504results about How to "Achieve high efficiency" patented technology

The invention provides a die-casting die heat balance distribution optimization method and system based on an energy model, and relates to the technical field of die-casting dies, and the method comprises the steps: collecting heat distribution data, and building a heat energy transfer mapping relation; extracting space-time correlation features by using a graph attention network; constructing a heat distribution dynamic evolution model; constructing a temperature field predictor based on a deep belief network; training a predictor by using a multi-agent coevolution algorithm; constructing a temperature field optimization strategy network; generating an optimal temperature field control sequence; establishing a regional adaptive control strategy; constructing a temperature field controller based on deep reinforcement learning; and a dynamic fuzzy immune optimization algorithm is adopted to adjust cooling control parameters, and intelligent optimization control over heat distribution of the die-casting die is achieved.

The invention relates to the technical field of ore conveying, and discloses an intelligent mine ore conveying management system, which comprises a deep reinforcement learning scheduling module used for realizing optimal scheduling through a deep reinforcement learning algorithm based on an ore conveying equipment state, a conveying path condition and ore queue information; the digital twinning system is used for creating a digital twinning model of the conveying equipment and carrying out multi-physics field simulation, real-time data synchronization and state prediction so as to manage the full life cycle of the equipment; and the edge calculation module is used for carrying out data acquisition, real-time processing and lightweight AI reasoning on a mine site so as to realize equipment anomaly detection and early warning. By introducing deep reinforcement learning scheduling, digital twinning, edge computing and block chain data management technologies, adaptive optimization scheduling, accurate state monitoring and fault prediction of the ore conveying system are realized, an efficient safety protection and intelligent user interface is provided, and the operation efficiency, reliability and safety of the system are improved.

The invention relates to a cloud side-end integrated collaborative digital-intelligent traffic collaborative management and control method, system and device and a medium, and the method comprises the steps: dynamically adjusting the main line speed limit and ramp control rate through driving main line variable speed limit and traffic induction collaborative management and control based on a hierarchical control architecture, so as to enable the performance of a regional road network to be optimal; a collaborative optimization framework is constructed and utilized under a hierarchical control framework, an optimal control scheme of a single ramp is expanded to each partition of a road network in a random environment, and an optimal joint behavior strategy and a behavior selection strategy of each agent are determined, so that adaptive collaborative control of each agent is realized; and a traffic flow optimal equalization strategy of the whole road network is obtained. Therefore, by integrating a main line variable speed limit and traffic induction collaborative management and control technology, a main line and multi-ramp collaborative optimization framework and intelligent agent configuration, optimization of regional road network performance, optimal balance of overall road network traffic flow and improvement of traffic management and control accuracy and response speed are achieved.

The invention discloses a cloud game resource dynamic allocation and management system, and relates to the technical field of resource scheduling, and the system comprises a first prediction module which is used for obtaining user behavior data of a cloud game in a first time period, inputting the user behavior data into a preset resource prediction model, and obtaining a predicted resource utilization rate in a second time period; the second prediction module is used for acquiring the resource utilization rate and inputting the resource utilization rate into the demand value prediction model to obtain a current resource demand value; the environment analysis module is used for acquiring environment parameter data of the cloud game, inputting the environment parameter data into a pre-trained random forest model as input data so as to acquire error adjustment data, and correcting the resource demand value based on the error adjustment data; the resource allocation module is used for generating a resource allocation strategy of the cloud game according to the user behavior data, the resource demand value and the environment parameter data; a distribution execution module; executing a resource allocation plan according to the resource allocation strategy; and the allocation evaluation module is used for evaluating the resource allocation effect.

The invention provides a machine learning-based multi-dimensional auxiliary decision analysis method and system for power supply of a distribution network. The method comprises the following steps of: S1, acquiring historical data and historical electrical load data of the distribution network; s2, obtaining a power supply demand prediction model through the long and short term memory network and the historical data of the distribution network; s3, obtaining a load change model through a time sequence prediction algorithm and historical electrical load data; s4, identifying the real-time operation data of the distribution network based on the output of the steps S2 and S3 to obtain a power supply capacity change trend and a load margin; s5, establishing a comprehensive objective function based on the output in the step S4, performing optimization solution on the objective function through a genetic algorithm, and outputting an optimal power supply decision scheme; and S6, executing the output of the step S5, monitoring the power supply operation parameters of the distribution network in real time, and dynamically adjusting the optimal power supply decision scheme based on the power supply operation parameters of the distribution network. According to the invention, the reliability of distribution network power supply is improved, and the dynamic adjustment and optimization of the distribution network power supply decision scheme are realized.

The invention relates to the technical field of sewage discharge treatment, and discloses a sewage low-carbon treatment system based on electrochemistry and biochar, which comprises a deep treatment filter tank, an electrochemical nitrogen and phosphorus removal device, a biochar adsorption and filtration module, an intelligent control and operation and maintenance system, a slow-release agent regulation and control module and a low-carbon and resource recycling system, the advanced treatment filter tank is used for filtering sewage impurities and particles, adsorbing and removing organic matters, reducing the content of nitrogen and phosphorus nutrient substances and adapting to the change of water quality and water quantity; the electrochemical nitrogen and phosphorus removal device is used for carrying out electrochemical reaction to remove nitrate, phosphate and fluorine ions. According to the invention, the electrochemical active reaction is combined with charcoal adsorption filtration, so that the technology greatly improves the sewage treatment efficiency, especially shows a higher removal rate when treating complex pollutants, can meet higher water quality standard requirements, and effectively overcomes the difficulty that the traditional treatment technology is difficult to remove multi-element pollutants.

The invention relates to the technical field of disaster prediction and early warning, in particular to a rural water supply project drought disaster prediction and early warning method and system. The method comprises the following steps: acquiring a hydrological data set; performing data preprocessing on the hydrological data set to generate a hydrological core processing data set; performing data feature extraction on the hydrological core processing data set to generate a hydrological feature data set; quantifying the hydrological characteristic data set to generate a multi-water-source drought early warning threshold calculation algorithm; constructing a multi-dimensional interaction model by using a multi-water-source drought early warning threshold calculation algorithm in combination with the hydrological characteristic data set, and generating a multi-source hydrological drought early warning and prediction model; therefore, by optimizing hydrological data processing and early warning threshold calculation, the problem of inaccurate prediction caused by inconsistent multi-source data in a traditional early warning system is solved, and the accuracy and reliability of early warning of drought disasters are greatly improved.

The invention relates to the technical field of digital printing machines, in particular to an intelligent ink droplet control method and system of a digital printing machine and the digital printing machine. The method comprises the steps of real-time data acquisition, multi-dimensional parameter modeling, real-time ink droplet behavior prediction, self-adaptive ink jet parameter adjustment, closed-loop feedback control, medium characteristic self-adaptive matching, intelligent multi-region optimization and environmental factor compensation. Ink droplet behaviors and environmental data are collected in real time through a high-speed camera and various sensors, a nonlinear model is constructed by using a deep learning technology, and a dynamic parameter optimization curve is generated. According to the method, the printing quality and efficiency are remarkably improved, and the method is suitable for various media and complex scenes.

The invention discloses a liquid fertilizer application effect analysis method based on self-supervised learning. The method comprises the following steps: S1, generating initial feature data; s2, environment feature representation is generated based on a heterogeneous environment self-adaption module; s3, extracting short-term and long-term spatio-temporal characteristics of the liquid fertilizer application effect based on the dual spatio-temporal characteristic decoupling and dynamic fusion model, and generating comprehensive spatio-temporal characteristic representation; s4, generating fertilization characteristics in different situations by utilizing self-supervised learning, and eliminating environmental interference factors through a causal reasoning module; s5, generating a fertilization intention feature, and dynamically adjusting a fertilization strategy; s6, when the difference between the prediction result and the actual effect exceeds a threshold value, triggering a re-learning module to update model parameters; and S7, carrying out integrated optimization, realizing feature sharing and task coordination through multi-task collaborative learning, and carrying out automatic tuning by utilizing Bayesian optimization. The intelligent analysis of the liquid fertilizer application effect is realized through the self-supervised learning and multi-task collaborative optimization method.

The invention relates to a method for extracting vanadium from a vanadium slag roasting product and preparing vanadium oxide or vanadyl sulfate, which comprises the following steps of: crushing the vanadium slag roasting product, grinding, carrying out synchronous reduction leaching in a leaching medium, regulating the pH value of a leaching solution to 0-5 by using a pH regulator, and carrying out solid-liquid separation on the leaching medium and the roasting product according to the liquid-solid ratio of (3-20): 1, so as to obtain the vanadium oxide or vanadyl sulfate. And leaching liquid containing positive divalent vanadium oxygen ions is obtained. According to the method, when the vanadium slag roasting product is leached, the reducing agent is added to directly control the valence state of vanadium in the leaching solution, tetravalent vanadium with the stable valence state is obtained, the problem that the vanadium leaching efficiency is low due to the fact that pentavalent vanadium ions are prone to hydrolysis is solved, vanadium oxyhydroxide is prepared in combination with a selective precipitation method, VO2 or V2O5 is prepared through calcination under different atmospheres, and the method is suitable for industrial production. Or in-situ extraction-reverse extraction is carried out in the reduction leachate to prepare vanadyl sulfate. The method is short in process, high in vanadium yield, compact in process linkage, less in three wastes and easy to industrialize.

The invention provides a ginseng age limit identification and detection method and system based on laser-induced breakdown spectroscopy. The ginseng age limit identification and detection method comprises the following steps: collecting ginseng of different age limits as samples; the method comprises the following steps: establishing original spectral data of a sample by utilizing LIBS (Laser-induced Breakdown Spectroscopy), and performing data set division and normalization preprocessing; carrying out feature selection on the preprocessed spectral data by adopting a genetic algorithm GA; processing the feature selection data by adopting different classification algorithms and establishing classification algorithm models; and aiming at the age limit prediction of each classification algorithm model, establishing an integrated classification model based on a voting method, and obtaining an age limit category with the highest probability as a final ginseng age limit prediction result. The method has the advantages of rapidness, no damage, high precision and the like, and can be widely applied to the fields of ginseng quality identification, market supervision and the like.

The invention relates to the technical field of printing and silk-screen identification, and particularly discloses an OCR (Optical Character Recognition) method and system for PCB (Printed Circuit Board) component printing and silk-screen. PCB images under visible light, near-infrared and ultraviolet light wavebands are acquired by utilizing a multispectral imaging technology, the visibility of characters is improved, and the interference of a high-reflection or shadow region is inhibited; the quality coefficient of the image is obtained by calculating the structural similarity of the image on different scales, and the quality coefficient is compared with a preset threshold value, so that a high-quality image is automatically screened out; the method comprises the following steps of: comprehensively analyzing each region by extracting contrast, brightness and sharpness data of a high-quality image, and determining and marking effective character regions; a dynamic time warping algorithm is adopted to calculate a deviation coefficient between an actual silk-screen characteristic and a design file characteristic, and whether a printing error exists in silk-screen or not is accurately recognized by comparing the deviation coefficient with a preset threshold value.

The invention relates to the technical field of sewage treatment, and discloses an advanced treatment method and device for ship oily wastewater, and the method comprises the following steps: carrying out preliminary filtration on the ship oily wastewater to remove suspended solids and particulate matters so as to obtain first oily wastewater; the first oily wastewater is subjected to coalescence separation treatment, floating oil, dispersed oil and a small part of emulsified oil in the wastewater are removed, the oil content in the wastewater is reduced to 500 ppm or below, and second oily wastewater is obtained; the second oily wastewater is subjected to cross-flow ultrafiltration deep treatment, emulsified oil and dissolved oil are intercepted, produced water and concentrated water are obtained, and the concentrated water serves as third oily wastewater; the third oil-containing wastewater flows back to the first oil-containing wastewater, and discharged water is obtained after the produced water is detected to be qualified. The invention provides a primary filtration, coalescence separation and ultrafiltration three-stage circulating filtration method, emulsified oil and dissolved oil can be efficiently separated and removed, oil resource recovery is realized, and membrane pollution is reduced.

The invention relates to the technical field of photovoltaic power station inspection. The invention provides a photovoltaic inspection management method and system based on an unmanned aerial vehicle, and the method comprises the steps: constructing an inspection management database based on historical data; respectively constructing an inspection planning model and a data analysis and evaluation model based on the inspection management database; planning a flight path and an endurance strategy through the inspection planning model, and dynamically optimizing the flight path and the endurance strategy through the inspection planning model according to the real-time data; an inspection task is executed according to the dynamically optimized flight path and the endurance strategy, related data of the photovoltaic power station in the inspection process are collected in real time, and the related data are returned in real time; related data returned in real time is analyzed and evaluated through the data analysis and evaluation model, and corresponding operation and maintenance measures are formulated according to evaluation results, so that the potential problems that an existing inspection mode depends on manual inspection, the efficiency is low, each corner of the photovoltaic power station is difficult to comprehensively cover, and missing is likely to occur are solved.

The invention discloses a hysteroscope image color feature transformation method based on deep learning, particularly relates to the field of medical image processing, solves the problem of insufficient analysis accuracy caused by color feature redundancy in a hysteroscope image, and ensures high-quality image data through multi-level accurate screening. Extracting multi-dimensional color features by using nonlinear mapping and structural analysis, and enhancing the expression and discrimination of the features; the constructed multi-level index system is combined with spectral analysis and graph theory operation, and the macroscopic distribution difference and the microcosmic robustness of the features are accurately measured; the method comprises the following steps: successfully extracting a key candidate dimension subset with high discrimination and robustness in a complex scene through a Pareto-like leading edge search and topological data analysis technology, multi-round iteration and graph theory clustering screening; therefore, the problems of color feature redundancy, insufficient stability and poor adaptability are effectively solved, and the accuracy and reliability of hysteroscope image analysis are remarkably improved.

The invention discloses a sinking type city global governance comprehensive management and control method and platform, and relates to the technical field of city management, and the method comprises the steps: deploying intelligent sensing equipment at a key position of a target city, and capturing multi-dimensional city operation data in real time; the interactive social media platform is used for acquiring Internet feedback data of target city residents; carrying out cross-domain data integration on the obtained data, carrying out spatio-temporal data analysis, obtaining a city operation change rule, carrying out simulation decision simulation, and generating a regional governance decision; in the execution process, treatment effect feedback is collected, and strategy adjustment and algorithm model optimization are carried out. The technical problems of lack of effective information sharing and collaboration among departments, lagging of dynamic change response of the city and low timeliness and accuracy in a traditional city governance mode in the prior art are solved, and the technical effects of precision and high efficiency of city governance are achieved through sunken global governance comprehensive management and control.

The invention provides a high-efficiency and environment-friendly sandstone material processing control system, and relates to the technical field of sandstone material processing control, and the system comprises a production demand determination module, a blank raw material determination module, a processing workshop control module, a processing workshop fault evaluation module, an environment-friendly monitoring processing module and a sandstone aggregate online detection module. According to the invention, intelligent control of gravel production is realized, physical online detection of finished aggregate detects particle size and shape of the finished aggregate, chemical online detection of harmful substances chloride and methylene blue in the finished aggregate is realized, the detection efficiency is improved, the labor intensity of testers is reduced, and the production cost is reduced. Online collection, real-time detection, remote analysis and dynamic feedback of the finished aggregate are realized, and the quality control level of the finished aggregate is improved.

The utility model discloses a moxibustion fumigation traditional Chinese medicine external treatment device which comprises a display control bin, a radio frequency induction bin and an independently-installed chip medicine cake box which are connected with one another. A radio frequency chip, a radio frequency coil and a temperature-resistant protective cover are installed in the radio frequency induction bin, a heat transfer through hole and a heater are arranged at the bin bottom, and an independent chip medicine cake box is installed at the lower end of the outer bottom. According to the device, information of the chip medicine cake box is read through a radio frequency chip and a coil of the radio frequency induction bin, and parameters such as optimal working time, working temperature and working wavelength of different traditional Chinese medicine external treatment medicines or non-medicine materials such as moxibustion or fumigation are matched; convenience, digitization, intelligentization, safety and standardization of moxibustion fumigation and other hundreds of traditional Chinese medicine external treatment methods can be achieved.

According to the electric meter intelligent dynamic complementary reading method and device based on big data analysis, the method comprises the steps that historical data are analyzed, the reading data items and the reading priority of an electric meter are intelligently adjusted, task data sets needing complementary reading are combined into a composite task according to requirements, automatic execution is executed in the non-busy time period, and the intelligent dynamic complementary reading efficiency of the electric meter is improved. And the data acquisition efficiency and accuracy are improved. A master station system is used for issuing a configuration scheme and a task to terminal equipment, data items of different electricity meters are sorted, and deep mining and analysis are carried out based on collected data within a past set time (flexible adjustment can be carried out according to specific field requirements); and a set of personalized complementary reading configuration strategy suitable for various ammeters is deeply combed and constructed, so that intelligent dynamic complementary reading is realized. The measure not only effectively guarantees the basic data support of big data processing, but also assists a decision application level to obtain more accurate and effective results.

The invention discloses a digital twin model evolution method and system based on constructional engineering. The invention relates to the technical field of constructional engineering. Related data of the long-distance water conveyance project are collected, wherein the related data comprise BIM model data, oblique photography three-dimensional model data and GIS data; meanwhile, the data are preprocessed, including format conversion, coordinate unification and denoising, so that the quality and consistency of the data are ensured. A three-dimensional BIM model of the engineering facility is constructed based on Autodesk Revit according to a design drawing and collected data, the facility and equipment in the BIM model are coded according to a KKS coding rule, and a coding database is updated; according to the method, the BIM model data, the oblique photography three-dimensional model data, the GIS data and other multi-source data are fused, a refined three-dimensional scene model is constructed, and comprehensive integration of engineering facility information is achieved. Therefore, data sharing and collaboration among different departments and professions are facilitated, and the overall working efficiency is improved.

The invention provides a preparation method of a Cu-based catalyst for acetalation reaction, which comprises the following steps: depositing Pt on the surface of a pretreated carbon carrier through an atomic layer deposition method, depositing a metal oxide CuOx on the surface of the metal Pt, and performing high-temperature reduction and medium-temperature oxidation treatment to obtain the Cu-based catalyst. According to the method, the size of Pt particles and the thickness of CuOx can be accurately controlled on the atomic scale. The catalyst is applied to the acetalation reaction of aldol, the size of Pt particles and the thickness of CuOx can be accurately controlled on the atomic scale in the hydrogen atmosphere, the Pt-coated CuOx core-shell structure catalyst with cuprous as the main component is obtained on the surface of the carbon carrier, and the catalyst has the catalytic performance of high acetal yield due to the stable cuprous structure. The preparation of the catalyst is simple and controllable, and the greenization of the aldol acetalation reaction process and the high efficiency of the product are realized through the catalyst.

The invention discloses an AI processor scheduling method, device and equipment based on a job scheduling system and a storage medium, and relates to the technical field of computer science, and the method comprises the steps: collecting and preprocessing historical data, and defining a state space of a scheduler based on the historical data; according to the state space of the scheduler, defining an action space of the scheduler and designing a reward function; constructing a resource scheduling model based on the action space and the reward function, and identifying an optimal scheduling strategy; in the process of executing the optimal scheduling strategy, continuously collecting new data to update the resource scheduling model, and sorting task priorities in the resource scheduling process; and based on the sorting result and the optimal scheduling strategy, the scheduler performs resource scheduling on the tasks in sequence. According to the invention, the action space and the comprehensive reward function are defined to realize fine scheduling control; a resource scheduling model is constructed, an optimal strategy is identified, and system adaptability is enhanced; the model is continuously updated, the task priority is adjusted, and the resource utilization efficiency is improved.

The invention discloses an optimization adjustment method for a ground source heat pump system, which comprises the following steps of: acquiring historical data and operation data of the ground source heat pump system, calculating historical released heat according to the historical data, performing optimization judgment according to pump side data and environment indexes, calculating heat association degree, and performing optimization adjustment on the ground source heat pump system according to the heat association degree. Calculating a use degree coefficient according to the user class indexes, determining liquid flow efficiency, first demand power and second demand power, constructing a ground source heat pump system target optimization function according to the energy consumption class indexes, constructing a ground source heat pump system optimization adjustment model, and determining a ground source heat pump system optimization adjustment result. And inputting operation data of a to-be-optimized ground source heat pump system into the ground source heat pump system optimization adjustment model to obtain required power and required liquid flow, and performing optimization adjustment according to the required power and the required liquid flow. According to the method, the precision and efficiency of optimization adjustment of the ground source heat pump system can be improved, meanwhile, good interpretability is achieved, and the method has important significance on operation cost reduction, energy conservation and emission reduction.

The invention provides a deep learning-driven data processing and compression method and system, and belongs to the technical field of cloud computing, and the deep learning-driven data processing and compression method and system comprises a data acquisition and preprocessing module which is responsible for obtaining original data from different sources, including images, videos, audios or sensor data and the like; the data is subjected to preliminary cleaning, format conversion and standardization processing, it is ensured that the data quality meets subsequent processing requirements, the system can dynamically adjust a compression strategy and model parameters according to real-time changes of data content through the optimization and self-adaptive control module, it is ensured that the optimal compression effect and the resource utilization rate can be achieved in different scenes, and the system reliability is improved. The module also realizes model compression and optimization technologies, such as quantification, network pruning and knowledge distillation, which effectively reduce the memory usage and calculation cost of the model, and further improve the efficiency of data processing and compression.

The invention discloses a method for improving the grain size and component uniformity of a solidification structure in the smelting process of a titanium alloy EB furnace through ultrasonic treatment, and belongs to the technical field of titanium alloy smelting. In order to solve the problems of non-uniform cast ingot components, serious grain boundary segregation, coarse structure grains and the like in the existing EB furnace smelting process, the invention provides a method for improving the solidification structure grain size and component uniformity in the titanium alloy EB furnace smelting process through ultrasonic treatment, which comprises the following steps: high-temperature titanium alloy melt flows into a crystallizer for solidification and ingot dummy; after ingot dummy is started, an ultrasonic radiation rod is inserted into a titanium alloy melt molten pool on the upper portion of the crystallizer; and when the cast ingot is close to a solidification end point, removing the ultrasonic radiation rod to obtain the titanium alloy cast ingot. According to the method, the ultrasonic radiation rod is inserted, the insertion depth and the ultrasonic frequency are controlled, and ultrasonic vibration and cavitation are utilized, so that grains of a titanium alloy solidification structure are refined, alloy components are uniform, the machining performance of a titanium cast ingot is improved, and efficient production of the high-quality titanium alloy is achieved.

The invention belongs to the technical field of part stamping, and particularly relates to new energy automobile part stamping forming equipment and a forming method thereof.The new energy automobile part stamping forming equipment comprises a forming part, a conveying mechanism is arranged at the upper end of the forming part, a feeding mechanism is arranged on one side of the forming part, and a die changing mechanism is arranged on one side of the forming part; a stamping die is arranged above the forming part, and materials to be formed are placed at the upper end of the conveying mechanism. According to the stamping forming equipment, automation, precision and high efficiency of the stamping forming process of the new energy automobile parts are achieved, the measures act together, the production efficiency is remarkably improved, the labor cost is reduced, the error rate in the production process is reduced, and the stability and consistency of the product quality are ensured.

The invention relates to the technical field of machining equipment, in particular to a semi-automatic adjustable three-roller plate rolling machine which comprises a supporting assembly, a material pressing assembly, a material pushing assembly and a connecting assembly, the supporting assembly comprises a supporting frame, and the material pressing assembly comprises two fixing bases, an upper roller, two lower rollers and a driving part; the two fixing seats are connected to the two opposite sides of the supporting frame respectively, the two ends of the upper roller are rotationally connected to the two fixing seats respectively, the heights of the axes of the two lower rollers are flush, a material rolling gap is reserved between the upper roller and the lower rollers, and the driving part drives the lower rollers to rotate. The pushing assembly is used for pushing the cylinder to move, the connecting assembly comprises a closing piece, an arc groove allowing the cylinder to slide through is formed in the top of one fixing base, the fixing base is divided into a fixing part and a connecting part, the closing piece comprises a closing rod, one end of the closing rod is rotationally connected to the fixing part, the other end of the closing rod is used for supporting the connecting part, and the pushing assembly drives the closing rod to rotate; the device has the effect that the problem that personnel cannot conveniently take out the cylinder is solved.

The invention discloses a measurement and control system and method for accurate receiving of long-distance pipe jacking pipe joints. The measurement and control system comprises an information acquisition module, an information transmission module, a data storage module, a data analysis module, an alarm module, a pipe jacking machine deviation correction module, an intelligent decision module and a relay control module. The measurement and control method comprises the steps of position and posture monitoring of the pipe jacking machine, position deviation analysis, posture deviation analysis, intelligent control over position and posture deviation correction of the pipe jacking machine, measurement and control received by the pipe jacking machine and determination of the relay chamber compensation length. Real-time position and posture data of the pipe jacking machine are monitored and analyzed through an Internet of Things sensor and a neural network technology, and intelligentization, real-time performance and high efficiency of position deviation value and posture deviation value monitoring and control of the pipe jacking machine are achieved. And by calculating the number of the remaining in-hole pipe joints and the compensation length of each intermediate jacking station, it is ensured that the front end face of the first jacking pipe joint is located at the receiving well portal design position, and the rear end face of the tail jacking pipe joint is located at the starting well portal design position.

The invention relates to the field of disassembly of packing belts of packing boxes, and particularly discloses an automatic recycling and sectioning device and method for a box packing belt, the automatic recycling and sectioning device comprises a recycling and feeding mechanism, a recycling and cutting mechanism, a tail end discharging mechanism, a power mechanism and an auxiliary device, and the recycling and feeding mechanism comprises a pressing air cylinder, an upper roller assembly and a lower roller; the recycling and shearing mechanism comprises a belt cutting air cylinder cutter set. The tail end discharging mechanism comprises a pressing belt wheel air cylinder, an upper pressing belt wheel and a lower pressing belt wheel. The power mechanism comprises a motor; the auxiliary device comprises a position sensor, a rack and a synchronous belt. According to the recycling and sectioning method, the two ends of the packing belt are tensioned, and then the upper blade and the lower blade are used for shearing. By the adoption of the packing belt recycling device, packing belts with different thicknesses can be recycled according to whether the packing belts are bent or not, the packing belts can be prevented from remaining in equipment through an internal mechanical structure, efficient operation of the mechanism is guaranteed, and automation and high efficiency of packing belt recycling are achieved.

The invention relates to the technical field of unmanned aerial vehicles, and discloses a method for regulating and controlling an unmanned aerial vehicle through positioning coordinates, which comprises the steps of evaluating the load capacity of the unmanned aerial vehicle and matching logistics tasks; planning an initial path according to the initial site and the target position of the unmanned aerial vehicle, and performing optimization by combining historical path data to obtain an optimal path; when approaching the target, judging the landing standard; if not, selecting a new landing point; and if yes, judging whether to accurately adjust the residual route, and adjusting according to the real-time information. According to the method, the safety and the stability of the unmanned aerial vehicle in the flight process can be guaranteed, the most suitable unmanned aerial vehicle can be automatically selected for distribution according to specific logistics task requirements, and the efficiency and the accuracy of logistics transportation are greatly improved.