735results about How to "Reduce consumption cost" patented technology

The invention discloses an AI model adaptive tuning method based on deep reinforcement learning, and belongs to the technical field of industrial process control, the method comprises the following steps: firstly, constructing a deep reinforcement learning environment comprising a state space, an action space and a reward function, the state space comprising a process variable measurement value, a controller parameter and a performance index; the action space is a parameter adjustment quantity vector, and the reward function comprehensively considers the stability, rapidity and accuracy of the system; then, based on a deep Q learning network of a dual-network structure, training data are stored and sampled by adopting an experience playback mechanism, and network parameters are iteratively optimized through a deep Q learning algorithm; and finally, realizing online adaptive adjustment of controller parameters by using the optimized deep Q network. The method has the advantages of being high in adaptability, good in optimization performance, high in practicability and the like, intelligent optimization control over an industrial process control system can be achieved, and the overall performance of the system is improved.

The invention relates to the technical field of chemical engineering and machine learning, and provides a method for predicting the solubility of carbon dioxide in a deep-eutectic solvent based on a machine learning model, and the method comprises the steps: collecting CO2 solubility data, carrying out the coding and feature descriptor conversion of a deep-eutectic solvent molecular structure through an open-source chemoinformatics software package RDKit, and constructing a data set. Four algorithms of XGBoost, random forest, a deep neural network and a convolutional neural network are adopted to construct a model, and hyper-parameters are optimized through grid search and k-fold cross validation. After the model is trained, the prediction performance is evaluated, and the optimal model is selected for CO2 solubility prediction. The method improves prediction efficiency and accuracy, has strong adaptability and generalization ability, and provides an effective tool for formula optimization of the eutectic solvent and improvement of CO2 capture efficiency.

The invention provides a path planning method and system for an automatic driving trolley, and relates to the technical field of path planning, and the method comprises the steps: obtaining the data of an acoustic sensor of an underwater environment, and obtaining the contour of the underwater terrain and the position information of an obstacle; carrying out dynamic environment modeling processing; the Euclidean distance is used as a heuristic function to calculate the dynamic model of the underwater environment, and a second optimized path conforming to kinematics and dynamics constraints of the automatic driving trolley is obtained; carrying out smoothing processing on the second optimization path through a dynamic motion primitive technology; constructing a three-dimensional map for path planning and obstacle avoidance; predicting an action track of the robot in a period of time in the future; and through real-time adjustment processing of the self-adaptive control system, path planning of the automatic driving trolley is completed. According to the method, the dynamic characteristics of the underwater environment and the motion constraint of the automatic driving trolley can be comprehensively considered, the three-dimensional map containing the obstacle and topographic information can be constructed in real time, and the future motion trail of the obstacle can be predicted.

The invention discloses an optimization method for a new energy ship energy management system, and the method comprises the steps: S1, obtaining a priority assumption condition related to the energy use of a new energy ship according to a pre-created new energy ship system; s2, constructing a ship energy management mathematical model about a new energy ship system based on priority hypothesis conditions; s3, according to the constructed ship energy management mathematical model, obtaining an objective function model and optimization constraint conditions of ship energy for quantitatively evaluating advantages and disadvantages of different energy use schemes; s4, ship operation energy consumption and historical navigation data are collected and obtained; s5, according to the historical data, obtaining a neural network prediction model for pre-estimating the photovoltaic generating capacity and the ship navigation power demand based on weather prediction data; and S6, according to the neural network prediction model, obtaining estimated data of the photovoltaic power generation amount and the ship navigation power demand, and based on the target function model of the ship energy and the optimization constraint condition, obtaining an optimal energy use scheme of a corresponding new energy ship energy management system according to an optimization algorithm. The problems that an existing ship energy management system and a calculation method thereof have remarkable defects in the aspects of energy efficiency optimization, multi-energy coordination, dynamic response, operation simplicity and convenience, intelligent management, safety, new energy compatibility, comprehensive optimization strategies and data processing capacity, and complex requirements and environmental protection requirements of modern shipping are difficult to meet are solved.

The invention relates to the technical field of battery control, in particular to a new energy automobile battery device and a control method thereof. The method comprises the following steps that dynamic parameter monitoring is conducted on an automobile battery, a battery state parameter set is obtained, and the battery state parameter set comprises a residual electric quantity continuous parameter, a voltage continuous parameter, a current continuous parameter and a temperature continuous parameter; according to the battery state parameter set, working condition identification is carried out on the automobile battery, a battery working condition type is obtained, and the battery working condition type is any one of a discharging working condition, a charging working condition and a standing working condition; if the battery working condition type is a discharge working condition, performing driving behavior data acquisition on the target automobile to obtain a driving behavior data set; and performing driving behavior pattern recognition on the target automobile based on the driving behavior data set to obtain a driving pattern type. The use efficiency and the safety of the battery are improved.

The invention relates to the technical field of freight scheduling, in particular to a freight scheduling information management method and system and a storage medium. The method comprises the following steps that uncompleted order collection is carried out on a freight platform to obtain an uncompleted order set, and the uncompleted order set comprises a plurality of uncompleted orders; each uncompleted order at least comprises an order ID, order release time, target cargo attribute data, publisher identity data, a departure place, a destination, an order state identifier, an order transportation task schedule, cargo stacking attributes, predicted delivery time and cargo expected arrival time; and performing order state classification and marking on the uncompleted order set to obtain a classified order set. By optimizing the loading space utilization rate of the vehicle, the unloaded and half-loaded conditions of the vehicle can be reduced, so that the fuel consumption and the transportation cost are reduced.

The invention relates to the technical field of air conditioner control, and discloses an air conditioner load data control method and system.The method comprises the steps that firstly, meteorological parameters and load parameters of an air conditioner are collected, and data cleaning, timestamp definition and standardization processing are conducted; then, defining a feature vector according to the processed data, generating a feature data set, and constructing a load prediction model by using a neural network algorithm; and pre-training the model by using the source domain data set through a transfer learning strategy, and enhancing the target domain data to generate a new sample. Next, parameters of the pre-training model are migrated to the new task model, and partial network layer parameters are frozen to form an initialized reinforcement learning model; and finally, training unfrozen layer parameters by using the target domain data set, and finely adjusting the model to realize real-time and accurate optimal setting of the air conditioner temperature, thereby achieving the purpose of load optimization control. Through combination of transfer learning and reinforcement learning, the generalization ability, robustness and adaptability of the model are effectively improved.

The invention relates to the technical field of parking lot operation, and discloses an optimal scheduling method for an intelligent parking lot of an intelligent community, the core of the method is to construct an intelligent parking decision model, the model models the scheduling process of the parking lot into a Markov decision process, and intelligent scheduling is realized by defining a state space, an action space, rewards and strategies. The reward function design comprehensively considers the parking space utilization rate, the vehicle access speed, the user satisfaction degree and the operation cost, and supports dynamic adjustment to meet actual requirements and user feedback. And carrying out model training by adopting a deep Q network algorithm, and evaluating a model effect through an A / B test. In the real-time scheduling stage, the method can collect the state information of the parking lot in real time, selects the optimal action according to the current state and strategy, and achieves efficient and intelligent parking management. The implementation of the invention is helpful for improving the overall intelligent level of the smart community, enhancing the attraction and competitiveness of the community, and providing powerful support for the construction and development of the smart community.

The invention belongs to the technical field of waste heat recovery and discloses an energy recovery and heat supply system and method based on thermoelectric conversion, and the method comprises the steps that an energy recovery unit collects heat of an industrial waste heat source, and a circulating pump drives a heat transfer medium to circulate in a pipeline; a stable temperature difference is formed at the two ends of the thermoelectric module, and thermoelectric conversion is carried out; the electric energy management unit rectifies and stabilizes the electric energy output by the thermoelectric module; a part of the electric energy after voltage stabilization is stored in a storage battery pack; the electric heating boiler is driven by electric energy to work; the heated hot water is conveyed to an area needing heat supply; and the monitoring module is controlled to operate to perform operation processing according to a preset algorithm and heat supply requirements. The energy recovery and utilization efficiency is high, waste heat resources which are wasted originally can be effectively recovered and converted into electric energy and heat energy to be used, an electric energy storage device, an auxiliary heating device and a monitoring system are arranged, and heat supply stability and continuity can be guaranteed under various working conditions.

The invention discloses a flue gas emission continuous monitoring device and a monitoring method, and relates to the technical field of flue gas emission, the device realizes gas inlet detection and multidirectional gas inlet and gas exhaust of cleaning pipelines and parts through a U-shaped gas inlet pipe, a preheating pipe, a first gas return pipe, a first gas inlet pipe, a gas collection chamber, a condensation pipe, a liquid collection chamber and a second gas return pipe; preheating and heat preservation are achieved through air inlet and air outlet, heat preservation measurement is achieved in the heat preservation shell, pipelines and parts, interference of temperature, oil and particulate matter is effectively reduced, and the monitoring precision is improved; the laser scattering sensor is used for measuring the concentration and the particle size of particulate matters, the optical fiber measuring unit is used for monitoring other components in flue gas, and real-time monitoring and emission component analysis of the flue gas are realized by collecting released liquid and measuring substances dissolved in the liquid; the problems that smoke monitoring is easy to interfere, detection data are easy to fluctuate, and a multi-operation-mode switching technology of a detection device is lacked, so that the smoke monitoring precision is low are solved.

The invention relates to the technical field of soilless culture, in particular to an intelligent cultivation tank system and a plant cultivation method. The system comprises a sensor configured to collect environmental data of a cultivation tank; the artificial intelligence module is configured to identify and analyze the environment data, obtain different types of sub-environment data, and generate a first nutrient solution adjusting instruction based on the different types of sub-environment data; the digital twinning module is configured to perform matrix conversion on the sub-environment data and map the sub-environment data to a digital twinning scene; the digital twinning module is configured to analyze spatio-temporal data of the plant in real time in a digital twinning scene and trigger a second nutrient solution adjusting instruction when the growth stage of the plant changes; the nutrient solution circulating device is configured to adjust the concentration of the nutrient solution in the cultivation tank based on the first nutrient solution adjusting instruction and / or the second nutrient solution adjusting instruction. According to the system, the intelligence of the soilless culture system is improved, and precise control over the plant growth environment is achieved.

The invention discloses a step type mine quarrying face in-situ ore moving type head section treatment process which comprises the steps that a crawler belt impact type roller crusher is arranged on a mine quarrying face in situ, and the crawler belt impact type roller crusher receives, conveys and crushes in-situ ore mined on the mine quarrying face; each crawler belt moving belt conveyor comprises a movable crawler belt driving base and a conveying belt conveyor body arranged on the crawler belt driving base, and the crawler belt moving belt conveyors are connected end to end to convey materials; the mine quarrying working face comprises a top quarrying working face arranged on the mountain top and a stepped in-mountain quarrying working face arranged on the lower portion of the mountain top. By means of the mountain height difference, the crawler belt moves the belt conveyor, so that conveyed materials roll down from a mine quarrying face towards the mountain or are directly conveyed to the downhill through the long-distance belt conveyor. According to the invention, head section crushing work and quarrying work are combined, intermediate transportation links are reduced, energy consumption is saved, cost is reduced, and flexibility and efficiency are high.

The invention relates to the technical field of line external damage prevention, in particular to a dynamic external damage prevention monitoring and early warning system and method for power equipment. The system comprises an unmanned aerial vehicle which carries a camera and is used for monitoring the power equipment and the surrounding environment in real time, and the unmanned aerial vehicle sends monitored data to an image processing module; the data processing module is used for receiving monitoring data of the unmanned aerial vehicle and preprocessing the monitoring data; the identification and tracking module is used for carrying out multi-target identification and real-time tracking on the preprocessed data; the alarm module is used for giving an alarm if the recognition and tracking module detects that the power equipment is threatened by the external damage target; and the control module is used for adjusting the flight height of the unmanned aerial vehicle and the shooting angle of the camera. The external damage target can be monitored, recognized and early warned in real time, so that countermeasures are taken in time, and accidents are prevented from occurring or expanding.

The invention belongs to the technical field of energy management of hybrid power systems, and discloses a quantum strategy learning energy management method and system for a multi-stack fuel cell hybrid power system, and the method comprises the steps: building a multi-stack fuel cell hybrid power system model which comprises a fuel cell power model, a lithium battery equivalent circuit model and power balance; constructing a quantum hybrid neural network, interacting with the established hybrid power system environment, distributing output power of different power sources, and constructing a state-action-reward set; the strategy is evaluated and updated on the basis of the collected state-action-reward data set, and then the energy management strategy is optimized. By fusing the variable component sub-circuit and the traditional neural network, efficient operation of the fuel cell system is ensured while the state of charge of the lithium battery is maintained in an ideal interval; and the gradient of the quantum circuit parameters is accurately calculated by using a parameter transfer rule, so that the optimization process of the quantum strategy learning energy management strategy is more accurate and efficient.

The invention discloses a building energy consumption management system and method based on big data, and relates to the technical field of energy consumption management, and the system comprises an intelligent energy consumption analysis module, an equipment health monitoring module, a dynamic energy consumption scheduling module, a renewable energy integration module, a user behavior analysis module, and a collaborative energy management module. The method comprises the steps of data acquisition and input, data analysis and decision, and resource sharing and collaborative management. According to the big data-based building energy consumption management system and method, through implementation of the big data-based building energy consumption management system, a building manager can realize comprehensive monitoring and intelligent optimization of energy consumption, energy consumption and operation cost are significantly reduced, and comfort and satisfaction of a user are improved at the same time; through integration of real-time data and an advanced analysis technology, environment setting in a building can be effectively adjusted, sharing of renewable energy sources and collaborative management of communities can be promoted, the goal of sustainable development is promoted, and the win-win situation of economy and environmental protection is achieved.

The invention provides an adaptive constraint federated learning method, device and system based on reinforcement learning, and the method comprises the steps: determining a hyper-parameter of an action of a current round based on a local reinforcement learning agent unit according to a global model weight and a local model weight of a previous round; performing adaptive constraint on the local model based on hyper-parameters in the training process, sending the local model weight of the round to a server in the federated learning system for aggregation, and determining the reward of the current round according to the global model weight and the local model weight corresponding to the previous round and the current round based on a reinforcement learning agent unit; and forming a tuple sample of the current round and storing the tuple sample. According to the method, the influence of data heterogeneity can be reduced, and the reliability and effectiveness of model training and the performance of the trained model can be improved; independent decision making is carried out through the local reinforcement learning agent, and extra data communication between the server and the client is not needed.

The invention discloses a method for electric spark machining of a high-carbon high-chromium molybdenum alloy steel chute, a first group of electrodes is adopted to machine the chute to be machined, the first group of electrodes comprises a first electrode, a second electrode and a third electrode, when the first group of electrodes is adopted to machine each chute, the first electrode is firstly used for rough machining, and then the second electrode is used for machining; carrying out semi-finish machining by using a second electrode, and finally carrying out finish machining by using a third electrode; after a plurality of skewed slots are machined, after a first electrode in a first group of electrodes is abraded, a second group of electrodes is adopted to machine the skewed slots to be machined, the second group of electrodes comprises a second electrode, a third electrode and a fourth electrode, when the second group of electrodes is adopted to machine each skewed slot, the second electrode is firstly used for rough machining, and then the third electrode is used for semi-finish machining; and finally, finish machining is conducted through a fourth electrode. And in this way, after the (n-3) th electrode in the (n-3) th group of electrodes is abraded, the (n-2) th group of electrodes is adopted to machine the chute to be machined, the (n-2) th group of electrodes comprises the (n-2) th electrode, the (n-1) th electrode and the nth electrode, when the (n-2) th group of electrodes is adopted to machine each chute, the (n-2) th electrode is firstly used for rough machining, and then the (n-1) th electrode is used for semi-finish machining, and finally, finish machining is conducted through the nth electrode. The invention aims to improve the processing efficiency and reduce the production cost while ensuring the processing quality.

The invention relates to the field of intelligent traffic management, and discloses an efficient road obstacle clearance rescue worker automatic scheduling method comprising the following steps: (a) deploying at least one message queue to cache rescue related data; (b) storing and querying rescue tasks and resource state data through a distributed database system; (c) collecting data from the rescue vehicle and the accident site in real time, and performing preprocessing; (d) asynchronously processing the collected data through a message queue according to the priority and the task type; (e) dynamically scheduling rescue resources by using a multi-factor decision algorithm; the message queue adopts a Kafka cluster and is configured with at least three partitions and at least two copies so as to ensure that a single server or partition fails. Through combination of a multi-factor decision scheduling algorithm and real-time traffic flow prediction, the system can dynamically adjust the scheduling route of the rescue vehicle, and the rescue vehicle is prevented from entering a congested road section, so that the time for arriving at an accident scene is remarkably shortened, and the overall rescue efficiency is improved.

The utility model relates to oil-water separation equipment for the coal chemical industry, and relates to the field of oil-water separation. One end of the horizontal separation tank is fixedly communicated with a liquid inlet pipe, the other end of the horizontal separation tank is fixedly communicated with a liquid outlet pipe, and a buffer chamber, a primary coalescence chamber, a refined coalescence chamber and a secondary coalescence chamber are sequentially arranged in the horizontal separation tank in the direction from liquid inlet to liquid outlet; the first-stage coalescence system is positioned in the first-stage coalescence chamber and is used for primarily separating an oil-water mixture; the fine coalescence system is positioned in the fine coalescence chamber and is used for performing fine separation on the oil-water mixture; the second-stage coalescence system is positioned in the second-stage coalescence chamber and is used for carrying out secondary separation on the oil-water mixture; the top of the horizontal separation tank is communicated with a light oil tank, and the bottom is communicated with a heavy oil tank. By means of the three-stage coalescence system, efficient separation of an oil-water mixture can be achieved, and a good separation effect is achieved.

The invention belongs to the field of green land maintenance, and provides an automatic simple maintenance method and system for greening of an arid valley slope, and the method comprises the steps: collecting soil data and historical meteorological data of a greening region before greening of the arid valley slope; according to the soil data and the meteorological data, first plants suitable for regreening are selected, and the first plants are evenly laid on the upper portion, the middle portion and the lower portion of the slope in different dimensions; shooting a first image of the first plant through a camera; the water shortage degree of the first plant is judged according to the first image, and water is supplemented to the first plant according to the water shortage degree; and judging the fertilizer deficiency degree of the first plant according to the first image, and fertilizing the first plant according to the fertilizer deficiency degree. According to the scheme, automatic and simple greening maintenance of the slope of the dry river valley can be efficiently carried out.

The invention belongs to the technical field of automatic focusing, and particularly relates to a dynamic focusing method based on an industrial measurement system. Comprising the following steps: S1, fixing a target on a measured target, and aligning a lens of an industrial measurement system with the measured target to obtain an image data set of the measured target; s2, sequentially inputting the detected target images into a target acquisition network to obtain a target image data set; s3, correspondingly calculating the working distance of the target relative to the camera based on the prediction frame of each target image; s4, all the target images are subjected to denoising and illumination normalization processing, and a processed target image data set is obtained; s5, predicting the predicted defocus distance of each processed target image by using a deep regression network; s6, performing static adjustment on the lens to obtain a clear first frame of target image; and S7, dynamically adjusting the lens according to the motion state of the target to be measured and the working distance of the target relative to the camera. The invention has the capability of dynamic focusing.

The invention provides an air conditioner control method for zone adjustment, an air conditioner system and a storage medium, and the method comprises the following steps: when entering a heat exchange mode, obtaining personnel position distribution data in a current physical space; dividing the current physical space into more than two temperature control areas according to the personnel position distribution data; determining a target temperature value corresponding to each temperature control area; and according to the target temperature value, the corresponding air conditioner is controlled to conduct temperature adjustment on each temperature control area. By means of the air conditioner control method, the indoor temperature control area division accuracy and flexibility can be improved.

The invention belongs to the technical field of preparation of a silicon fertilizer from coal gangue, and particularly relates to a silicon fertilizer preparation method for improving the effective silicon conversion rate of coal gangue and reducing energy consumption. The invention aims to solve the problems of low silicon resource utilization rate and high energy consumption in the prior art. The preparation method comprises the following steps: (1) crushing coal gangue, sieving and drying for later use; (2) adding a specific auxiliary agent and coal gangue according to a certain proportion, and fully mixing; (3) roasting the mixture at a certain temperature for a certain time; and (4) cooling the roasted product to obtain the silicon fertilizer. The silicon dioxide in the coal gangue can be effectively converted into silicate which can be conveniently absorbed and utilized by plants, the yield of the silicon fertilizer is increased, the environmental problem caused by accumulation of the coal gangue can be solved, the silicon fertilizer can also be used as a soil improvement material, good economic benefits and social benefits are achieved, and a solid foundation is laid for wide utilization of the coal gangue. Compared with a traditional method, the method has obvious economic and environmental benefits.

The invention discloses a block chain Filecoin data analysis platform, which is characterized in that user data is stored on a plurality of nodes in a distributed manner by utilizing a decentralized storage characteristic of a Filecoin network, and the tampering resistance and security of the data are ensured through encryption and a consensus mechanism of a block chain; according to the method, an efficient block chain transaction processing and data analysis algorithm is adopted, a data analysis task is automatically executed through an intelligent contract, the use of data transmission and computing resources is optimized, and the efficiency of data processing and analysis is greatly improved; the advanced encryption technology is adopted to perform end-to-end encryption on the data, and a strict access control strategy is implemented to protect the privacy and security of the user data; all operation records and data analysis results of the platform are recorded on the block chain, so that the transparency and traceability of an analysis process are ensured, the analysis results cannot be tampered and can be verified, and the trust of a user to the analysis results is enhanced.

The invention discloses a prevention and control method for early intervention of thermal runaway of a lithium battery, and aims to solve the problems that in the prior art, an alarm can be given only when smoke and temperature are obviously increased, the reaction time is long, and early intervention cannot be realized. The prevention and control method is divided into a first-level keep-alive operation mechanism and a third-level early warning operation mechanism, and comprises the following steps: in a 0-level state, a PACK-level sensor and a master control maintain online heartbeat; the first-level state is that the PACK-level sensor detects data exception in the early stage; in the second-stage state, the PACK-stage sensor preliminarily considers that the battery is possibly in the early stage of thermal runaway, and the PACK detector sends a second-stage fire alarm state to the controller; and in the third-level state, the PACK level sensor determines that the battery PACK is in the thermal runaway state, and the PACK detector sends a third-level fire alarm state to the controller. According to the method, the temperature of the battery PACK is rapidly reduced through early intervention and low-quantity disposal strategies, the battery PACK is prevented from entering a thermal runaway state, and therefore fire disasters are avoided, economic losses are reduced, and the safety of the system is improved.

The invention provides a control method for the rolling force of a temper mill and related equipment, and relates to the technical field of metallurgical production, the method comprises the steps that pattern information of strip steel is obtained, and the pattern information comprises width information and thickness information; determining the height of an inlet and the height of an outlet of the anti-wrinkling roller based on the model information; determining the diameter of a working roll based on the model information; determining inlet tension and outlet tension according to the diameter of the working roll; and controlling the rolling process of the temper mill based on the height of the inlet of the anti-wrinkling roller, the height of the outlet of the anti-wrinkling roller, the inlet tension and the outlet tension.

The invention discloses high-viscosity dimethyl silicone oil and a preparation method thereof.The preparation method comprises the following steps that a siloxane polymer is heated, a basic catalyst is added in the inert gas atmosphere, and a first mixture is obtained; continuously heating and adding an end-capping reagent into the first mixture in a vacuum environment for reaction to prepare a second polymer; adding a neutralizer into the second polymer for reaction to obtain a third polymer; and heating and devolatilizing light components in the third polymer in a vacuum environment to obtain the finished product silicone oil. A high-viscosity dimethyl silicone oil product prepared by carrying out a catalytic reaction in a potassium hydroxide silanolate-silicon-based phosphate catalytic neutralization system and then using a linear siloxane oligomer as a main polymer has the advantages of low polymerization intermediate volatile matter, high product viscosity and low molecular weight distribution coefficient, and has a wide application prospect.

The invention provides a pathological image analysis system based on weak supervised learning. The pathological image analysis system comprises a model processing module, an information transmission module and a data feedback module, the model processing module performs image processing and intelligent analysis on the digital pathological section by adopting weak supervised learning to obtain an intelligent analysis result of the digital pathological section; the information transmission module encrypts and transmits the digital pathological section intelligent analysis result to the data feedback module; and the data feedback module generates feedback information according to the intelligent analysis result of the digital pathological section, and presents the feedback information to a pathologist. According to the pathological image analysis system based on weak supervised learning provided by the invention, the dependence on the labeled data is reduced through weak supervised learning, the cost consumption is effectively reduced, the system can adapt to the characteristics of different types of digital pathological sections, the limitation of the pathological image analysis system is eliminated, and the application range of the pathological image analysis system is widened.

The invention discloses an inter-satellite laser link capturing device and method based on front finger angle rotating mirror space scanning, and belongs to the field of spacecraft laser capturing. A local-end satellite uses a front finger angle rotating mirror to control emergent laser beams to perform sub-region space scanning; a large-mass one-dimensional overall pointing deflection mechanism and a satellite platform are used for controlling emergent laser beams to execute primary driving so as to move the sub-regions; repeating the first step after moving the sub-region; and circularly carrying out regional scanning on the uncertain region until the far-end satellite receives the irradiation of the emergent laser beam so as to correct the direction of the far-end satellite and emit a retroreflection beam to the local-end satellite, and stopping scanning and correcting the direction of the local-end satellite after the local-end satellite receives the retroreflection beam. According to the inter-satellite laser link capturing device and method based on front finger angle rotating mirror space scanning, the purpose of greatly reducing the time cost of an inter-satellite laser link capturing task and the consumption cost of a propellant by fully utilizing limited beam pointing control resources on a satellite can be achieved.

The invention provides an intelligent mining production management and control method based on an ADP algorithm, and relates to the technical field of intelligent mining, and the method comprises the steps: firstly obtaining vehicle data and wrong vehicle point data, carrying out the road division of a two-way single lane with a wrong vehicle point as the end points of the two ends of a road section, and carrying out the position judgment and display of a running vehicle in the two-way single lane; judging passing priorities of the vehicles, and checking whether vehicles running in opposite directions exist in adjacent road sections of the current vehicle or not; carrying out data preprocessing on the collected mine car scheduling data, dividing the data into a training data set and a test data set, carrying out classification processing on the preprocessed data, and establishing a classification model; identifying and dividing a naturally formed area of the underground mining area operation area by adopting a clustering algorithm; establishing a nonlinear system model, establishing a performance index, comprehensively considering transportation efficiency, cost saving and time benefit, measuring the effect of a scheduling strategy, and setting an optimization target; and establishing an ADP algorithm, and outputting an optimal scheduling scheme.