4767 results about "Economic benefits" patented technology

The invention relates to the technical field of metallurgical raw material batching control, and discloses a bottom blowing smelting furnace automatic batching intelligent control method and system based on deep learning, and the method comprises the steps: achieving intelligent batching through multi-source data fusion, physical constraint modeling and dynamic optimization control; edge calculation is adopted to realize data space-time alignment and purification, and physical and economic mixed features are constructed; modeling a reaction path based on a graph neural network, and embedding conservation law constraint to synchronously predict key process parameters; and finally, in combination with gradient sensitivity analysis and reinforcement learning, constructing a differentiable optimization framework to realize multi-target dynamic ratio decision and real-time compensation control, and forming a perception-decision-execution closed loop. The system comprises a global sensing and data purification module, an intelligent decision-making and optimization batching module and a high-precision execution and closed-loop control module. According to the invention, the batching strategy is adaptively adjusted, and optimal resource allocation and maximum economic benefit are realized.

The invention discloses a deep and large reservoir ecological scheduling method, system and equipment based on a data-driven model of a coupling physical mechanism, and belongs to the technical field of water resource management and environmental protection. Firstly, a physical water temperature model is constructed based on measured data, diversified water temperature change scenes are generated, and a deep learning model constrained by a physical mechanism is constructed. Secondly, carrying out sensitivity analysis to identify key influence factors for driving water temperature change, constructing a reservoir optimization scheduling model, coupling a deep learning model constrained by a physical mechanism, and deducing a scheduling rule set on the premise of meeting a water temperature target; and finally, carrying out multi-index optimization analysis. The invention further provides a deep and large reservoir ecological scheduling system and electronic equipment, and the deep and large reservoir ecological scheduling method is realized. According to the method, the power generation scheduling rule set of the deep and large reservoir can be scientifically deduced, the optimal scheduling scheme with both ecological benefits and economic benefits is screened out by introducing the multi-index optimization method, and overall balance of ecological requirements and power generation benefits is achieved.

The invention relates to the technical field of environmental protection and climate change, in particular to an enterprise carbon emission reduction data intelligent monitoring and analysis system, which comprises a data acquisition and preprocessing module, a mechanism model library module, a carbon emission accounting and monitoring module, an abnormality diagnosis and traceability module, an emission reduction potential analysis module and a visualization and report module. Through multi-source data access, cleaning and standardization processing, high-quality and uniform-format basic data are provided for the system, the accuracy and reliability of subsequent carbon accounting and analysis are guaranteed, accurate positioning and source tracing of carbon emission abnormity are achieved by means of causal reasoning and a path optimization algorithm, and the carbon emission abnormity analysis method has the advantages of being high in practicability and easy to popularize. A scientific basis is provided for an enterprise to quickly solve the problem that emission exceeds the standard, and through reinforcement learning simulation, multi-target optimization and knowledge graph matching, an optimal strategy considering the emission reduction effect, cost and productivity is dynamically recommended, and the enterprise is assisted to achieve collaborative improvement of carbon emission reduction and economic benefits.

The invention discloses a tuna pond culture strategy optimization method and system based on meteorological influence analysis, and the method comprises the steps: collecting regional meteorological data and pond culture monitoring data, and constructing a multi-modal pond ecological feature vector; establishing a weather-water body dynamic coupling model by using a space-time diagram neural network, and generating water body situation response prediction information; a breeding regulation and control strategy is made based on the prediction information, and strategy self-adaptive optimization is achieved through real-time monitoring data; evaluating the anti-disaster capability of the tuna after the meteorological influence is finished, screening high-quality germplasm resources by combining genetic analysis, and generating an anti-disaster breeding scheme. According to the method, full-chain optimization from meteorological early warning to culture regulation and control to genetic breeding is achieved, and the climate toughness and economic benefits of tuna pond culture are comprehensively improved.

The invention discloses a traditional Chinese medicinal material standard base pest and disease damage intelligent monitoring and precise control method and control system, and relates to the technical field of agricultural intelligence. The Internet of Things technology, the block chain and the artificial intelligence algorithm are integrated to improve monitoring accuracy and prevention timeliness, environment and plant states are acquired through the sensor and image data, safe storage of data is ensured by using the block chain, early recognition of diseases and pests is performed by using the convolutional neural network, and the accuracy of disease and pest control is improved. A disease risk index is calculated in combination with environmental parameters, a reinforcement learning algorithm dynamically optimizes a control strategy, control measures are automatically executed through Internet of Things equipment, an execution result is recorded on a block chain, in addition, the strategy is continuously adjusted according to a control effect evaluation result, intelligent and precise disease and pest management is realized, resource consumption is effectively reduced, and the economic benefit is improved. The traditional Chinese medicinal material production safety and efficiency are improved.

The invention discloses a hydrogen energy park planning method and system based on a heuristic algorithm, and relates to the technical field of smart energy management, and the method comprises the steps: constructing a mathematical model of a park comprehensive energy system which is composed of a renewable energy power generation unit, an energy conversion device, a carbon capture and gas power device and a multi-type energy storage unit; based on the mathematical model, with the minimum annual comprehensive cost of the park comprehensive energy system as a target, establishing a cost target function, setting constraint conditions, and formulating an energy management strategy; and according to an energy management strategy, solving according to an improved taboo horse racing optimization algorithm THRO, and planning the hydrogen energy park according to a solving result. According to the invention, not only is the interior of the exterior of the environment realized, but also the maximization of economic benefits is realized.

The present application relates to the technical field related to greenhouse planting, and particularly relates to a crop management and control method and apparatus for a smart greenhouse, and a device and a medium. The method comprises: acquiring environmental data and crop data; inputting the environmental data and the crop data into a preset regulation and control model to obtain an optimal planting scheme and / or an optimal environment control strategy; and on the basis of the environment control strategy, regulating and controlling preset smart remote control devices in a smart greenhouse. By means of the configuration in the solution provided in the present application, accurate control of a planting environment can be performed by collecting environmental data and crop data in a timely manner, thereby increasing the crop growth rate and improving the production efficiency. Furthermore, a preset regulation and control model is used in the present application, so as to automatically optimize a planting scheme and an environment control strategy on the basis of different environmental conditions and crop requirements, thereby reducing manual intervention and management costs, and thus improving the production efficiency and economic benefits.

The invention provides an irrigation district water resource scheduling optimization method based on cloud computing, and the method comprises the following steps: collecting irrigation district multi-source data in real time through a space-air-ground integrated sensing network, and uploading the data to a cloud platform after preprocessing; the method comprises the following steps: constructing digital twin bodies of an irrigation area in a cloud platform, dividing the irrigation area into a plurality of sub-areas according to water consumption attributes, and integrating hydraulics and hydrological agriculture models; real-time data and future weather information are fused, and a water demand prediction value is generated through a water demand prediction model by taking the sub-region as a unit; a scheduling scheme is generated by adopting a multi-objective optimization algorithm based on the predicted value, and the minimum economic benefit, water resource utilization rate and energy consumption are taken as optimization objectives; and performing simulation evaluation on the scheme by using the digital twinborn body, outputting a performance index aided decision, and finally determining an optimal scheduling scheme. According to the method, accurate prediction, dynamic optimization and intelligent scheduling of the water resources in the irrigation area are realized, and the utilization efficiency of the water resources and the irrigation management level are improved.

The invention belongs to the technical field of medical material management, and particularly relates to a medical material intelligent supply chain management scheme and system based on multi-modal AI. According to the invention, through multi-modal data fusion, full-life-cycle and full-link tracking of materials is realized, information islands are eliminated, management transparency is improved, unexpected consumption and physical damage risks are identified in an early stage, a large amount of overstock or clinical supply interruption is avoided, operation cost is reduced, and based on a dynamic loss threshold, replenishment and cross-warehouse allocation can be realized just in need. The inventory turnover rate and the supply guarantee rate are considered, environment monitoring and validity period information are processed, it is ensured that temperature and humidity sensitive materials are stored under the optimal condition, losses caused by expiration or environment abnormity are reduced, the waste rate and the excessive stockpiling risk are comprehensively reduced, meanwhile, timely availability of key materials is clinically guaranteed, and the service life of the materials is prolonged. And the economic benefit and safety of the medical material supply chain are improved.

The invention relates to the technical field of new energy grid connection, and discloses a wind power plant power generation performance evaluation method, device and equipment and a storage medium, and the method comprises the steps: calculating the power generation capability parameters of a target wind power plant according to the operation parameters and configuration parameters of the target wind power plant; calculating an economic effect evaluation parameter according to the availability rate of the target wind power plant, the power grid compatibility comprehensive score, the power generation capacity parameter and the health coefficient; and determining a power generation performance evaluation result of the target wind power plant according to the power generation capacity parameter and the economic effect evaluation parameter. According to the power generation performance evaluation method for the wind power plant provided by the embodiment of the invention, the limitation that evaluation is carried out only from a single index of short-term power generation capacity traditionally is avoided in a mode of combining the power generation capacity parameters and the economic effect evaluation parameters, and comprehensive evaluation of the power generation capacity and the economic benefits is realized.

The present application provides a reverse-type integrated miniature linear servo electric cylinder. An open end of a rotor housing in a power transmission mechanism is fixedly connected to a planetary carrier by means of a coupling, the planetary carrier is directly driven by means of integration of the motor rotor housing and the planetary carrier, and after power transmission via the planetary carrier, an output end is directly driven to achieve linear motion without adding a gear reduction mechanism to a power transmission system of the electric cylinder, thereby significantly reducing the internal space of the electric cylinder occupied by the power transmission mechanism, allowing the electric cylinder to develop to have a more miniaturized and compact structure, saving costs; moreover, the energy utilization rate of the electric cylinder is improved, the electric energy consumption of the electric cylinder is reduced, and relatively good economic benefits can be yielded.

The invention belongs to the technical field of bioinformatics, particularly relates to a computer simulation and regulation method and system for beef cattle fat metabolism, and aims to solve the problems of excessive fat deposition, low feed conversion efficiency, difficulty in multi-objective optimization and the like in existing beef cattle breeding. A multi-scale biological network model is constructed, a computer simulation and prediction engine is developed, an intelligent intervention strategy optimization engine is designed, and implementation and feedback optimization are carried out. By reducing unnecessary fat deposition, improving the feed conversion efficiency and improving the beef quality, the economic benefit of beef cattle breeding is remarkably improved, the requirements of consumers for high-quality and safe beef products are met, and the method has important value for promoting intelligent upgrading and sustainable development of the beef cattle industry.

The invention provides a rail transit engineering low-carbon evaluation method, and relates to the technical field of green low carbon, and the method comprises the steps: obtaining multi-source data of rail transit engineering, and determining a dynamic carbon emission factor of the rail transit engineering based on the multi-source data; based on the dynamic carbon emission factor, determining the total carbon emission of the full life cycle of the rail transit project, wherein the total carbon emission comprises a first carbon emission in a construction stage, a second carbon emission in an operation stage and a third carbon emission caused by the influence of the rail transit project on the social environment; and constructing a correlation model of the total carbon emission and the economic benefit, and determining the comprehensive benefit index of the rail transit project based on the correlation model. According to the method, the multi-source operation data of the rail transit project can be integrated to calculate the dynamic carbon emission factor, accurate quantification of the full-life-cycle carbon emission of the rail transit project is achieved, and therefore a decision basis is provided for low-carbon planning, operation and policy making of the rail transit project.

The invention aims to provide a thermal power plant water production workshop production scheduling optimization method and device, and the device comprises a rolling time domain prediction module, a multi-source data fusion module, a multi-target optimization decision module, and a closed-loop feedback control module. The problems that in existing production scheduling, multiple factors are not fully and comprehensively considered, so that the water production cost is high, production arrangement is unreasonable, night shift production scheduling cannot be effectively reduced and the like are solved, maximization of water production economic benefits is achieved, and safe production is guaranteed. The invention provides an intelligent water production and production scheduling system and method for demineralized water of a thermal power plant. Water production cost minimization and production safety collaborative optimization are achieved through data fusion, prediction optimization and dynamic closed-loop control.

The invention discloses an intelligent control system and method for shot blasting equipment based on multi-modal perception, and relates to the technical field of industrial surface treatment.The intelligent control system and method for the shot blasting equipment based on the multi-modal perception have the advantages that a technological parameter self-evolution decision-making mechanism with deep reinforcement learning as a core and real-time feedback of discharging quality as a system is established; the shot blasting process is converted from static experience setting to dynamic model driving, a process parameter optimization model serves as a deep reinforcement learning agent, instant reward functions of processing quality, energy consumption, material consumption and equipment loss are comprehensively considered, and the function guide model pays attention to whether the final quality reaches the standard or not, and also pays attention to the real-time reward functions of the processing quality, the energy consumption, the material consumption and the equipment loss. And meanwhile, the comprehensive economic benefits of the whole process are actively optimized, the quality evaluation and prediction model performs real-time and quantitative analysis on visual images of discharged workpieces, and immediately feeds back results to the decision model, so that the system has the learning ability, and autonomously explores and converges to an optimal process parameter combination for specific workpieces and specific states.

The invention discloses a fluid distribution dynamic regulation and control method and device based on a shale reservoir microstructure, and relates to the technical field of oil and gas exploitation. The method comprises the steps that microstructure parameters of a target shale reservoir are obtained through a core experiment, and a multi-scale model is constructed based on the microstructure parameters; determining control equations of different physical fields of the multi-scale model; coupling the control equations of different physical fields, and resolving through a finite element method; performing a numerical simulation experiment based on the multi-scale model and the resolving result to determine regulation and control parameters; the regulation and control parameters are input into the multi-scale optimization model to optimize the regulation and control parameters, and a regulation and control strategy is obtained. The problems that an existing displacement experiment is single, the complex interaction process under the actual mining environment is not comprehensively considered, and the guiding effect on mining work is limited are solved. Business personnel can be guided to formulate a development scheme more scientifically and accurately, the mining efficiency and the success rate are improved, the development cost can be reduced, and meanwhile economic benefits are improved.

The invention belongs to the field of flame-retardant finishing of textiles, and discloses a phytic acid-based flame retardant and a preparation method of a flame-retardant cotton fabric. Natural phytic acid, polyethylene glycol, melamine and metal salt are used as main raw materials, the environment-friendly characteristic of a biomass material is combined, an efficient and environment-friendly flame retardant is prepared, the prepared flame retardant is arranged on a cotton fabric through a traditional impregnation method, and the flame retardance and smoke suppression performance of the arranged cotton fabric are greatly improved. The method not only has a good flame retardant effect, but also avoids potential hazards of a traditional flame retardant to the environment and human health, and has a wide application prospect. The flame-retardant cotton fabric can be widely applied to the fields of household textiles, protective clothing, fire-fighting equipment and the like, and has remarkable economic benefits and social benefits. The invention not only provides an efficient and environment-friendly flame retardant preparation method, but also opens up a new way for application of biomass materials in the field of flame retardance, and has important scientific significance and practical value.

The invention belongs to the technical field of environmental protection control, and relates to a cost reduction method and device based on carbon asset full life cycle management, and the method comprises the steps: obtaining the carbon emission data converted from the cost input of each stage of an enterprise; the carbon emission data is analyzed; formulating a carbon emission list based on the analysis result of the carbon emission data, the carbon emission thermodynamic diagram and the carbon quota of the enterprise; predicting the carbon emission of the enterprise based on the carbon emission list; establishing an enterprise carbon management personal incentive mechanism based on the result of predicting the enterprise carbon emission; and preposed carbon sink design is carried out, so that the full-life-cycle income of carbon assets is improved. The preposed carbon sink design is emphasized, and carbon sink projects such as afforestation and energy conservation and emission reduction technology application are reasonably planned, so that the carbon emission of enterprises is effectively counteracted, the full-life-cycle income of carbon assets can be improved, and the win-win situation of economic benefits and environmental benefits is realized.

The invention discloses an automatic guider assembly line and an automatic guider assembly method, and relates to the field of manufacturing of automobile shock absorbers, and the automatic guider assembly line is characterized by comprising a first material machine and a second material machine which are separated from each other, and a butt joint conveying belt for connecting the first material machine and the second material machine. The first material machine is provided with an assembly rotating disc used for sequentially stacking parts such as an oil scraper ring and a valve block to form a small assembly, and a small assembly detection station and a small assembly NG discharging station are arranged before the small assembly is installed into a guider bare part. And the second material machine is used for performing bushing press fitting and final detection on the semi-finished products conveyed from the first material machine. Precise assembly and high-force press-fitting procedures are separated, so that the problem of process interference is effectively solved, and the assembly stability and the product quality are improved; meanwhile, the middle quality doors are arranged on the sub-assembly levels, so that unqualified products can be removed in advance, the cost superposition type waste is fundamentally avoided, and the production efficiency and the economic benefits are remarkably improved.

The invention discloses a multifunctional cellulolytic enzyme and application thereof in degradation of agricultural by-products, and belongs to the technical field of biology. The multifunctional cellulolytic enzyme is named RuCelI, and the amino acid sequence of the multifunctional cellulolytic enzyme is as shown in SEQ ID NO. 1. Based on a rumen microbial metagenome resource mining technology, a novel multifunctional cellulose decomposition enzyme RuCelI is developed, and the enzyme has double activities of endo-cellulase and xylanase, can be used for degrading agricultural by-products, reducing the treatment cost and improving the degradation efficiency, and is beneficial to improving the utilization rate and the economic benefit of the agricultural by-products.

The invention discloses a pond multi-objective optimization management method oriented to agricultural watershed non-point source pollution regulation and control, and relates to the technical field of agricultural watershed non-point source pollution abatement. Multi-dimensional attributes such as pond application types, morphological characteristics and sediment nutrient states are integrated into a unified quantitative framework for the first time, and a generalized additive model is constructed to obtain a multi-objective optimization model; nonlinearity and interaction of pond attributes on river water quality influence can be accurately captured, and the accuracy of river nitrogen and phosphorus concentration prediction is improved. According to the method, pond management measures are quantified into controllable decision variables, a multi-target dynamic optimization model for balancing environmental benefits and economic benefits is established, a Pareto optimal solution is used for replacing a traditional experience decision, a scientific, efficient and quantitatively-implemented pond management scheme is provided, the one-sidedness of a single target is avoided, and the method is suitable for large-scale popularization and application. The cost minimization is realized while the non-point source pollution reduction is maximized, and the accurate configuration and scientific quantitative decision of pond management resources are realized.

The invention discloses a method for preparing 3003 alloy container aluminum foil from high-regeneration aluminum. The method comprises the specific steps of smelting, continuous casting and rolling, homogenizing annealing, cold foil rolling, finished product annealing and checking and packaging. During smelting, the secondary aluminum waste and the electrolytic aluminum liquid are used at the same time, the quality of the aluminum melt is guaranteed, and therefore the short-process technology that high-proportion secondary aluminum waste is used, and continuous casting and continuous rolling coils are directly produced through the electrolytic aluminum liquid is achieved; a continuous casting and rolling production process is adopted to directly use aluminum melt to produce an aluminum coil with the thickness of 3-5mm, so that 1-2 rolling passes can be directly reduced, energy is saved, consumption is reduced, the production cost is reduced, and obvious economic benefits and social benefits are achieved; compared with 25-30 hours of homogenizing annealing of cast-rolled blanks in the prior art, the time is shortened by 5 hours, the material production process flow can be simplified, the operation intensity of staff is relieved, the metal in-process time is saved, and the production cost is reduced.

The invention discloses an energy storage multi-time scale coordinated optimization scheduling method and system based on model prediction control and a medium, and the method combines the dynamic characteristics of an energy storage system and the scheduling demand of multi-time scales, and carries out the coordination between short-term scheduling and long-term scheduling through model prediction control. Wherein the short-term scheduling is based on the real-time predicted system state and demand, and model prediction control is adopted for optimization so as to realize accurate control of the charging and discharging process of the energy storage equipment; for long-term scheduling, energy storage operation strategies from several hours to several days in the future are planned, so that the economic benefits of the system and the absorption capacity of renewable energy sources are maximized; and by establishing a space equation of the energy storage system and combining the predicted load, renewable energy power generation and market electricity price information, a multi-time-scale optimization framework is adopted to carry out joint scheduling. Experimental results show that the method can significantly improve the operation efficiency of the energy storage system, reduce the power grid load fluctuation, and reduce the energy storage cost and the power market risk at the same time.

According to the large-span steel structure refined construction method based on the simulation model, through whole-process refined simulation and optimization, the construction level of a large-span steel structure is remarkably improved, the construction scheme is determined based on a multi-objective optimization algorithm, accurate design of parameters such as hoisting, supporting and monitoring is achieved, a temperature field model is combined with actual measurement correction, and the construction efficiency is improved. The integration of optimization closure temperature, node semi-rigidity and initial defects enables simulation errors, support stability evaluation and optimization guarantee of safety coefficients, most construction risks can be predicted in advance on the whole, the field adjustment time is shortened, the formal construction period is shortened, the construction efficiency and safety are greatly improved, the obvious engineering value and economic benefits are achieved, and the method is suitable for popularization and application. The problem that an existing large-span steel structure construction method lacks a calculation model for predicting the risk in advance and shortening the construction period by combining multi-aspect data is solved.

The invention discloses an intelligent control system and control method for a flue gas environment-friendly island, belongs to the technical field of flue gas treatment, and solves the problem of how to improve the working condition adaptability, response speed and prediction precision of the environment-friendly island. According to the technical scheme, through intelligent integrated control over a plurality of subsystems in the denitration, dust removal and desulfurization processes, the working condition adaptability, the response speed and the prediction precision of the environment-friendly island are improved, on the premise that standard emission of pollutants is guaranteed, the comprehensive operation efficiency of all the process links is remarkably improved, energy consumption, material consumption and equipment loss are reduced, and the economic benefit is improved. According to the invention, an edge computing device can be used as an external communication module of a DCS, online robust operation is realized, the method is especially suitable for an environmental protection island SCR system in various flue gas treatment fields, safe, efficient and economical intelligent operation of the flue gas environmental protection island system can be realized on the premise of not changing original control logic, and the system has a wide application prospect. And the method has a wide popularization prospect and a remarkable industrial practical value.

The invention discloses application of an apple MdMYS1 gene in regulating and controlling the wax content of plant fruits and leaves, and belongs to the technical field of plant genetic engineering. According to the invention, the MdMYS1 gene with high expression quantity in apple varieties with more waxiness is separated, and the nucleotide sequence of the MdMYS1 gene is shown as SEQ ID NO. 1. Through subcellular localization, the transcription factor expressed by the MdMYS1 gene is found to be localized on a cell nucleus. Experimental results show that overexpression of the MdMYS1 gene can significantly increase the wax content of the apple fruits and leaves by promoting biosynthesis of wax, which indicates that the MdMYS1 gene plays a key role in regulation and control of the wax content of the apple fruits and leaves. The invention provides an efficient and rapid way for apple breeding, provides a gene material for improving apple quality, and has a wide application prospect in improving economic benefits and ecological benefits of apple planting.

The invention relates to a method for detecting a coal seam interface while drilling, relates to the technical field of geological exploration, overcomes the limitation of a single physical field by using multi-modal fusion of electromagnetic waves, sound waves and gamma rays, remarkably improves the detection precision, greatly reduces the identification error of a thin coal seam compared with a traditional method, and improves the identification success rate of a complex structural region (fault and dirt band). Preliminary response within 1s is realized through underground edge calculation, decision lag is compressed to 1.5 s through ground cooperative processing, and leap of data real-time performance and predictive capacity is realized; the front 0.5-1.5 m prediction function gains time for track adjustment, and the risk of penetrating out of the coal seam is reduced. The multi-sensor redundancy design still keeps the signal-to-noise ratio not less than 20dB in a strong noise environment, the detection success rate is greatly improved when mud invasion is serious, and the environmental adaptability is high. The coal seam drilling rate is increased, invalid footage is reduced, the single well comprehensive cost is reduced, and economic benefits are remarkably improved.

The invention provides a prediction method and system for quantity report, quotation and bidding by a building load aggregator, and the method comprises the steps: collecting bidding historical data, and carrying out the preprocessing of the bidding historical data; inputting the preprocessed bidding historical data as an input item into a pre-trained quantity and quotation behavior prediction model to obtain a bidding action probability distribution result of the competitor; and based on the bidding action probability distribution result of the competitor, in a preset bidding decision simulation environment, predicting an optimal quantity report quotation bidding strategy of our party as a building load aggregator, and outputting a final prediction result. According to the method, the market competition pattern can be dynamically predicted, and the optimal quantity and quotation strategy is generated, so that the bid winning rate and the economic benefit of the building load aggregator in the demand response real-time market are improved.

The application discloses a kind of unmanned vehicle accurate parking methods of port crane scene, through the setting of desired accurate parking position, adjust the distance and direction of vehicle, guide vehicle accurate parking, through the position that template map has saved desired accurate parking, and the plane parameter of crane, improve the accuracy of vehicle stop, can improve the efficiency of loading and unloading box operation, increase the economic benefit of single vehicle unit time, simultaneously, through laser radar, the point cloud data of crane can be directly scanned and obtained, realize the accurate parking of port crane scene, do not need to make any modification to crane equipment and install any equipment, there is no special requirement to crane structure, size etc., with good universality, the characteristics such as low introduction cost.

The invention belongs to the technical field of Tibetan medicinal material processing, and particularly relates to a Tibetan medicinal material vacuum freeze-drying process which comprises the following steps: feeding a loading tray into a freeze-drying bin, and cooling at a speed of 1-2 DEG C / min; when the center temperature of the material reaches-35 DEG C, maintaining for 2-4 hours; a vacuum system is started, and the pressure in the bin is reduced to 10-30 Pa; the initial temperature of the heating plate is set to be-10 DEG C to 10 DEG C; real-time monitoring is carried out through an array type sensor network; vacuum breaking operation; through accurate perception, intelligent diagnosis, robust decision making and self-adaptive regulation and control, the quality problems of collapse, coking, active ingredient degradation and the like of the medicinal materials in the drying process can be effectively prevented, and high quality and high uniformity of a final product are ensured. Meanwhile, by optimizing a drying path, unnecessary energy consumption and time waste are avoided, the economic benefit of the whole production process is improved, and the method is particularly suitable for modern processing of precious Tibetan medicinal materials with extremely high quality requirements.