6345 results about "Whole systems" patented technology

The invention provides a computing system and method for GPU computing power scheduling, and the method comprises the following steps: 1, obtaining computing task data, and carrying out the information collection of each task through receiving a to-be-processed computing task queue; according to the invention, through task information collection and deep analysis, and GPU resource omnibearing monitoring and workload prediction, deep understanding of tasks and GPU resources is realized, highly accurate computing power distribution can be carried out, resource waste is avoided, and the resource utilization rate is improved; advanced technologies such as a knowledge graph, mixed integer programming and deep reinforcement learning are fused through the intelligent scheduling model, the fusion mode is innovative in the GPU computing power scheduling field, the limitation of singleness of a traditional scheduling model is overcome, the method can better adapt to a complex and changeable computing environment, the task execution efficiency is optimized, and the task scheduling efficiency is improved. The adaptability and competitiveness of the whole system in a complex environment are remarkably improved, and an excellent solution is provided for the GPU computing power scheduling problem in the field of high-performance computing.

The invention discloses a central air conditioner energy-saving optimization system based on load prediction, and relates to the technical field of air conditioner energy saving.According to the central air conditioner energy-saving optimization system based on load prediction, a VMDTransformerGRU hybrid deep learning model is introduced, through multi-source data fusion, the precision of cold load prediction is remarkably improved, and the prediction model can recognize the load change trend in advance. According to the improvement, the dynamic response capacity of the system is effectively enhanced, pre-regulation and control can be carried out before load changes, energy waste is reduced, a water chilling unit, a cooling tower and a pump are modeled to be independent intelligent agents, multi-agent deep reinforcement learning (MADRL) is adopted, and all the intelligent agents can autonomously learn and optimize control strategies of the intelligent agents. The intelligent agents cooperate with one another, efficient operation of the whole system is achieved through autonomous decision, and operation conflicts between devices are avoided. In this way, dynamic coordination and joint optimization among all the devices are achieved, invalid energy consumption is reduced, and the overall energy-saving effect and operation stability of the system are remarkably improved.

The invention provides an optical storage and charging cooperative control method and system based on multi-energy complementation. The method comprises the following sub-steps: respectively establishing a photovoltaic power generation model, an energy storage system model and a charging load model; acquiring historical illumination information, charging information and electricity price information, constructing a prediction model based on a neural network, and predicting and outputting illumination intensity, charging load demand power and electricity price in a future time period; constructing a target optimization function by taking the annual net cost and the power deviation rate as optimization targets; according to the method, the cooperative optimization control of the photovoltaic power generation, energy storage and charging system is realized, the annual net cost is reduced, the power deviation is reduced, the energy storage and charging cooperative control strategy is established, the target optimization function is solved by adopting the improved whale optimization algorithm, and the charging and discharging power sequence is generated according to the optimal solution obtained by solving and is input to the system for execution. And the operation efficiency and reliability of the whole system are improved.

The invention relates to the technical field of liquid cooling, and discloses a server cabinet liquid cooling control method and system based on intelligent liquid cooling distribution, and the method comprises the steps: collecting the temperature, cooling liquid flow, pressure and environmental parameters of each node in a server cabinet in real time through a liquid cooling distribution unit; the load change of the server is predicted based on a dynamic distribution algorithm, and the pump set rotating speed and the valve opening degree of the liquid cooling distribution unit are dynamically adjusted so as to dynamically distribute the cooling liquid flow according to needs; a multi-parameter coupling optimization algorithm is adopted, and the energy efficiency ratio of the liquid cooling system is adjusted according to the temperature, the cooling liquid flow and the pressure; cooling liquid leakage is detected through a multi-mode sensor, a liquid leakage protection mechanism is triggered in a grading mode according to the severity degree of leakage, and the liquid leakage protection mechanism comprises local pipeline isolation and whole system shutdown. The system corresponds to the method. By adopting the method, the energy efficiency improvement and the reliability enhancement of the heat dissipation system of the server cabinet are realized.

The invention discloses a distributed power supply storage and charging integrated system, and belongs to the technical field of power system management. The system comprises a clean energy access module, an energy storage control scheduling module, an intelligent charging control module, an energy management scheduling module, a communication network interconnection module and an equipment state monitoring module, the battery state is dynamically evaluated from multiple dimensions, and a matching strategy is called through a real-time environment sensing mechanism; the charging and discharging path and rate are more in line with the actual load demand and the energy supply and demand state of the system, the battery circulation loss is reduced, the energy utilization rate is maximized, the non-critical load reduction power of the user is dynamically guided, and the specific charging demand of the user in the future time period is dynamically predicted. Therefore, the system has the capability of allocating energy resources in advance, centralized coordination is carried out on the multiple charging piles, imbalance of the energy resources in spatial distribution is avoided, the scheduling flexibility of the whole system is improved, and the self-adaptive capability of the system to complex power consumption behaviors is enhanced.

The invention relates to a synchronous line loss intelligent diagnosis and analysis system based on an electric power knowledge graph. The synchronous line loss intelligent diagnosis and analysis system comprises a data acquisition layer, a data access layer, a data storage layer, a data analysis layer and a visualization layer, the data acquisition layer is used for acquiring power grid node data; the data access layer is used for carrying out dynamic synchronization on power grid node data based on Apache Kafka; the data storage layer comprises a time sequence database and a graph database; the data analysis layer comprises a detection module and an inference engine; and the visualization layer is used for dynamically displaying a propagation path and node characteristics of an abnormal region based on a topological structure of the knowledge graph, and querying an abnormal reason of the node and the influence on the whole system by clicking the node of the device. According to the invention, intelligent detection and diagnosis of the line loss abnormal area are realized, and the problem root cause is rapidly identified.

The present invention relates to the technical field of secondary device monitoring. Disclosed are an online monitoring method for an operating state of a secondary current loop neutral line, and a system. The method comprises: by using a high-precision sensor for collection and a composite filtering algorithm for processing, obtaining a secondary current fundamental component of each neutral line of a transformer substation which is not influenced by harmonic waves and transient states, and uploading the secondary current fundamental component to a protection information master station; retrieving originally configured access protection information master station information to obtain three-phase secondary current loop information of each bay of the transformer substation, summarizing secondary current information of all monitoring ranges, and performing grouping and classification marking on said information by transformer substation, bay and winding; and using the collection information for operation analysis, automatically identifying and confirming an operating state of the line, and on the basis of the operating state, calculating the outlier deviation linearity and the continuous dynamic trend deviation degree of each winding of each bay. The present invention solves the problem that intermittent faults in secondary current loop neutral lines are concealed and are not easily found, realizes comprehensive monitoring for the whole system, and can quickly and accurately position a specific winding and issue an abnormal early warning.

The invention discloses an electric power capital construction site operation environment data monitoring and safety management method, which belongs to the field of intelligent decision technology and electric power safety management, and comprises the following steps: constructing a semantic network framework according to a construction plan; collecting and calibrating multi-source environment data to generate a trusted data set; generating a real-time risk network graph based on the semantic framework and the trusted data set; calculating a robust risk index and performing sensitivity deconstruction; generating a closed-loop intervention instruction when the risk indicator exceeds a safety threshold; and finally, collecting, feeding back, iteratively optimizing the whole system, and generating a cross-project multiplexing intelligent template library. According to the method, a comprehensive technical path of semantic modeling, causal inference and closed-loop adaptive optimization is adopted, the operation situation can be deeply analyzed, potential risks can be quantified and attributed prospectively, the optimal intervention strategy is intelligently generated, and the intelligence, precision and prospective level of safety management of the electric power capital construction site is remarkably improved.

The invention relates to the technical field of resource scheduling, in particular to a cloud computing resource management system. In the cloud computing resource management process, the execution data of the computing tasks are analyzed, the task combinations with relevance are screened, and the computing path task set is established, so that the scheduling of the computing tasks is more intelligent, and the resource utilization rate is improved. And resource allocation is performed according to the matching degree of the calculation demand and the computing power pool, the adaptability of the calculation resources is dynamically adjusted, and accurate scheduling of the calculation task is realized. And in combination with the load state of the calculation node, the CPU utilization rate, the memory occupancy rate, the I / O load and other key indexes are monitored in real time, so that the resource allocation is more flexible, the calculation pressure of the high-load node is reduced, and the stability of the whole system is ensured. The load conditions of the computing power pools are evaluated, the task allocation strategy is adjusted, the computing tasks are reasonably distributed among the multiple computing power pools, the task migration and resource expansion strategy is optimized, and the balanced utilization rate of computing resources is improved.

According to the intelligent home environment intelligent regulation and control system and the control method thereof, various intelligent sensors are arranged, various environment data such as indoor temperature, humidity and illumination are monitored in real time, the data are preprocessed and then sent to a data analysis layer, data features are extracted according to the monitored data and historical data based on the deep learning technology, and user preferences are deduced; the control parameters are adjusted and sent to the equipment control layer, a specific instruction is sent to an indoor smart home, an air conditioner, a humidifier or a curtain and the like are controlled to accurately control the indoor environment, the living preference of a user is met, and in the process, the effect monitoring and dynamic adjusting layer monitors the adjusting condition in real time, and the user experience is improved. If the ideal effect is not achieved after adjustment, the equipment control layer is controlled to continue adjustment, in the process, the abnormal condition of the system is detected, the power consumption of the equipment and other data are monitored at the same time, normal work of the system is guaranteed, possible faults are predicted, a user is reminded in time, and safe and energy-saving operation of the whole system is guaranteed.

The invention discloses a repetition-frequency-adjustable high-energy nanosecond laser and a working method thereof, and belongs to the technical field of lasers. The laser comprises a synchronizing signal controller, a nanosecond seed source, an electro-optical selection system, a first end face pumping amplification stage, a second end face pumping amplification stage and a side face pumping bi-pass amplification stage, the synchronizing signal controller is adopted to generate a clock reference signal, a frequency division synchronizing signal and a clock synchronizing signal, and integrated control over system signals is achieved. The clock reference signal is used for triggering the seed source to generate nanosecond seed light pulses, the frequency division synchronizing signal is optimized and adjusted to control the electro-optical menu system to screen out frequency division seed light pulses, and the repetition frequency of the seed light pulses is adjustable. Meanwhile, a pulse pumping mode MOPA structure is adopted, the repetition frequency of each amplification stage pumping light pulse and the repetition frequency of the non-frequency-divided seed light pulse are kept consistent through a clock synchronization signal, it is ensured that the heat effect of the whole system is stable, and repetition frequency adjustable nanosecond laser output with high energy and good reliability is achieved.

The invention discloses a comprehensive algorithm for distribution and scheduling of AGV (Automatic Guided Vehicle) carrying tasks. S1: an SCADA system; equipment communication, data acquisition and storage, protocol analysis and logic processing; s11, the communication protocol, the address and the communication rhythm of the equipment are configured through the system, data acquisition data are stored in ES (ElasticSearch), the data acquisition data are stored for one year and backed up regularly, and the system achieves service logic processing through an LUA script. The carrying efficiency of the AGV is integrally improved, and the execution time of carrying a single task by the AGV is shortened by 10%; the production capacity of a workshop is improved, and the carrying rate exceeds 96% under the condition of no interference; the workshop logistics cost is reduced, and the overall number of AGVs is reduced by 10%; and the first pass yield of material carrying is improved by 15%. The deep fusion data analysis capability enables the whole system to predict demands more accurately, avoid conflicts, optimize the overall production efficiency, optimize the production process, reduce waiting and resource waste, and improve the flexibility of path planning and the reliability of task completion.

The invention provides an AI flood real-time simulation method for urban hydrological multi-physics field decoupling supervision, and the method comprises the steps: constructing an intelligent physical prediction model of urban flood, carrying out the layered decoupling of an urban hydrological hydrodynamic system, separating a one-dimensional drainage pipe network submodule from a two-dimensional surface flood module, designing a multi-scale physical constraint loss function for different links, and carrying out the real-time simulation of the AI flood. Microcosmic, mesoscopic and macroscopic multi-scale physical constraint fusion is realized, and residual constraints of the Saint-Venant equation are embedded in a neural network agent of a drainage system in a microcosmic level; in the mid-level, embedding the residual constraint of the shallow water equation in the surface flood routing system; in the macroscopic level, the water balance constraint of the whole system is realized; urban hydrology and hydrodynamic force are decoupled, and multi-scale physical field supervision constraint is achieved.

The invention relates to the technical field of battery management, in particular to a charging type mobile power station remote monitoring and analysis system which comprises a battery pack state monitoring module, a monitoring path adjusting module, a partition monitoring and energy dispatching module, an energy distribution module and a charging efficiency optimization monitoring module. According to the invention, through refined battery level monitoring and health state analysis, the real-time performance and accuracy of battery management are improved, the voltage, current and SOC parameters of the battery are monitored in real time, the health state of the battery is comprehensively evaluated, the monitoring path and the monitoring frequency are dynamically adjusted, resource allocation and efficiency maximization are ensured, the energy distribution difference between the battery packs is accurately identified, and the battery management efficiency is improved. By adjusting the charge and discharge flow, avoiding over-charge and over-discharge, improving the stability and service life of the battery and optimizing the charging current distribution and the charging plan, efficient charging is realized, the energy utilization efficiency is improved, the response speed and precision of the whole system are improved, a more intelligent and personalized solution is provided, and the requirements of different application scenes are met.

The invention discloses a load balancing control method based on data traffic, and relates to the technical field of data transmission. Comprising the following steps: constructing a flow monitoring module based on data flow, constructing a self-adaptive flow classification model, dynamically allocating weights to load nodes with balanced loads, dynamically adjusting a transmission path of the flow, carrying out real-time load balancing scheduling, and processing abnormal flow. According to the load balancing control method based on the data traffic, the self-adaptive traffic classification model and the multi-path traffic reconstruction algorithm are introduced, so that the defects in the prior art are fundamentally overcome. The real-time performance, the stability and the resource utilization rate of the system are remarkably improved through deep classification of the traffic characteristics and a multi-path dynamic segmentation strategy. According to the method, resource allocation can be optimized in a complex traffic scene, the load balancing efficiency is improved, meanwhile, the service quality of delay-sensitive and bandwidth-intensive services is guaranteed, and the availability and reliability of the whole system are enhanced.

The invention relates to the technical field of fire-fighting management, and discloses an intelligent fire-fighting service management system, which comprises a data acquisition unit used for acquiring three basic parameters, namely environment data, working condition data and battery data, of a fire-fighting object in an office building, and the system acquires the environment, working condition and battery data of a fire extinguisher through the data acquisition unit; the fusion unit generates a dynamic composite abnormal threshold value, and the adjustment unit executes a three-stage sampling frequency adjustment mechanism according to the threshold value, the battery health degree score and the data deviation degree, which means that the sampling frequency can be reduced to reduce the battery energy consumption when the state of the fire extinguisher is normal and the risk is relatively low, and when the risk is increased or the battery health degree is reduced, the battery energy consumption is reduced. The whole system realizes accurate management and regulation of the battery according to the actual working condition of the fire extinguisher, energy waste is avoided, the service life of the battery is prolonged, the cost of frequently replacing the battery is reduced, and the sustainability of intelligent fire control management is enhanced.

The invention relates to a seat cushion robot self-adaptive tightening system and an operation method. The system provided by the invention is composed of an integrated industrial personal computer, an AGV mobile platform, a collaborative robot, a tightening end effector, an industrial camera and accessories thereof. The collaborative robot is installed on the AGV mobile platform, the tightening end effector and the industrial camera are installed at the tail end of the AGV mobile platform, and the whole system is controlled by the integrated industrial personal computer. The accurate position of the center of the to-be-tightened bolt can be obtained through a visual identification method in combination with a designed multi-layer screening method; meanwhile, different feeding modes are set according to different pre-tightening conditions, automatic cap searching is achieved, and it is guaranteed that tightening is in place; in addition, the counter-acting force arm is added to counteract counter-acting force in the tightening process, and robot damage is avoided. The tightening system and the operation method provided by the invention not only have extremely high flexibility, but also can adapt to a complex tightening environment, so that the accuracy of tightening work is ensured, and the working efficiency is also improved.

The invention discloses an LNG storage tank BOG liquefaction recovery system and a control method thereof.The system comprises an LNG storage tank, an LNG buffer tank, an LNG pump and a refrigerating machine, the LNG pump is installed in the LNG buffer tank, when BOG in the LNG storage tank needs to be recovered, BOG conveyed by the LNG storage tank is cooled and liquefied through the refrigerating machine and then returns to the LNG storage tank, recovery of BOG gas in the LNG storage tank is achieved, and in the BOG recovery process, the BOG is recovered through the refrigerating machine. And when the supercooling degree of the LNG storage tank needs to be maintained, the LNG conveyed by the LNG storage tank is cooled through the refrigerating machine, the cooled LNG returns into the LNG storage tank, and after the cooled LNG is mixed with the LNG in the LNG storage tank, the temperature of the LNG in the LNG storage tank is reduced, so that the supercooling degree of the LNG storage tank is maintained. The whole system is simple in structure, BOG in the LNG storage tank can be liquefied and recycled, and the supercooling degree of the LNG storage tank can be maintained.

The present disclosure provides a system for generating motor control commands for a humanoid robot, comprising an alpha model with over 1 billion parameters that processes visual observations and language instructions at a first frequency to generate contextual embeddings, and a beta model operating at a higher second frequency. The beta model includes an embodiment-specific state encoder projecting robot state information into a shared embedding space, a diffusion transformer module generating denoised action sequences through iterative flow-matching that cross-attends to the alpha model's contextual embeddings, and an embodiment-specific action decoder converting denoised sequences into motor control commands. The beta model generates action chunks comprising future action sequences over a predetermined time horizon in a single inference step, with the complete system having less than 5 billion parameters.

The invention belongs to the technical field of vehicle-mounted human-computer interaction, and particularly relates to a vehicle-mounted music recommendation system based on context awareness, which covers the technologies of computer vision, recommendation systems, human-computer interaction and the like. The whole system is divided into two main parts: the first part is a context information collection module, and the module realizes real-time recognition of the emotion of a driver by collecting facial images and voice information of the driver; and the second part is a feedback module used for processing interaction between the driver and different emotion analysis results, including mapping rule reasoning and music recommendation. The system generates a personalized music recommendation list by analyzing the facial expression of the driver and the emotional characteristics extracted from the voice information, so as to reduce the music selection burden of the driver and prevent the occurrence of extreme emotions, thereby having important significance for guaranteeing the personal safety of the driver and the road traffic safety.

According to the intelligent grotto excavation stability analysis system based on the BIM, through deep fusion of the BIM model and multi-source monitoring data, whole-process dynamic control over the grotto excavation stability is achieved. The system adopts a lightweight model processing technology to remarkably improve the calculation efficiency, and an intelligent inverse algorithm effectively solves the subjectivity problem of traditional parameter determination. A three-dimensional numerical simulation and real-time monitoring data interactive verification mechanism greatly improves the reliability of an analysis result, and a dynamic risk assessment system realizes the accuracy of risk early warning through multi-index fusion analysis. A final support scheme optimization module combines engineering experience with an intelligent algorithm, safety is ensured, economical efficiency is taken into account, and an intelligent solution capable of being self-adaptive to engineering condition changes is formed. According to the whole system, seamless connection of all links is achieved through modular design, and full-chain technical support from data to decision making is provided for safety construction of underground engineering.

The invention discloses a method and equipment for predicting a tumor position under respiratory movement based on LSTM (Long Short Term Memory), and the method comprises the steps: collecting a 4DCT (4 Discrete Cosine Transform) image sequence of a tumor part of a plurality of patients under free respiration; based on the acquired 4DCT image sequence, extracting a movement track of a tumor centroid point position along with time change; sequentially inputting all tumor motion data points in each fixed time window on the motion trail into a CNN-Attention-LSTM model for offline training, wherein the CNN-Attention-LSTM model is used for predicting the tumor position at the next time; and in a radiotherapy stage, performing online real-time updating on the trained CNN-Attention-LSTM model by using the latest respiratory movement data of the current patient, and predicting the tumor position at the next time. The real-time position of the tumor is provided for the radiotherapy robot, the time delay of the whole system is compensated, and the tumor position tracking is accurately realized in real time.

A method for providing cyber security measures in a shared network estimates in a probabilistic model, a likelihood of a cybersecurity event occurring in a system. Based on the estimated likelihood selectively applies some but not all of the cyber security measures. Based constraints of system resources available for operations a combination discretionary security measures are selected for execution. During runtime of the system, security risk of an aspect of the system is periodically evaluated causing reconfiguration of the security measures for execution based on the estimated security risk and system resources available for operations. Timed automata corresponding to security threats are assigned a risk score associated with the security threat based on one or more states of the automata. An aggregation of multiple risk scores produces a trust score for the overall system. A number of security measures may be selected and allocated based on the trust score.

The invention discloses an FPSO stand pipe supporting structure stability online identification method and system, and belongs to the field of ocean engineering.The identification method comprises the following specific steps that firstly, the state of an FPSO stand pipe supporting structure is monitored in real time through a sensor system, and collected original sensor data are preprocessed; iI, training a stable recognition model by using historical sensor data, and globally optimizing the trained stable recognition model; the method can adapt to the rapid change of the environment in real time, improves the response capability of the whole system, effectively reduces the possibility of instability, remarkably reduces the probability of instability accidents, improves the safety of platform operation, achieves the dual optimization of safety and economy, and improves the reliability of the system. The comprehensive operation benefit of the system and the overall efficiency and accuracy of the stability online identification method are improved; the security risk of centralized storage of data can be avoided, the calculation efficiency of the system is improved, the network bandwidth pressure is reduced, the robustness and accuracy of the model are enhanced, and the fault-tolerant capability and stability of the system are improved.

The invention relates to the technical field of power grids, and discloses a port green power virtual energy storage aggregation regulation and control method, which comprises the following steps: acquiring an aggregation scene and resource data; generating a power hard constraint mapping and pile division execution reference by calculating a multi-band energy index and identifying a system power transfer characteristic; a local rapid control configuration set is generated by solving a virtual impedance feasible region and combining power hard constraint allocation compensation budget; and fusing a sub-pile execution reference (slow channel) generated based on the power hard constraint with a fast channel execution track obtained after local online execution compensation, performing priority arbitration under the constraint of the power hard constraint, and outputting a final execution power track. According to the invention, a double-layer regulation and control framework of slow channel planning and fast channel compensation is constructed, and through a core link of power hard constraint mapping, coordinated operation of fast and slow channels and safety and stability of the whole system are ensured, and fast and reliable dynamic support capability can be provided for a power grid.

The invention discloses an AI (artificial intelligence) digital human interaction system, which comprises a dialogue management module for receiving emotional state information transmitted by an emotional recognition module and other information input by a user feedback processing module; tracking a dialogue state, selecting a proper response mode according to a dialogue strategy, and generating a corresponding system behavior; the system behavior instruction is transmitted to an action and expression generation module; the user feedback processing module is used for collecting and analyzing feedback data of the user; and the feedback module analyzes and processes the feedback data, extracts valuable information, and transmits an analysis result to the dialogue management module and the reinforcement learning module for guiding the improvement and optimization of the system. Comprehensive information acquisition is realized through the multi-modal information acquisition module, continuity and individuation can be enhanced through the dialogue management module, an interaction strategy is optimized through the reinforcement learning module, and continuous improvement and real-time feedback are ensured through the user feedback processing module; and the interaction effect of the whole system is more natural and vivid.

The invention relates to a high-flow gas mixing system and method and semiconductor process equipment. The large-flow gas mixing system comprises a first gas input unit, a second gas input unit, at least one first flow control unit, at least one second flow control unit, a first gas mixing unit, a first concentration analysis unit, a second gas mixing unit, a second concentration analysis unit, a first gas output unit and a second gas output unit. The flow control device has the advantages that the first gas input unit is communicated with the plurality of first flow control units, the second gas input unit is communicated with the plurality of second flow control units, and the plurality of second flow control units are used for controlling the flow at the same time, so that the flow control precision is improved, and the concentration precision is further improved; the first gas mixing unit is respectively communicated with the first flow control unit and the second flow control unit, and the first gas and the second gas are primarily mixed through the first gas mixing unit, so that the concentration uniformity of the whole system is improved.

The invention discloses a simulation six-degree-of-freedom real sea condition random wave detection system and a simulation six-degree-of-freedom real sea condition random wave detection method. The simulation six-degree-of-freedom real sea condition random wave detection system comprises the following parts: a wave direction reproduction mechanism for simulating a real sea condition sea surface motion attitude, placing a wave buoy or a wave sensor and providing the motion attitude; the horizontal rotation driving mechanism is used for driving the wave direction reproduction mechanism to horizontally rotate; the laser ranging mechanism is used for calculating the wave height of the wave direction reproduction mechanism; the photoelectric period measuring mechanism is used for calculating the wave period of the wave direction reproduction mechanism; the wave direction angle measuring mechanism is used for measuring the wave direction of the wave direction reproduction mechanism; the bottom plate mechanism provides a detection platform for the whole system; the overall angle measuring mechanism is used for indicating the azimuth angle of the bottom plate mechanism; the land wheel mechanism horizontally moves the whole system and adjusts the bottom plate mechanism to be in a horizontal posture; and the control unit is used for controlling six-degree-of-freedom actions of the wave direction reproduction mechanism, respectively acquiring data of each mechanism and acquiring wave direction, wave height and wave period numerical values. According to the invention, all-parameter detection and calibration can be realized, and high-quality reproduction and magnitude transmission of wave direction parameters are realized.

The invention discloses a task scheduling method and system for a dense warehousing system, and the method comprises the steps: building an elevator task distribution model, a shuttle vehicle selection model and a warehouse-in and warehouse-out task time mathematical model according to the characteristics of warehousing operation, and preliminarily determining a task distribution scheme of an elevator through employing a heuristic rule on this basis, an improved A * algorithm is used for carrying out path planning on the shuttle vehicle, the time needed by the shuttle vehicle for completing a task is calculated, it is ensured that the elevator and the shuttle vehicle start to run, the scheme is further optimized through an improved PSO-SA algorithm, and the algorithm combines particle swarm optimization and a simulated annealing algorithm, so that the algorithm is optimized. A specific scheduling result of cooperative task execution of the elevator and the shuttle vehicle can be obtained, a scheduling scheme which enables the cooperative work effect of the whole system to be optimal is finally obtained, and maximization of the work efficiency is ensured.

The invention provides a photocatalysis and biological reaction mixed efficient air purification system, and relates to the technical field of biological air purification, the system comprises a photocatalysis reaction module, a biological reaction module, an intelligent control module, a sensor monitoring module, a data analysis and optimization module, and a modularization and self-maintenance module; the system is combined with a photocatalytic reaction module and a biological reaction module, a multi-stage degradation and efficient decomposition treatment mode is adopted, the photocatalytic reaction module firstly degrades pollutants in air into intermediate products, then the biological reaction module further degrades the intermediate products through microorganisms, and finally thorough purification is achieved. According to the multi-stage synergistic reaction, the air purification efficiency is remarkably improved, thorough removal of pollutants in the treatment process is ensured, the environmental risk is reduced, various indexes in the degradation process are dynamically adjusted through a multi-target optimization algorithm, it is ensured that each stage achieves the optimal effect, and the performance and stability of the whole system are further improved.