises are improved, and the potential production risk is reduced at the same time.

The invention provides a digital human interaction control method and device fusing emotion semantics and logical reasoning and a storage medium. The method comprises the following steps: analyzing multi-modal input data of a user, constructing emotion-semantics joint representation, and generating a logic decision path; and through a cognitive fusion module, emotion-semantic representation and a logic decision path are fused, and an interaction response adapting to emotion and logic consistency is generated. The system optimizes an emotion semantic model and a logical reasoning rule on line according to user feedback and interaction history, and real-time interaction of emotion dynamic and logical rules is achieved. The system can dynamically adjust the logic decision path based on the multi-mode emotional state of the user, and improves the naturalness and situation adaptability of interaction. A dynamic time warping algorithm and a factorization machine are introduced to process a multi-modal feature fusion problem, and the accuracy and robustness of emotional state recognition are improved. The online optimization mechanism enables the model and the rule to be evolved continuously, and reasoning errors are corrected automatically through user feedback, so that error circulation is avoided.

The invention discloses a slope instability sliding real-time early warning method based on an improved machine learning algorithm. The method comprises the following steps: S1, outputting a consistent slope monitoring data set; s2, constructing a slope monitoring map structure based on the consistent slope monitoring data set and the spatial position information of each sensor; s3, outputting a slope state feature vector; s4, performing online optimization on key parameters of the dynamic graph attention residual image convolutional neural network model by using an adaptive particle swarm optimization algorithm, and outputting an optimized dynamic graph attention residual image convolutional neural network model; and S5, re-mapping the consistent side slope monitoring data set to generate a new side slope state feature vector, judging an instability sliding risk in the side slope state according to a comparison result between the side slope state feature vector and an early warning threshold value, and generating side slope instability real-time early warning information. According to the method, the risk that too many invalid connections are established in a state stable region is effectively avoided, and the physical rationality and the anomaly capture capability of the slope map in the actual instability trend are enhanced.

The invention provides a micro-grid energy management method and system based on deep reinforcement learning, and relates to the technical field of power grids, and the method comprises the steps: constructing a dual-time scale deep reinforcement learning model which comprises a day-ahead scheduling sub-network and a real-time scheduling sub-network; the day-ahead scheduling sub-network predicts micro-grid operation strategies at a plurality of time points in the future based on the long short-term memory network; the real-time scheduling sub-network is based on a depth deterministic strategy gradient algorithm, real-time state data and a day-ahead scheduling prediction result are fused to construct an evaluation function, an instant reward value is calculated, and renewable energy power generation, energy storage charging and discharging and an external power grid electricity purchasing and selling power adjustment instruction are optimized online. According to the invention, through dual-time-scale collaborative optimization, the energy management efficiency and economic benefits of the micro-grid are improved, and the operation stability of the micro-grid is enhanced.

The invention provides a thermal power plant flexibility transformation and deep peak regulation optimization control method and system, and relates to the technical field of control engineering, and the method comprises the steps: collecting the omnibearing operation data of a thermal power plant to construct a multi-level thermal power plant dynamic nonlinear model, carrying out the real-time identification of model parameters, generating an identification result, and building a multi-dimensional thermal power plant operation characteristic database; based on the thermal power plant operation characteristic database, designing a transformation scheme, and performing visual simulation and optimization to obtain a flexible transformation result; constructing a multi-objective optimization function, formulating an adaptive deep peak regulation optimization control strategy, carrying out short-term and medium-term prediction on a power grid load, generating an initial control strategy based on a prediction result, carrying out online optimization and adjustment, and continuously evaluating and optimizing the control strategy.

The invention provides an agent strategy generation and online optimization method based on dynamic scene perception, and the method comprises the steps: obtaining a scene demand description of a user, and carrying out the analysis of the scene demand description, so as to generate a target task sequence which can be executed by an agent disposed in a target scene; dynamically sensing the current environment characteristics of the target scene to generate a dynamic semantic topology network and generate a dynamic scene graph according to the dynamic semantic topology network; on the basis of the dynamic scene graph, hierarchical modeling of the incidence relation is carried out on the behavior space corresponding to the intelligent agent, and behavior semantic features containing scene perception are generated; the behavior semantic features containing scene perception are mapped to an intelligent agent strategy representation space, and a behavior feature strategy for controlling an intelligent agent to execute a target task sequence is obtained; and according to the determined scene value representation, decomposing the behavior feature strategy to obtain an advantage estimation value adapted to the scene so as to carry out online optimization on the behavior feature strategy. According to the invention, the intelligent agent strategy can accurately adapt to the requirements in the business process of an enterprise.

The invention discloses a seabed tracked robot path planning method and system based on reinforcement learning, and the method comprises the steps: obtaining environment data in real time through multiple sensors, extracting submarine topography three-dimensional features, obstacle distribution and ocean current dynamic parameters in combination with a neural network, and carrying out the combined feature extraction; establishing a seabed environment simulation model, and synthesizing various landform training data by using an adversarial generation technology; a hierarchical reinforcement learning framework is designed, a global layer collaboratively optimizes a long-distance path through a distributed agent, a local layer designs a high-frequency control strategy, and the global and local strategies realize multi-dimensional collaborative optimization through a dynamic weight adjustment mechanism; model parameters in a dynamic environment are updated in real time through an online optimization module, and a simulation strategy is quickly deployed to an entity robot through transfer learning. According to the invention, a neural network feature extraction and multi-level reinforcement learning collaborative autonomous decision-making system is constructed, and a high-reliability and low-energy-consumption autonomous operation solution is provided for a deep sea operation scene.

The invention relates to the technical field of charging pile power distribution, and discloses a charging pile dynamic power distribution method and device based on load prediction, and the method comprises the steps: carrying out the system identification analysis of the real-time operation data of a charging pile, and constructing a charging pile system small signal model; training to obtain a low-sensitivity load prediction model and a high-sensitivity load prediction model, and generating charging load prediction data; performing control optimization calculation to obtain a dynamic power compensation control parameter; genetic optimization and multi-stage power distribution are carried out, and a multi-stage power distribution scheme including a conventional load distribution mode and a peak load distribution mode is obtained; power limitation is carried out by reducing charging power of non-priority users, load transfer is carried out through power coordination of adjacent charging piles, an emergency power adjustment control instruction is obtained, a closed-loop optimization mechanism is adopted, and stable operation of a charging pile system under various working conditions is guaranteed through parameter online optimization and dynamic adjustment of a control strategy.

The invention discloses a smelting waste gas recycling optimization system based on fuzzy control, and relates to the technical field of automatic control. The problems of low control precision and response lag caused by the fact that an existing static membership function cannot adapt to multi-scale working condition changes in real time are solved. Comprising a sensing acquisition module, a membership reconstruction module, a feature decoupling module, a coupling compensation module, a rule optimization module and an execution control module. Standardized data are acquired through sensing acquisition and filtering, an adaptive membership function is constructed by adopting incremental fuzzy clustering, and closed-loop self-correction of a multi-modal control instruction is realized by combining wavelet packet decoupling, feed-forward compensation and double-depth Q network reinforcement learning online optimization; the waste gas desulfurization efficiency, energy consumption optimization and treatment system stability of waste gas treatment can be effectively improved.

The invention relates to the technical field of power grid energy storage systems, in particular to a battery intelligent fault diagnosis method and system suitable for a power grid energy storage system. According to the method, multi-source monitoring data including battery electrical parameters, power grid features, environmental conditions and historical operation records are obtained, unified time step alignment and feature extraction processing are carried out on the data, and statistical features, time sequence features and frequency domain features are obtained. A double-layer diagnosis model structure is adopted, a random forest model is used for diagnosing main fault modes such as over-charging and over-discharging, and a convolutional neural network model is used for diagnosing complex fault modes such as short circuit and temperature out-of-control. The system has a dynamic updating mechanism, and online optimization is realized by calculating incremental updating values of model parameters; meanwhile, a model evaluation mechanism is established, and when an evaluation index is lower than a preset standard, a retraining process is triggered. According to the invention, high-precision and real-time identification and early warning of the battery fault are realized, and the operation reliability and safety of the energy storage system are significantly improved.

The invention relates to a talent matching and intelligent recruitment method and system based on an AI large model. The method comprises the steps of obtaining multi-modal data of candidates and post demand data of recruiters; performing semantic extraction and feature fusion on the multi-modal data through a multi-modal fusion encoder to generate a comprehensive feature vector containing semantic association features and behavior features; performing field adaptation processing on the comprehensive feature vector based on a dynamically updated industry knowledge graph to generate a skill label set of candidates; performing dynamic matching degree calculation on the skill label set and post demand data according to historical recruitment feedback data by adopting a reinforcement learning model to generate a recommendation list; and outputting the recommendation list to a recruiter terminal, and performing online optimization on the reinforcement learning model based on operation behavior data of a recruiter. According to the invention, intelligent matching of the job seeker and the recruitment demand is realized, and the recruitment efficiency and accuracy are improved.

The invention provides an electric instrument table intelligent control method based on multi-modal perception and model prediction, and relates to the technical field of electric instrument tables, and the method comprises the steps: obtaining high-precision environment perception data through a multi-modal sensor fusion technology, and constructing a dynamic three-dimensional map to recognize an instrument and an obstacle; the system drives the multi-degree-of-freedom mechanical arm to move efficiently through optimal path planning based on model prediction control, the operation period is remarkably shortened, the overall operation efficiency and throughput capacity are improved, meanwhile, potential collision and abnormal stress are monitored in real time in the grabbing and placing process, an intelligent obstacle avoidance and safe shutdown mechanism is started, and the safety of the robot is improved. According to the method, the robustness and safety of system operation are greatly improved, a flexible grabbing strategy is integrated for precious fragile instruments, lossless operation is achieved through real-time force feedback, high-value samples are effectively protected, finally, through machine vision verification and online optimization, the system can continuously conduct self-learning, the operation precision is continuously improved, and the success rate is continuously increased. And the self-adaptive performance is improved.

The invention provides an injection molding process quality prediction and process parameter sliding window online optimization method and system. The method comprises the steps of obtaining time sequence process parameter data and batch statistical data; constructing and training a CNN-PCLSTM quality prediction model to output a product quality prediction value; dividing a sliding window in the time sequence process parameter data; constructing a loss function based on a deviation between a product quality prediction value predicted by the CNN-PCLSTM quality prediction model and a preset expected product quality target value, performing iterative optimization calculation based on the loss function, and adjusting a time sequence process parameter set value in the sliding window; updating the process parameter set value in the sliding window obtained through iterative optimization calculation to a control system in the injection molding process; and moving the sliding window and repeating the steps. According to the method, the problems of low quality prediction precision and insufficient optimization efficiency in the prior art are effectively solved.

The invention discloses a train key component online health monitoring method based on multi-sensor signal cooperation, and the method comprises the steps: selecting an indirect signal as an edge end train online abnormal state early warning index based on the correlation analysis of multi-sensor signals, and diagnosing whether the state of a train key component is abnormal or not; the vibration signals serve as signals for further diagnosis at the cloud, state features of the vibration signals are extracted at the edge end, online optimization of sparse coding is carried out, and the vibration signals are transmitted to the cloud to serve as diagnosis signal features for further diagnosis; real-time training precision and sample complexity are integrated into online estimation of a model training process, optimal features are transferred from a source domain-edge end to a target domain-cloud end, and the cloud end diagnoses the type and grade of a fault by means of the complex operation capability of the cloud end; a dynamic aggregation strategy is provided, the features of a prediction model are determined, optimization of nodes is enhanced, and a feature network is made to adapt to and complete an online prediction task.

The invention discloses a self-adaptive low-delay motion scene live broadcast method and system, and particularly relates to the technical field of scene live broadcast. Through unified mapping and exception suppression of multi-source time sequence data, a multi-scale sliding window predictor and a short-time autoregression and long-time trend sensing algorithm are combined; a more accurate bandwidth prediction result with interval confidence description is generated, characteristics such as offset cumulant, fluctuation intensity and error residence time are extracted by using a residual trajectory, threshold crossing frequency, switching amplitude, direction alternation rate and critical zone residence duration are analyzed synchronously with a parameter switching log, critical oscillation characteristics are formed, and the bandwidth prediction accuracy is improved. The risk identification is more accurate, the high-frequency oscillation risk score of the system is calculated through a normalization and time sequence risk identifier, the risk assessment result is mapped into an executable stable intervention strategy, the system state observation sequence after adjustment execution is subjected to short-time assessment, and a feedback packet is formed to write back a closed loop. And online optimization of the weight, the decision threshold and the cooling time of the bandwidth predictor is realized.

The invention provides an electric heating gas energy storage collaborative charge state disturbance suppression optimization method, and relates to the technical field of information, and the method comprises the steps: carrying out the filtering processing of a second-level disturbance signal in the real-time operation data of a power grid through an adaptive Kalman filtering algorithm, so as to improve the signal-to-noise ratio; performing time-frequency domain feature extraction on the filtered disturbance signal by using a wavelet analysis method, and judging the type and severity of the disturbance signal in combination with a preset disturbance mode library; and according to the type and characteristics of the disturbance signal. According to the method, an electric energy storage collaborative optimization control model is constructed by adopting a robust optimization theory, the uncertainty of disturbance signals and electric energy storage characteristics is considered, an optimal power instruction is solved, in the control process, the state of an electric energy storage unit is dynamically evaluated, a strategy is adjusted, online optimization is carried out according to power grid index changes, the control effect is continuously improved, and the control efficiency is improved. Through cooperative optimization control of electric energy storage, second-level disturbance of the power grid is effectively suppressed, and safe and stable operation of the power grid is guaranteed.

The invention relates to the field of robot control, and discloses a robot control method and system, and the method comprises the steps: carrying out the fusion sensing of environment data through a multi-modal sensor, and constructing a dynamic environment model through the combination of the clustering segmentation of a dynamic obstacle and the geometric matching of a target cart; generating a Nash equilibrium obstacle avoidance path based on a non-cooperative game framework, and coordinating motion tracks of multiple robots by using distributed model predictive control and an alternating direction multiplier method; a heterogeneous grabbing pose generation network is designed, simulation and real scene feature distribution are aligned through domain adversarial training, and online optimization of a grabbing strategy is achieved in combination with incremental learning; newly-added obstacles and cart deviation are monitored in real time to trigger dynamic re-planning, and the path and the grabbing pose are updated through a closed-loop feedback mechanism. According to the method, the cooperative control robustness in a dynamic environment is improved, the obstacle avoidance efficiency and the grabbing stability are both considered, and the method is suitable for automatic operation in complex scenes such as airports.

The invention relates to the technical field of inspection control, and provides a self-adaptive inspection control method and system for an electric inspection robot. Space-time alignment and fusion modeling are carried out on the multi-modal environment data to obtain a three-dimensional environment model, and dynamic path planning is carried out on the three-dimensional environment model according to the position information of each target robot and a target inspection point list to generate an anti-interference elastic path. Performing motion parameter dynamic optimization by combining the real-time dynamic parameters of each target robot to generate an anti-interference control instruction, and calculating the detection distance between each target robot and the to-be-detected target according to the position information; and when the detection distance is smaller than a distance threshold, performing anomaly detection and classification on the to-be-detected target to obtain a corresponding fault level label, and performing multi-machine cooperative task allocation on the fault level label according to the global scheduling information to obtain an optimal inspection queue. Through real-time planning, online optimization and multi-machine cooperation, the safety, reliability and inspection efficiency of the electric power inspection robot are improved.

The invention discloses a multi-target dynamic optimization hydrogen energy unmanned aerial vehicle energy management method and system under an MPC framework, belongs to the technical field of energy management, comprehensively considers factors such as energy consumption, battery degradation and output fluctuation, and realizes safe and efficient operation of a hybrid power system based on a predictive control idea. The management system comprises a sensing subsystem, a control subsystem, an interaction subsystem and a power supply subsystem. The management method comprises the following specific steps: adjusting a predictive control time domain according to a historical demand power sequence; updating the aging parameter of the hydrogen fuel cell, and fixing the weight coefficient of the cost function; through a multi-target online optimization algorithm, obtaining an optimal SOC change curve under a prediction time domain; and outputting the reference power of the fuel cell through linear MPC control according to the reference change curve.

The invention discloses an environmental data processing method and system based on ocean engineering, and relates to the technical field of data processing, and the method comprises the steps: receiving an original observation data flow through a multi-source data preprocessing module, and carrying out the dynamic noise filtering and abnormal value adaptive detection; fusing the multi-source heterogeneous data through a multi-scale data fusion module, and embedding the fused multi-source heterogeneous data into a marine kinetic equation as a soft constraint; non-linear evolution features are extracted from the fusion data through a feature extraction and state representation module, and a high-dimensional environment state vector is constructed; real-time prediction of model parameters is executed through online learning and an inference engine; and performing uncertainty propagation calculation on the processing flow through a confidence evaluation module and generating a final environment state report. According to the method, the adaptive capacity of data preprocessing can be remarkably improved, the physical consistency of multi-source data fusion is improved, the nonlinear evolution law of ocean phenomena is accurately captured, and continuous online optimization and edge side low-delay response of model parameters are achieved.

The invention discloses a three-dimensional modeling method and system, and relates to the technical field of intelligent perception and three-dimensional reconstruction, and the method comprises the steps: taking BIM as a priori, sampling a geometric entity as a visibility evaluation point cloud, and constructing a graph structure environment state under the constraints of a field of view, distance measurement, an incident angle, overlapping and other sensors; on the basis, a deep reinforcement learning agent which is pre-trained by a synthetic scene and subjected to domain randomization migration is introduced, stations and scanning parameters are selected online according to an observation-decision-execution-update closed loop, visibility and coverage are re-estimated after each step of scanning, and self-adaptive correction is carried out on an unexecuted sequence in combination with online optimization; compared with an off-line global optimization method, the method has the advantages that a search space is effectively compressed through candidate station pre-generation and visibility gating, and continuous and adjustable balance is formed among coverage, point cloud quality and operation time by multi-target awards; for engineering constraints such as temporary shielding, site reachability, registration overlapping degree and the like, the strategy can be dynamically replanned in an execution period.

The invention relates to the technical field of refrigeration house energy-saving control and digital twinning, in particular to a refrigeration house fan energy-saving operation method and system, and the method achieves the whole-field temperature deduction of a sensor-free area by constructing a CFD reference model and simulating and predicting the three-dimensional transient airflow and temperature distribution in a refrigeration house. Model parameters are calibrated through measured data, and the order of the model is reduced by adopting intrinsic orthogonal decomposition and Galerkin projection, so that the calculation amount is remarkably reduced, and online optimization is supported. And in combination with a data assimilation algorithm, the prediction result of the reduced-order model is continuously corrected, and the adaptability to environment change and system aging is improved. The optimization control stage takes minimization of the total energy consumption of a fan as a target, solves an optimal fan control sequence under the hard constraint that the whole-field temperature does not exceed the cargo safety upper limit and does not exceed the cargo safety lower limit, and adopts a rolling time domain mode for execution to realize collaborative optimization of safety and energy conservation.

The invention provides a multi-mode task arrangement and intelligent execution method and system for an intelligent car, and relates to the technical field of task arrangement, and the method comprises the steps: carrying out the clustering analysis of historical task data through a deep learning algorithm, and constructing a task model library; based on a new task instruction matching execution mode, constructing and optimizing a decision tree, and generating an optimal execution path; converting the execution path into a control instruction sequence; environment data are collected in real time when a task is executed, and online optimization is carried out based on a deep reinforcement learning model; the execution data is fed back to the task model library, and the follow-up execution efficiency is improved.

The invention discloses a thermal spraying coating thickness online optimization control method based on digital twinning, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: synchronously collecting multi-modal online observation data and process control observation data in a thermal spraying process, carrying out the time-space alignment, and generating a multi-modal observation set; performing adaptive optimization of multi-modal uncertainty gating on the candidate control set according to the credible thickness field confidence distribution map to generate a control instruction sequence; and the control instruction sequence acts on the thermal spraying process, response feedback data are continuously collected in the execution period, a new multi-mode observation set is generated according to the response feedback data, and real-time closed-loop optimization control is kept. According to the method, the thickness field with high credibility and the credible thickness field confidence distribution diagram can be generated through forward generation and uncertainty quantification of physical guidance, and depth coupling of multi-modal data and physical rules and accurate mapping of uncertainty are achieved.

The invention discloses a hierarchical processing system for energy storage data, and the system comprises an optimization feature extraction module which collects initial energy storage data, builds a unified time sequence on the time dimension of the initial energy storage data through a specified time interval # imgabs0 #, carries out the alignment sampling, introduces the initial energy storage data after the alignment sampling into an auto-encoder, and carries out the noise reduction processing, performing feature extraction on the data subjected to noise reduction processing to obtain optimized energy storage feature data; the dynamic grading adjustment module is used for standardizing the optimized energy storage characteristic data, calculating the information entropy of the standardized characteristic to obtain an initial weight, introducing a Q reinforcement learning algorithm to carry out online optimization on the weight, designing a third-order grading model based on the optimized initial weight, and carrying out dynamic grading adjustment on the optimal energy storage characteristic data; and performing data classification on different optimization energy storage characteristic data combinations and dynamic weights according to a third-order classification model, dividing the data into an emergency level, an important level and a common level based on the third-order classification model, and providing targeted processing and management strategies for different levels of data.

The invention provides an AGC hydropower station intelligent control method based on multi-source data fusion. Constructing a control feature vector of the multi-dimensional feature; performing spatial-temporal feature modeling on the control feature vector, and extracting a time sequence dependency relationship between power grid load change and hydraulic dynamic response and a spatial coupling effect between units; establishing a multi-objective optimization function, and dynamically adjusting the weight coefficient of each objective through fuzzy logic according to the current working condition; a self-adaptive differential evolution algorithm is adopted to carry out on-line optimization on an active power distribution coefficient of a unit and PID parameters of a speed regulator, and the requirements of guide vane opening change rate constraint and water hammer effect avoidance are met. According to the method, multi-source heterogeneous data such as power grid, hydraulic engineering and equipment states can be effectively fused, multi-target dynamic optimization control is realized through the space-time attention model and the adaptive differential evolution algorithm, and the control precision, the response speed and the equipment operation safety of the hydropower station AGC system are remarkably improved.

The invention discloses a multi-objective optimization method and system for a regional integrated energy system, and the method comprises the steps: constructing a multi-source input sequence sample; inputting a multi-source input sequence sample into the wind power and photovoltaic prediction model for processing, obtaining a current wind power and photovoltaic output optimization prediction result, calculating the current wind power and photovoltaic output optimization prediction result and a really constructed sample, obtaining a combined loss function value to train the model, obtaining the trained wind power and photovoltaic prediction model, processing the sample collected in real time, and obtaining a wind power and photovoltaic output prediction result. Outputting current wind power and photovoltaic output optimal values; and constructing an online optimization model of the integrated energy system, performing online optimization solution on the constructed model by an accelerated particle swarm optimization algorithm to obtain a control strategy of the energy system, issuing the control strategy to the energy equipment, and performing multi-target optimization scheduling. According to the method, the wind power and photovoltaic prediction model is combined with the accelerated particle swarm optimization algorithm, prediction errors are fully considered, and the stability and flexibility of the regional integrated energy system are enhanced.

The invention relates to the technical field of self-adaptive pipe network dynamic hydraulic balance online optimization, and discloses a self-adaptive pipe network dynamic hydraulic balance online optimization method. By constructing a node-pipeline topology model and defining a control variable and a threshold value, data input standardization is realized; the opening degree and flow of the valve are collected in real time, a hydraulic impedance and spectrum matrix is constructed, and the dynamic working condition is accurately reflected; decomposing the spectrum matrix, extracting global and local oscillation mode features, and endowing differentiated weights for adjustment; a residual error is calculated based on the water demand and the flow, a tolerance threshold and projection verification are set, and subsequent operation is triggered only during real unbalance; the opening adjustment direction is determined according to spectral analysis and sensitivity evaluation, and blind movement is avoided; the optimal step length is analyzed and solved, amplitude limiting is carried out, and convergence speed and impact suppression are both considered; the valve opening is updated according to branches and issued in a closed loop mode, continuous online optimization and automatic control are achieved, and the real-time performance, the stability and the intelligent level of a pipe network are remarkably improved.

The invention belongs to the technical field of server detection, and discloses a server PCB automatic detection system based on operation data acquisition and analysis. The system is composed of an operation data distributed acquisition module, a preprocessing and synchronous time sequence correction module, a signal integrity real-time detection module, a thermal field dynamic distribution analysis module, a time sequence drift intelligent analysis module, a multi-dimensional joint feature extraction module and an abnormal mode adaptive identification module. A fault trend depth prediction module; and a self-learning feedback and online optimization module. Acquisition units carrying FPGA hardware are arranged at key nodes of a PCB of a server, distributed acquisition of current, voltage, temperature and other multi-source electrical data is achieved by means of the high-speed parallel acquisition characteristic of the acquisition units, after asynchronism and noise interference of the acquired data are eliminated through a preprocessing and synchronous time sequence correction module, all functional modules work cooperatively, and the multi-source electrical data are acquired. A whole-process online dynamic detection system from data acquisition to anomaly analysis is constructed, and anomaly can be quickly captured in a fault germination stage.

The invention provides a data quality rule automatic matching method, system and device for public security service data, and belongs to the technical field of computer information processing. The method comprises the following steps: structurally analyzing a public security data element standard document; constructing a basic rule and an association rule according to an analysis result; constructing a quality rule knowledge graph, creating four types of entity nodes including data element nodes, rule nodes, standard nodes and synonymous name nodes, and establishing three types of relation edges including a dependency relation, a synonymous name relation and a reference relation; extracting multi-modal features of the to-be-retrieved field, wherein the multi-modal features comprise semantic features and structured features; a rule is generated through a hierarchical matching engine, and matching processes of an accurate matching layer, a semantic matching layer and a statistical matching layer are executed in sequence; and executing online optimization. According to the method, the bottleneck problems of insufficient rule coverage, semantic understanding deficiency, poor dynamic adaptability and the like of a traditional method are solved, and the method has remarkable technical progress and practical value.