484 results about "Decision unit" patented technology

The present invention relates to a system and computer implemented method for cost-aware autoscaling of artificial intelligence workloads using predictive queueing models, designed to achieve proactive and economically optimized scaling of computational resources across cloud and edge environments. The invention introduces a predictive queueing-based technique that anticipates future workload congestion by modeling dynamic task arrivals and service times using a stochastic queueing process. A cost estimation unit computes the total projected operational cost of potential scaling actions by integrating real-time infrastructure pricing data, predicted delay penalties derived from service-level objectives, and estimated energy consumption. A scaling decision unit applies reinforcement learning-based optimization to select the scaling action that minimizes total cost while ensuring compliance with latency and throughput constraints. The system includes a hardware-integrated autoscaling controller device comprising a predictive computation processor, cost-decision processor, and scaling actuation interface configured for real-time execution of predictive and scaling operations.

The invention provides a Linux access control system based on attributes, and relates to the technical field of data access control. The system comprises a system monitor module, a data interaction module and a decision unit. The system monitor module collects attributes from a kernel and a user space and writes the attributes into the data interaction module; the access decision unit compiles the access control strategy into an eBPF program and mounts the eBPF program to a corresponding hook; executing the kernel to the hook, and triggering an eBPF program; an eBPF program queries a Flow rule; matching the attribute with the Flow rule, and if the matching is successful, executing a corresponding action; if all the Flow rules fail to match, executing a default action; the system can be expanded during operation, and can be loaded or unloaded based on dynamic loading characteristics and strategies of the eBPF program and the eBPF program during operation of the system, so that the problem that a kernel needs to be compiled in a traditional LSM scheme is solved; the method does not intrude the kernel, is completely based on an eBPF program, does not modify a kernel source code, and can guarantee the stability and compatibility.

The invention discloses an autonomous decision-making system of a multi-level intelligent agent in an urban area and an operation method thereof. Each end-side decision-making unit broadcasts an equipment state variable to a side-side decision-making unit; the side decision-making unit determines a global reference state and generates a control instruction of each end decision-making unit; after the equipment executes the control instruction, each end-side decision-making unit updates an equipment state variable according to the real-time operation data of the equipment, and when an abnormal event is judged to occur, a side decision-making unit predicts a decision-making variable of each piece of equipment based on a collaborative optimization model of an equipment target and an urban area target; the cloud regulation and control platform predicts a global consistency variable based on the collaborative optimization model of all the side decision units; predicting a regulation and control instruction of each side decision-making unit according to a global dynamic optimization target under a system operation constraint; and the side decision-making unit decomposes the regulation and control instruction into the control instruction of each end decision-making unit, so that the problems that the global cooperative capability of the urban regional integrated energy system is weak and cooperative scheduling is not timely under extreme events are solved.

The invention relates to the technical field of power system automation, and particularly discloses a cross-domain heterogeneous computing power real-time calling and unified scheduling system for the power industry, which comprises a computing power resource global sensing unit, a business demand dynamic modeling unit, a cross-domain unified scheduling decision unit and a task execution and feedback control unit, according to the method, heterogeneous computing power states are collected in real time, task requirements are dynamically modeled, a scheduling scheme is generated based on multi-objective optimization, and task execution monitoring and load balancing are realized by means of closed-loop feedback, so that the utilization efficiency of global computing power resources is improved, and the real-time performance and reliability of power business are guaranteed.

The invention relates to the technical field of bridge engineering structure health monitoring, in particular to a service life prediction method and system for a bridge connection part, and the method comprises the steps: collecting the strain of a grouting sleeve, a reinforcement-grouting interface ultrasonic reflection signal and environment temperature and humidity data through a distributed sensor array; a three-dimensional dynamic void rate model is generated through fusion of a spatial topological mapping algorithm, a correlation function of load circulation and void expansion is established, the dynamic void rate is input into a bond strength coupling degradation model, after strength attenuation is quantized in combination with steel bar corrosion data and bond failure early warning is triggered, stress redistribution is calculated through multi-physics coupling simulation, and the dynamic void rate is obtained. Early warning is taken as a starting node, the actual load spectrum and temperature and humidity data are combined, Monte Carlo sampling is adopted to simulate rigidity degradation and output the residual life probability, the system comprises a multi-source collaborative sensing unit, a micro-void evolution analysis unit and a life prediction decision unit, and the safety control precision of bridge connection parts is improved.

The invention discloses a rice growth period distinguishing method and an intelligent irrigation control device. Belongs to the technical field of crossing of intelligent agriculture and irrigation and water conservancy engineering and particularly relates to the technical field of rice growth period judgment and intelligent irrigation. The technical problem that an accurate and effective rice growth period judgment method for guiding intelligent rice irrigation is lacked at present is solved. The method specifically comprises the following steps: calculating a rice growth process index through an improved accumulated temperature model; and constructing a multi-factor coupling growth period judgment method, and judging the current rice growth period through the rice growth process index. The system comprises a sensing unit, a judgment and decision-making unit, an electric execution unit and an input and power supply unit, the judgment and decision-making unit receives the environment data of the rice field and the water layer depth data of the rice field, judges the current growth period of the rice according to a rice growth period judgment method, calculates the current water layer deviation and generates a control instruction to the electric execution unit.

The invention discloses a real-time emergency prevention and control system and method for an electrochemical energy storage system based on digital twinning, and belongs to the field of energy storage safety. The system comprises a multi-source sensing unit, a digital twin deduction unit and a visual emergency decision unit. The sensing unit collects multi-source data to form a state vector; the deduction unit outputs a future thermal runaway evolution path containing a temperature field, a gas concentration field and a fire blast risk probability and uncertainty measurement thereof in real time through a pre-trained Gaussian process regression agent model; and the decision-making unit performs three-dimensional visual rendering, jointly determines a high-confidence risk area based on the risk level and the uncertainty level, and generates an emergency prevention and control instruction with accurate spatial positioning. The problems that in the prior art, disaster evolution paths cannot be predicted in real time, and prevention and control measures are extensive are solved, millisecond-level online deduction and accurate active prevention and control of the thermal runaway process are achieved, and the safety of an energy storage system is remarkably improved.

The invention relates to the technical field of new energy power distribution planning, in particular to a new energy power distribution planning intelligent simulation system oriented to a novel power system. Comprising a multi-source data dynamic modeling unit; the multi-objective optimization decision-making unit is used for realizing collaborative optimization of economy and reliability of the power distribution network and generating a planning scheme of equipment configuration, energy storage layout and network reconstruction by improving a heuristic algorithm; and the uncertainty analysis unit is used for quantifying the influence of the wind and light output fluctuation on the planning scheme and outputting a risk assessment index through a scene generation technology and a probability sampling method. Through multi-modal data fusion, time sequence model construction and a self-adaptive updating mechanism of the multi-source data dynamic modeling unit, the dynamic adaptation capability of the model to new energy power fluctuation and load characteristic change can be improved, and meanwhile, the space-time correlation characteristics between the source and the load are mined, so that the power grid model can be synchronously evolved along with the time sequence data change; and the deviation between the model and the actual operation condition is reduced.

The invention relates to the technical field of water conservancy projects, in particular to an anti-seepage control system for an earth rock cofferdam. The method comprises the following steps: periodically acquiring external hydro meteorological data and internal structure response data of a cofferdam through a sensor group based on a monitoring unit; establishing a multi-field coupling model based on a finite element method through a model building unit, and dynamically coupling a wave load, a tidal water level and an osmotic pressure field; the minimum cofferdam crest elevation is determined by comparing the difference between the minimum safety coefficient and a preset safety coefficient on the basis of a decision-making unit; and based on the analysis unit, inputting the minimum cofferdam crest elevation into the multi-field coupling model to obtain the maximum value of the hydraulic gradient of the escape point of the downstream slope within a third preset time period, determining an anti-seepage state based on the maximum value, and adjusting related parameters based on the anti-seepage state. The seepage risk of the cofferdam is monitored, predicted and actively controlled in real time, so that the seepage rate of the earth rock cofferdam is reduced.

The invention belongs to the technical field of electric overhead working trucks, and provides a narrow-body self-walking working platform with a hybrid power system, which comprises a walking platform, a working device and a control system. The walking platform adopts a narrow-body four-wheel-drive chassis and is provided with a hybrid power system of a fuel engine and an electric pump, and the control system integrates an environment sensing unit, a stability calculation unit, a track prediction unit, an early warning unit and a control decision unit. The environment sensing unit constructs a high-precision three-dimensional environment map; the stability calculation unit calculates the overall gravity center position and the dynamic stability boundary of the platform in real time; the track prediction unit establishes a dynamic hydraulic system response model; and the early warning unit adopts an artificial potential field method to calculate a safety avoidance path. And the control decision unit automatically adjusts the control instruction according to the risk type and degree. According to the invention, the organic combination of environment perception, dynamic stable control, prediction trajectory planning and electrified body safety protection is realized, and the operation safety and efficiency of the aerial work platform are obviously improved.

The invention relates to the technical field of sensing data processing, and discloses an old people home safety decision service system fusing multi-mode sensing data, which comprises a data interaction analysis unit, a data change analysis unit, a data calculation unit, a safety analysis unit and a safety decision unit. The hidden association of the multi-modal indexes is accurately captured, the early degradation of the collaboration level can be perceived through the graph condensation degree and the graph equilibrium value, the collaboration degradation track can be used for analyzing the collaboration state, and the finally generated safety decision instruction can perform early warning in advance. According to the scheme, single-mode threshold dependence is broken through, hidden dangers hidden in data correlation evolution are recognized in advance, and the lagging of home safety decision of old people is solved.

The invention discloses coal mine screening equipment for coal mining and a use method, and the equipment comprises a multi-source sensing module which is used for obtaining the original data of various physical characteristics of raw coal in real time; the data fusion and feature extraction module is used for extracting a fusion feature vector used for representing the key difference between the coal and the gangue; the decision-making unit is used for generating a sorting control instruction aiming at the current batch of raw coal in real time; the execution module is used for carrying out coal and gangue separation on the coal flow; the feedback module is used for feeding back clean coal product quality indexes and tailing data detected in real time to the decision-making unit; multi-dimensional information such as mineral components, moisture and lumpiness is fused through the multi-source sensing module, comprehensive analysis is carried out through the data fusion and intelligent recognition model, the limitation of a single sensing mode can be overcome, the recognition precision and reliability of coal and gangue are improved, efficient sorting is achieved from the source, and the recognition efficiency of coal and gangue is improved. The effects of improving the purity of a clean coal product and reducing the coal carrying rate of the gangue are directly achieved.

The invention provides an integrated hospital intelligent monitoring system for cold-chain medicine temperature early warning, and relates to the technical field of medical monitoring. The integrated hospital intelligent monitoring system for cold-chain medicine temperature early warning comprises a sensing layer module which is deployed in a dual-mode temperature sensing unit in cold-chain equipment and comprises a main sensor and an auxiliary sensor, the main sensor is arranged in a central area in the cold-chain equipment, and the transmission layer module is arranged in a central area in the cold-chain equipment. The transmission layer module comprises a hybrid networking unit, the hybrid networking unit integrates a 433MHz wireless ad hoc network subunit and an NB-IoT / 4G remote transmission subunit, the platform layer module comprises a data processing unit and an intelligent early warning decision unit, and the execution layer module. When the box door is opened, the system can accurately identify non-fault temperature fluctuation by analyzing the temperature difference of the double sensors, the false alarm rate is reduced to 5.2% from the average industrial 38.7%, and the interference of invalid alarm to medical staff is greatly reduced.

The invention relates to the technical field of building construction intellectualization, in particular to a building construction intelligent monitoring system based on multi-source sensor data fusion. Comprising a data acquisition and storage unit; a data fusion processing unit; the construction state analysis unit uses an improved anomaly detection algorithm; a risk assessment early warning unit; and a visualization and decision-making unit. Through the dynamic adaptation module, the structural displacement feature weight is adjusted based on the construction stage parameters to preferentially extract the construction key stage structural features, and the structural material elastic deformation limit is corrected in combination with the material linear expansion coefficient and the temperature difference value; meanwhile, the dynamic critical generation module generates a risk critical judgment basis changing along with the construction process in combination with structural bearing capacity parameters and equipment mechanical stress limits in the construction stage, so that the problem that abnormal detection and risk judgment are lack of dynamic adaptability is solved; and the accuracy of personnel border crossing, equipment abnormity and structure abnormity identification and the adaptability of risk assessment are effectively improved.

The invention relates to the technical field of transformer voltage regulation cooperative control, in particular to a transformer voltage regulation cooperative control system based on Internet of Things data acquisition, and the system comprises a heterogeneous control strategy execution unit which is used for generating an expected control instruction signal; the power grid regulation toughness quantification unit is used for collecting real-time control instruction data and calculating to obtain an accumulated toughness attenuation index; the data attack identification unit is used for synchronously acquiring an original voltage measurement signal and an expected control instruction signal generated by the heterogeneous control strategy execution unit; obtaining an attack correlation index; the control right switching decision unit is used for calculating to obtain a switching trigger value; generating a final control output; the heterogeneous control strategy execution unit is also used for responding to the final control output and performing switching execution between a deep reinforcement learning strategy and a classic robust control strategy; according to the invention, accurate evaluation of physical consequences caused by attack behaviors is realized, and equipment failure caused by overfatigue is avoided.

The present application relates to the technical field of intelligent war game, in particular to an intelligent war game simulation system based on digital twin fusion large model, which comprises a digital twin modeling unit, a large model decision unit, a deduction execution unit and a dynamic interaction unit. The digital twin modeling unit adopts a dynamic precision adaptive modeling mechanism, which can realize dynamic switching of model precision on demand, balance the demand for detailed simulation and the consumption of computing resources, and realize sub-second dynamic synchronization of virtual models and war game scenes through a timing calibration algorithm. Through the multi-modal feature analysis module, spatial adversarial, timing decision and rule trigger features are fused, and the entity performance boundary and scene rule constraint are embedded in the war game rule verification module, which can generate compliant adversarial decision parameters in line with the logic of war game, solving the problem of insufficient model precision adaptation and decision constraint.

The invention relates to the technical field of vehicles, in particular to a collaborative decision-making system and method under mixed traffic flow, a vehicle and a storage medium, and the method comprises the steps: obtaining the traffic environment information under the mixed traffic flow in a target range through a roadside sensing unit, and calculating the behavior intention probability of a manual driving vehicle through a preset intention prediction model; a vehicle end decision-making unit is utilized to construct, adjust and expand a perception domain based on traffic environment information and behavior intention probability in combination with vehicle-mounted sensor data, generate a risk constraint trajectory and a collaborative decision of the vehicle, and re-plan a driving path of the vehicle; and the cloud optimization unit is used for updating a preset intention prediction model, receiving traffic environment information, reconstructing an area-level traffic situation under mixed traffic flow, and transmitting optimized signal timing and formation parameters to the road side sensing unit and the vehicle end decision-making unit. Therefore, the problems of single adaptability of a traffic collaborative decision-making scene, no fusion of real-time road side data and the like in related technologies are solved.

The invention discloses an electric ship remote intelligent control method and system based on cloud-side cooperation, and relates to the technical field of cloud-side cooperation computing, and the method comprises the steps: carrying out the feature extraction of images around a ship and time-space aligned multi-modal data, obtaining visual features, motion features and environment features, carrying out the feature fusion, and generating an environment situation vector; inputting the environment situation vector into a channel boundary detection model, analyzing the emergency degree of the current situation, generating a collision avoidance instruction, and generating a ship control decision through a three-level collaborative decision; combining a ship control decision with ship control characteristics, simulating a traffic development situation in a navigation area, comparing with historical ship navigation conflict data, and identifying potential conflict data; through collaborative fusion of the ship-end decision-making unit, the shore-end decision-making unit and the cloud-end decision-making unit, an optimal control strategy is generated in combination with respective advantages, intelligent decision-making of global optimization in a complex environment is realized, and adaptability and safety of ship control are enhanced.

The invention discloses a non-inductive cooperative regulation and control method and device for flexible loads in a building and a medium. The method relates to the field of power control, and solves the problem that a high-precision and quick-response flexible load regulation and control scheme is lacked. According to the method, the problem of space-time correlation adaptation of the equipment is solved by collecting the running state, environment and personnel distribution multi-source heterogeneous real-time data of the equipment, constructing the topological dynamic graph of the equipment, outputting nonlinear space-time correlation information and capturing the dynamic thermal-mechanical coupling relation between the equipment in real time. A personalized regulation and control instruction is generated by an independent decision-making unit to replace a fixed threshold value; coupling disturbance is predicted in combination with associated information, initial PID parameters are compensated, it is ensured that the temperature adjusting amplitude is converged to a user perception threshold value, and frequent starting and stopping of equipment are avoided. The independent decision-making unit and PID compensation are both based on local data operation, and the instruction response period is shortened; differentiated control instructions are converted according to equipment sensitivity, a high-sensitivity area is finely regulated and controlled to ensure comfort, and a low-sensitivity area preferentially responds to power grid peak clipping.

The invention relates to the technical field of intelligent management of wharf accessories, in particular to a wharf accessory scheduling management method and system fusing the Internet of Things and image big data analysis. Comprising a multi-agent sensing unit; an image big data acquisition and analysis unit; the multi-agent reinforcement learning scheduling decision-making unit is used for realizing dynamic scheduling decision-making and defining the accessories, the crane and the automatic guided vehicle as collaborative operation agents; and executing a feedback and model iteration unit. According to the method, the dynamic weight coefficient is output through the LSTM network, a state interaction mechanism between agents is constructed in combination with GNN, a scheduling scheme adaptive to a dynamic environment is generated, ship berthing time and weather parameter changes are monitored in real time, scheme recalculation is triggered when a threshold value is exceeded, the problem of decision logic staticization is solved, the no-load rate of accessories and operation conflicts are reduced, and the working efficiency is improved. And the adaptability of scheduling to complex scenes is improved.

The invention relates to the technical field of industrial Internet of Things and industrial automation monitoring, in particular to an industrial Internet of Things temperature transmitter data low-power-consumption transmission system which comprises a local trend feature extraction unit used for maintaining a sliding time window and solving the change rate and the change acceleration of the current physical process; generating a current model parameter; the virtual shadow prediction unit is used for obtaining a virtual prediction value; the dynamic confidence interval judgment unit is used for generating a dynamic confidence threshold value; comparing the deviation with a dynamic confidence threshold; when the deviation is greater than the dynamic confidence threshold, generating a model failure trigger signal; the model parameterization resynchronization unit is used for constructing the current model parameters into data frames; sending a data frame through a wireless module; writing the current model parameter back to the virtual shadow prediction unit to update the sent parameter; according to the method, the problem that details of a key process are lost due to discretization in a traditional dead zone scheme is effectively solved, and the analysis value of industrial monitoring data is improved.

The invention discloses a motor state monitoring method, system and equipment based on multi-sensor data fusion, and relates to the technical field of industrial monitoring, and the method comprises the steps: deploying a flexible strain-temperature composite sensor array at a key position of a motor housing, carrying out the real-time collection to obtain a multi-source fusion signal, carrying out the wavelet packet transformation, and carrying out the real-time collection of the multi-source fusion signal; extracting non-stationary fault fingerprints, calculating gear wear topology invariants, performing incremental parameter exchange with a cloud knowledge base, dynamically generating a health degree confidence ellipse in combination with real-time working conditions, and if the health degree exceeds a threshold value, triggering a brain-like decision-making unit to perform simulation fault-tolerant control. The technical problems that an existing motor state monitoring method is single in sensing data, a feature extraction shallow layer and a decision-making mechanism are solidified, the diagnosis sensitivity of early-stage composite faults is insufficient, and fault-tolerant control lags are solved, and the purposes that multi-physics field games are fused with deep fault fingerprints and cloud incremental evolution are achieved. And the technical effects of prospective identification and real-time fault-tolerant control of potential faults are realized.

The invention discloses an environment monitoring management system and method for a precision workshop, and relates to the technical field of environment monitoring and control, and the method comprises the steps: synchronously collecting multi-dimensional environment parameters through a distributed sensor network, accessing a manufacturing execution system in real time, analyzing the requirements of a process window, and carrying out the real-time monitoring of the environment parameters; and performing fusion and abnormality diagnosis on the data under a unified space-time reference, generating a closed-loop regulation and control instruction, and coordinating and controlling an execution mechanism. The system comprises a multi-mode environment perception unit, a process semantic analysis unit, a space-time fusion and abnormality diagnosis engine, an intelligent regulation and control decision unit and an execution mechanism cooperative control unit. According to the invention, deep understanding of environment disturbance and production process relevance can be realized, compound environment abnormity can be accurately identified, production task requirements can be adapted in real time, environment parameters are ensured to be always in an optimal process tolerance band, the product yield and the process repeatability are obviously improved, and millisecond-level response and cooperative linkage control are realized.

The invention relates to the technical field of communication network maintenance, and discloses a communication network operation quality monitoring and closed-loop operation and maintenance scheduling management system, which comprises an acquisition unit for acquiring network element telemetry data; the observation module extracts a flow fingerprint, converts a link into a transfer function model, and generates a consistency gradient vector according to singular point offset; the echo prediction module converts the scheduling instruction into an expected gradient offset vector; the module calculates the scheduling residual error of the actually measured offset and the expected offset; the decision-making unit generates a path reconstruction instruction, and the scheduling residual error is utilized to check the execution efficiency and update the operation reference; according to the invention, the flow conduction consistency constraint is anchored, the link degradation characteristic and random fluctuation decoupling is realized, and the transient interference generated by the scheduling action is eliminated.

The invention relates to the technical field of adaptive control, in particular to a syndrome differentiation adaptive control system for intelligent traditional Chinese medicine treatment equipment, which comprises an environmental parameter acquisition unit for acquiring microenvironment parameters in real time and generating environmental compensation parameters; the moxibustion sense feedback input unit quantifies subjective moxibustion sense feedback of the patient; the syndrome differentiation and trend prediction unit is used for outputting dynamic syndrome type probability distribution and a syndrome evolution prediction sequence based on the traditional Chinese medicine knowledge graph; and the self-adaptive control decision unit is used for generating a final self-adaptive control instruction when the emergency safety condition is not triggered by integrating the environment compensation parameters, the moxibustion sense feedback, the syndrome type probability and the evolution prediction result. According to the system, three types of data sources are fused, a four-stage serial correction architecture is adopted, and multi-stage regulation and control are automatically completed in a treatment cycle, so that equipment is not out of alignment due to environmental fluctuation, downshift is performed no longer after scalding, and prescription change is performed no longer after symptom transformation, and therefore, temperature control precision improvement and manual intervention return-to-zero can be realized.

The invention discloses an intelligent analysis and self-adaptive regulation and control system for a glass fiber wiredrawing forming process, which relates to the technical field of high silica glass fiber product processing and comprises a multi-modal data acquisition unit, a process prediction model unit, a reference process model unit, a self-adaptive regulation and control decision unit and a control execution unit. By introducing the prediction model and the self-adaptive decision-making unit, two strong coupling parameters of speed and temperature can be decoupled, collaborative optimization regulation and control are carried out, precise control over the wire drawing process is achieved, the uniformity of the fiber diameter is remarkably improved, the future process trend can be predicted on the basis of real-time data, and the method has good application prospects. Traditional feedback control is improved into predictive feedforward control, fine adjustment is carried out before process parameters are obviously deviated, the rate of finished products is greatly improved, meanwhile, the optimal production process is solidified into a reference model, complex regulation and control logic is precipitated into a decision algorithm, and dependence on artificial experience is eliminated.

The invention discloses a universal emergency panoramic command and intelligent decision-making system, which relates to the technical field of emergency command and comprises a space-air-ground cooperative sensing network unit, a multi-source data fusion and intelligent analysis unit and a dynamic sand table and cooperative decision-making unit. According to the invention, by constructing a space-air-ground cooperative sensing network, panoramic sensing and real-time environment modeling of centimeter-level precision of a disaster site are realized, based on multi-source data fusion and intelligent analysis, the system can quickly identify a disaster target and generate an interactive semantic map, and through a three-dimensional intelligent sand table and a cooperative decision-making mechanism, the system can quickly identify the disaster target and generate an interactive semantic map. Multi-terminal situation synchronization and intelligent plan generation are realized, so that the emergency response speed, precision and cooperation efficiency are improved.

The invention relates to the technical field of coal mine safety production monitoring and intelligent early warning, and discloses a coal mine hidden danger recognition and intelligent early warning system based on multi-modal feature fusion, which comprises a multi-modal sensing data acquisition module, an edge calculation preprocessing module, a central analysis server and a linkage alarm execution module, the central analysis server integrates a three-dimensional flow field construction unit, a feature extraction unit, a cross-modal fusion unit and a hidden danger decision-making unit. The method comprises the following steps: constructing a three-dimensional limited flow field by using monocular depth estimation and an optical flow field, and inverting gas concentration into a virtual gas concentration distribution diagram; performing cross-modal depth correlation on visible light, infrared, audio and gas distribution characteristics by using a Transform network; and the hidden danger decision-making unit identifies categories based on the fused features and generates a visual heat map. According to the method, visual reconstruction of invisible gas and deep complementation of heterogeneous data are realized, the problem of high false alarm rate of single-mode monitoring is solved, and the accuracy and response speed of coal mine hidden danger early warning are improved.

The invention relates to the technical field of database query optimization and distributed system data processing, and discloses an intelligent routing optimization method and system for multi-table association query, and the method comprises the steps: a query topology analysis unit analyzes an object relation mapping configuration file to construct a query topological graph; the environment parameter detection unit collects real-time state data of the server cluster based on the query topological graph, wherein the real-time state data comprises a pre-estimated cache hit rate, associated sparseness and a target table data line number estimation value; the routing strategy decision-making unit compares the real-time state data with a preset threshold value, and selects a cache-driven step-by-step query strategy, a column-type compression transmission strategy or an asynchronous intermediate table unloading strategy; and the dynamic execution control unit executes data acquisition and assembly operation according to a selected strategy. According to the method, the problems of low database computing resource utilization rate and application server memory overflow caused by the adoption of a static strategy in a traditional framework are solved, and the system response speed and the service availability of multi-table association query are effectively improved.

The invention relates to the technical field of hydroelectric generating set intelligent optimization control, in particular to a hydroelectric generating set optimization system and method based on the reinforcement learning technology, and the system comprises a multi-source sensing fusion unit, a reinforcement learning decision unit and an instruction execution unit. A unified time sequence feature tensor is generated through dynamic time warping time sequence alignment, Kalman filtering noise reduction, gradient normalization and feature cascade fusion, a reinforcement learning decision unit extracts three types of features by using a multi-scale time convolution network, and the coupling strength is quantified through a multi-head attention mechanism. The double branches respectively generate a start-stop sequence carrying start-stop loss punishment and a load distribution proportion of a coupling flow power function, a triple optimization mechanism and staged course learning are combined, an optimal matching scheme is output, an instruction execution unit converts the scheme into a control instruction, and the control instruction is output to a unit PLC control system after verification and compliance.