190results about How to "Resolution time" patented technology

The invention discloses a network threat deduction system and method based on Transform and a graph attention network model. The system mainly comprises four modules including a threat intelligence extraction module, a network threat entity construction module, a network threat deduction module and a deduction result application module. The method comprises the following steps: preprocessing and mining network security threat intelligence data by utilizing a graph attention network (GAT) model, constructing a complete network threat intelligence knowledge graph based on a data association relationship, and determining a vulnerability utilization weight in the graph; modeling is carried out on a reasoned path sequence based on a Transform model, a next reasoning sequence is predicted in a generative mode, and rapid and efficient network threat intelligence reasoning is achieved. According to the method, the complexity of attack deduction calculation is reduced, the deduction efficiency is improved, and security risk investigation and early warning can be carried out for a large-scale network environment.

The invention relates to the technical field of metal materials, and provides a medium-and-high-entropy aluminum alloy performance prediction method, system and method and a medium, and the method comprises the following steps: obtaining a characteristic data set for medium-and-high-entropy aluminum alloy performance; constructing an MLP prediction model, and obtaining an optimal hyper-parameter combination of the MLP prediction model through ESOA; training the MLP prediction model according to the feature data set and the optimal hyper-parameter combination; and predicting the performance of the medium-high entropy aluminum alloy according to the trained MLP prediction model. According to the method, the advantages of the ESOA and the MLP prediction model are combined, the accuracy of performance prediction of the medium-high entropy aluminum alloy can be effectively improved, the time and experiment cost can be reduced, the prediction cost and the calculation amount can be reduced through the powerful calculation capacity of the MLP prediction model, the prediction precision and the generalization capacity of the model are enhanced, and the method is suitable for popularization and application. The method is suitable for performance prediction of the medium-high-entropy aluminum alloy material.

The invention provides a multi-degree-of-freedom mechanical arm obstacle avoidance path planning method and system, and relates to the technical field of robot obstacle avoidance. According to the method, forward kinematics is used for solving the transformation relation and three-dimensional coordinates of all joints of a mechanical arm, a working space of the mechanical arm is generated, modeling is conducted on an obstacle in the working space, and a corresponding obstacle space is generated; building an obstacle avoidance space, generating a preliminary global path, optimizing the preliminary path, calculating the movement speed, the acceleration and the joint limit moment of the mechanical arm, comprehensively generating a maximum obstacle avoidance angle, comparing the obstacle avoidance angle of each joint with the maximum obstacle avoidance angle, screening extreme joint angles, and counting the number of the extreme joint angles. And calculating a redundancy degree of freedom and setting a threshold value, and if the redundancy degree of freedom exceeds the threshold value, determining that obstacle avoidance succeeds and path planning is effective. According to the method, the obstacle avoidance space of the mechanical arm is determined, the path is optimized, and the redundant degree of freedom is managed and evaluated, so that effective obstacle avoidance of the mechanical arm in a complex environment is realized.

The invention provides a multi-unmanned aerial vehicle three-dimensional inspection method and system based on an improved simulated annealing algorithm, and the method comprises the steps: dispatching an unmanned aerial vehicle to collect point cloud data of a region needing inspection, processing the obtained point cloud data, and obtaining the three-dimensional coordinate data of an unmanned aerial vehicle inspection point; according to the intensive degree of the inspection data points and the number of the inspection unmanned aerial vehicles, adopting a K-means + + algorithm to allocate inspection tasks of the unmanned aerial vehicles; and after the routing inspection tasks of the unmanned aerial vehicles are distributed, according to the task routing inspection point three-dimensional coordinates of the unmanned aerial vehicles, an improved simulated annealing algorithm is adopted to optimize routing inspection tracks of all the unmanned aerial vehicles. According to the invention, after the point cloud data is acquired through the unmanned aerial vehicles to obtain the three-dimensional coordinates of the target point needing to be inspected, the inspection task area of each unmanned aerial vehicle is optimized through the clustering method, and then the inspection three-dimensional track of each unmanned aerial vehicle is optimized by adopting the improved simulated annealing algorithm, so that the total inspection time is minimized.

The invention provides an electrical equipment electric leakage identification method and identification device based on voiceprint signals. The method comprises the following steps: processing an acquired sound signal by using a variational mode decomposition algorithm to obtain a plurality of intrinsic mode functions, selecting three key intrinsic mode functions from the plurality of intrinsic mode functions to respectively serve as gray values of three color channels of red, green and blue of an image, and generating a voiceprint image of the sound signal; extracting texture features of the voiceprint image through a gray level co-occurrence matrix, and performing depth feature extraction and classification identification on the texture features by adopting a CNN-SVM model to obtain an electric leakage detection result; and when the electric leakage detection result shows that an electric leakage fault occurs, the position of a fault source is determined by calculating the time delay difference of the acoustic signals transmitted to the plurality of acoustic signal sensors. According to the scheme, the problem that in the prior art, accurate electric leakage fault source positioning cannot be provided, so that maintenance and fault removal time is prolonged is solved.

The invention discloses a data-driven calculation method, device and equipment for hydrogen storage capacity of a solid-state hydrogen storage device, and belongs to the technical field of solid-state hydrogen storage. The method comprises the steps that distributed parameters of the solid-state hydrogen storage device are obtained through a multi-physical-field full-coupling model of the solid-state hydrogen storage device, and the multi-physical-field full-coupling model is constructed based on structural characteristics of the solid-state hydrogen storage device; obtaining a dynamic response database of the multi-physics field full-coupling model, wherein the dynamic response database is used for representing a corresponding relation between distributed parameters of the multi-physics field full-coupling model and the hydrogen storage capacity of the solid hydrogen storage device; performing dynamic identification on the multi-physical field full-coupling model through a dynamic response database to obtain an identification model of the solid hydrogen storage device; and inputting the target distributed parameters of the solid-state hydrogen storage device into the identification model, and performing data driving calculation on the target distributed parameters through the identification model so as to output the predicted hydrogen storage amount of the solid-state hydrogen storage device. According to the method, the calculation cost and the solving time can be reduced.

The invention discloses a hospital inquiry system based on the Internet. The hospital inquiry system comprises a business platform layer, a multi-terminal application layer, a composite interaction layer and a verification layer, the service platform layer is constructed by adopting a Java Springboot and SpringCloud micro service architecture, and a plurality of core functions can be packaged into an independent service process; the multi-end application layer can select an optimal technology stack to form a multi-end application based on operation habits and performance requirements of different terminal users, and each end performs front-end and back-end separated interaction with the service middle station through an API gateway; and the composite interaction layer is used for establishing a communication link between a doctor and a patient so as to carry out a full-service process of online inquiry, prescription making and medicine distribution. According to the system, a one-stop business process of online inquiry, prescription extraction, medicine purchasing and distribution is realized, meanwhile, doctors are supported to receive diagnosis through a mobile terminal or a computer anytime and anywhere, and the problem that the licensing behaviors of the doctors are bound to fixed places and time of physical hospitals is solved.

The invention discloses a complex equipment global reliability analysis method based on a physical information neural network, and the method comprises the steps: segmenting a sample feasible region through employing a Voronoi region division method, calculating the region sensitivity through the model response in a training process, dividing a sensitive region, carrying out the resampling, dynamically adjusting the distribution of sample points in the training process of the physical information neural network, and carrying out the global reliability analysis of complex equipment. And iteratively updating sample points, constructing a network model, and efficiently predicting the failure probability of any time-space coordinate of the system by utilizing the system global response prediction capability of the physical information neural network and combining a Monte Carlo simulation method. The method can be used for calculating the global failure probability of complex equipment under the comprehensive action of various uncertainties, the problem that traditional reliability analysis depends on a large amount of simulation calculation to obtain experimental samples is solved, the calculation cost is effectively reduced, the limitation to fixed hotspots is overcome, the problem that potential failure areas change at any time and space is solved, and the reliability of the equipment is improved. The analysis efficiency is greatly improved; and the waste of computing resources is reduced.

The invention provides a risk assessment method and device for network security and a network security system, and the method comprises the steps: obtaining target log information, and determining a first risk coefficient according to the type of the target log information; determining a corresponding second risk value according to each monitoring parameter in the monitoring parameter group, and carrying out weighted calculation to obtain a second risk coefficient; performing cross validation on the target log information, the first risk coefficient, the monitoring parameter group and the second risk coefficient to obtain a third risk coefficient and a vulnerability risk list; performing static analysis and dynamic analysis according to the vulnerability risk list to obtain a fourth risk coefficient; inputting the vulnerability risk list into an auditing tool to obtain a fifth risk coefficient; and performing weighted calculation according to the first risk coefficient, the second risk coefficient, the third risk coefficient, the fourth risk coefficient and the fifth risk coefficient to obtain a risk assessment value. The method solves the problems that in the prior art, network security assessment needs workers to reach an assessment site, and manpower and time consumption is large.

The invention relates to a super-early-strength ultra-high-performance potassium magnesium phosphate cement-based composite material and a preparation method thereof.The super-early-strength ultra-high-performance potassium magnesium phosphate cement-based composite material (UHPMKPCC) takes dead burned magnesium oxide and monopotassium phosphate as cement raw materials, takes silica fume as one of cementing materials, and takes calcium carbonate as one of the cementing materials; wollastonite powder, metakaolin, fly ash, sodium dihydrogen phosphate and the like are used as auxiliary cementing materials, quartz sand is used as aggregate, and a composite retarder and a water reducing agent are adopted to jointly realize preparation and application of the UHPMKPCC with the ultra-low water-binder ratio. Compared with the existing ultra-high performance Portland cement-based material (UHPC) technology, the UHPMKPCC disclosed by the invention has the advantages of remarkable ultra-early strength mechanical property, low drying shrinkage and the like; compared with an existing ultrahigh-performance magnesium ammonium phosphate cement-based material technology, the UHPMKPCC has the advantages that the setting time can be effectively regulated and controlled, and no ammonia gas is released. The method can be applied to safe solidification treatment of radioactive nuclear waste in the fields of first-aid repair, rush building and reinforcement in the civil engineering field and safe treatment of nuclear waste.

The invention discloses a universal interpretable sequence recommendation method and system based on a multi-task self-prompt technology, and belongs to the technical field of sequence recommendation. A user interaction sequence is obtained, and a sequence recommendation module and a self-prompt generator are pre-trained based on the user interaction sequence; constructing a short input sequence based on the latest interaction of the user interaction sequence, inputting the short input sequence into a pre-trained sequence recommendation module to obtain an embedded representation of the short input sequence, inputting the embedded representation into a pre-trained self-prompt generator, extracting user features and generating personalized prompts of the user features; and on the basis of personalized prompts of user characteristics, alternately performing prompt fine adjustment on the pre-trained sequence recommendation module and the pre-trained explanation generation module, predicting a user recommendation result and generating natural language explanation. A flexible and efficient multi-task interpretable sequential recommendation framework ARTS is provided, accurate personalized recommendation is provided for recommendation results, and high-quality natural language interpretation is generated.

The invention relates to a multi-time scale optimization method and device for a multi-distributed resource virtual power plant, and the method comprises the steps: building a virtual energy storage model of an electric vehicle; based on the virtual energy storage model of the electric vehicle, constructing a day-ahead optimization model by taking the lowest electricity purchase cost as a target, and solving the day-ahead optimization model to obtain optimized charging and discharging scheduling of the electric vehicle and energy storage equipment and a switching plan of a reactive power compensation device; the method comprises the following steps: constructing an intra-day optimization model by taking the minimum wind and light curtailment as a target, taking charge and discharge scheduling of an electric vehicle and energy storage equipment and a switching plan of a reactive power compensation device as input of the intra-day optimization model, and solving the intra-day optimization model to obtain an intra-day reducible load and an optimization result of the energy storage equipment. According to the method, the problems of multi-energy integration and multi-time scale optimization in the virtual power plant can be solved.

The invention discloses a wetland fine mapping method based on remote sensing images and a deep learning super-resolution algorithm, which comprises the following steps of: firstly, acquiring high-resolution and low-resolution remote sensing images of a research area, pre-processing the high-resolution and low-resolution remote sensing images, and pairing high-resolution wetland samples with low-resolution images to obtain a high-resolution wetland data set; secondly, acquiring land coverage data and extracting wetland information to obtain a low-resolution wetland data set; then, inputting the low-resolution wetland data set and the high-resolution wetland data set into a convolution transformation super-division network reconstruction feature based on a convolution block attention module, and performing segmentation by using a U-Net architecture based on Scharr convolution and fast Fourier convolution; thirdly, for a wetland mapping task, designing a weighted loss function of three loss functions, and improving the wetland mapping precision from image details, time sequence and image consistency; finally, an optimal model is selected through evaluation and used for wetland fine drawing, so that the precision of wetland fine drawing is improved, and powerful support is provided for wetland research in various fields.

The invention discloses a leaderless group interview system and method based on large model multi-agent, and belongs to the technical field of artificial intelligence interview. The method comprises the following steps: generating interview questions by an enterprise human resource manager and a corresponding agent; scoring standard making, role description, role reference answer scheme generation and role cue word generation; after an interviewer receives the conference participation invitation and starts a leader-free group interview process, a preparation stage, an independent statement stage, a free discussion stage and a summary statement stage are performed in sequence, and a final interview record is generated; the enterprise human resource manager sends the interviewer name, the interview question, the final interview record and the scoring standard to the scoring agent, and checks and revises the interview score and evaluation output by the scoring agent, and finally the background management terminal automatically sends the interview score and evaluation to the interviewer. And completing leaderless group interview based on the large-model multi-agent.

The invention relates to a cable line partial discharge waveform classification method and device, and the method comprises the following steps: collecting the partial discharge waveform data of a cable line, extracting the fat degree of the waveform, the maximum slope of the waveform leading edge, and the maximum signal-to-noise ratio, and inputting the data into a pre-trained partial discharge waveform classification model, classifying the partial discharge waveform and the noise waveform to obtain a partial discharge waveform classification result; the training process of the partial discharge waveform classification model comprises the steps of obtaining training data, inputting the training data into an SVM classification model for training, performing parameter adjustment on the SVM classification model through a global optimal parameter algorithm model to obtain optimal parameters for training of the SVM classification model, and finally obtaining the trained SVM classification model as the partial discharge waveform classification model. Compared with the prior art, the method has the advantages that the training time of the whole partial discharge waveform classification model is greatly shortened, and meanwhile, the partial discharge waveform classification model is better in performance and higher in accuracy.

The invention discloses a grate cooler working condition prediction method combining intrinsic orthogonal decomposition and deep learning, and belongs to the technical field of digital twinning. The prediction method comprises the following steps: simulating a grate cooler flow field through a CFD technology to obtain a large amount of sample data, and combining an intrinsic orthogonal decomposition method with a multilayer back propagation neural network to predict the working condition of a grate cooler; therefore, rapid prediction of the flow field of the grate cooler is achieved, the problems of high cost and long time of CFD simulation calculation are solved, meanwhile, prediction data and actual sensor detection values are in butt joint through a coupling filtering correction algorithm, the problem that errors exist between simulation prediction values and field detection values is solved, and the prediction accuracy of the flow field of the grate cooler is improved. And actual deployment and guidance optimization production of the reduced order and digital twin model of the grate cooler in a factory are realized.

The present invention belongs to the technical field of coalbed methane development, and relates to a fracturing method for increasing coalbed methane production. The method comprises: determining a calculation expression for desorption efficiency according to the maximum adsorption amount of coalbed methane; determining the desorption heat required to achieve the desorption efficiency, and obtaining the maximum adsorption amount of coalbed methane; determining the temperature change according to the maximum adsorption amount of coalbed methane; determining the heat released by breaking the bond energy; preparing an exothermic gas-producing fracturing fluid according to the heat released by breaking the bond energy, adjusting the fracturing fluid formula and concentration according to the coal seam components, and adjusting the intensity and rate of gas production and heat release. The present invention can solve the problem that it is difficult to increase the single well production of coalbed methane and cannot maintain stable production for a long time due to the existing technology that mainly relies on the seepage field and stress field for fracturing, and improve the desorption rate and desorption amount of adsorbed gas, thereby increasing the coalbed methane production.

The invention provides a transformer fault feature fusion diagnosis method and system based on multi-modal time sequence data reconstruction, and relates to the technical field of power system transformer fault diagnos.The method comprises the steps that firstly, a gas-in-oil-dissolved time sequence waveform, a box wall vibration time sequence waveform and a sleeve heat map time sequence waveform are captured respectively; inputting a pre-constructed transformer operation situation reconstruction network to perform cross-modal physical field space-time alignment processing, generating a transformer multi-physical field evolution trajectory taking a fault energy injection point as an initial reference, and performing topology traceability tracking processing of fault feature clues on the transformer multi-physical field evolution trajectory to obtain a transformer global fault feature topology chain; and inputting a fault mode semantic analysis network, generating a transformer comprehensive diagnosis descriptor fusing a fault type semantic tag and a fault space positioning coordinate, extracting a local evolution trajectory fragment, generating a maintenance decision trigger instruction containing a fault residual life consumption rate, and sending the maintenance decision trigger instruction to a transformer on-line monitoring master station system. Safe and stable operation of the transformer is ensured.

The invention discloses a roller way on-line maintenance method and a roller way on-line maintenance device. A roller with a wear recess in a roller way rotates, the wear recess of the rotating roller is welded until the wear recess is filled with a filled solder, the roller does not need to be detached from the roller way, the wear recess can be welded in cooperation with rotation of the roller, and the wear recess can be welded in the wear recess. And the absolute value of the difference between the height of the filled solder and the depth of the wear recess conforms to the set threshold range, so that the solder filled in the wear recess almost has no height difference with the peripheral wall of the roller, and after the wear recess is filled with the solder, the roller of the roller way can be normally put into use. According to the roller way, the rollers of the roller way do not need to be disassembled, the time period of welding and roller maintenance is completed in the roller rotating process, the efficiency of roller maintenance completed through welding and filling is high, the maintenance time of the roller way is shortened, the maintenance efficiency can be effectively improved, and the technical problems that the maintenance time of the rollers is long and the maintenance efficiency is low are solved to a certain extent.

The invention belongs to the technical field of observable technology, and provides an electric power information system alarm traceability method and system based on redundancy processing and observation, after alarm information corresponding to a traceability object is determined, alarm types are matched according to the determined alarm information, and the alarm information is displayed on a graphical interface according to the matched alarm types; specifically, when graphical interface display is carried out, whether the alarm type is index alarm is judged, if yes, index graphical interface display is carried out, if not, whether the alarm type is link alarm is judged, if yes, link graphical interface display is carried out, and if not, log graphical interface display is carried out; according to the method, the type matching is performed according to the alarm source, and the graphical interface display object is automatically judged and determined according to different alarm types, so that the judgment efficiency is improved, the overall alarm traceability and the observability of fault information are realized, and the traceability accuracy is ensured.

The invention provides a method for synthesizing 6-aminocapronitrile from caprolactam. The method comprises the following steps: a gasification device is sequentially provided with a uniform distribution part, an efficient dispersion part, a high-temperature gasification part and an in-situ separation part from top to bottom; liquid-phase caprolactam is injected from the top end of the gasification device, and ammonia gas is injected through the efficient dispersion part; in the efficient dispersion part, liquid-phase caprolactam is broken in a first circular tube nest under the shearing action of gravity and ammonia gas to form liquid drops, and after high-temperature gasification, a gas phase is output from the side part of the gasification device, then enters the reactor filled with the monolithic catalyst from the bottom of the reactor, reacts through the monolithic catalyst and is output from the top of the reactor. According to the invention, caprolactam can be efficiently gasified, coking is not easy to generate, the retention time of a gas-phase reactant in the reactor can be accurately controlled, the reaction efficiency is greatly improved, the discharge amount of wastes in the production process of hexamethylenediamine is reduced, the cost is reduced, the economic benefit is improved, and a guarantee is provided for green, environment-friendly and safe production.

The application relates to the technical field of automobile manufacturing, and discloses a welding support device for automobile sheet metal parts, which comprises a layout base, a main hole position positioning support is arranged on the top of the layout base, a main clamping positioning support is arranged on the left side of the main hole position positioning support, and a clamping welding assembly is arranged on the left side of the main clamping positioning support. The bottom welding electrode is moved upward through the operation of the bottom telescopic rod, the bottom welding electrode is in contact with the main sheet metal, the top welding electrode is moved downward through the operation of the top telescopic rod, the top welding electrode is in contact with the auxiliary sheet metal, the top welding electrode and the bottom welding electrode are electrified, heat generated by the current at the contact point melts the main sheet metal and the auxiliary sheet metal, and the main sheet metal and the auxiliary sheet metal are solidified under the pressure of the top telescopic rod and the bottom telescopic rod to form welding points, at this time, the main sheet metal and the auxiliary sheet metal are preliminarily welded, and the problems that the existing support device cannot clamp the main sheet metal and the auxiliary sheet metal for welding immediately, and the mechanical hand carrying the spot welding device needs to consume time during movement and leads to low efficiency are solved.

The present invention discloses a method for determining the necessity of stratified water intake at a water intake of a water diversion and regulation project. The method comprises the following steps: step 1: collecting data on the parameters of the water diversion and regulation project and the environmental status of the water source area, the water diversion line area and the water receiving area; step 2: predicting the water temperature along the water diversion channel; step 3: predicting the water temperature of rivers and lakes in the water receiving area; and step 4: determining the acceptable degree of water temperature changes of ecologically sensitive targets of rivers and lakes in the water receiving area. The present invention has the advantage of being able to quickly identify the necessity of water temperature stratification.

The embodiment of the present application provides a kind of method and device for transmitting audio, comprising: determining the device type of target microphone in N+1 microphone, N is greater than or equal to 0;According to the device type, determine the target channel in the N+1 channel of protocol controller, each microphone in N+1 microphone includes protocol controller, each channel in N+1 channel corresponds to one microphone in N+1 microphone, and each channel is used to transmit the audio collected and played by the corresponding microphone;The target audio collected and played by the target microphone is transmitted through the target channel. The present application solves the problem of non-synchronous audio collection and play processing time.

The invention relates to a semiconductor sealing and testing area fast response scheduling method and system based on empirical learning, and the method comprises the steps: building a semiconductor sealing and testing area fast response scheduling system, transmitting bonding process lot data and machine table or unit data to a bonding process scheduling module through a semiconductor sealing and testing area data module; the scheduling module is used for scheduling a bonding process in a semiconductor sealing and testing area; estimating an initial scheduling scheme of the bonding process by using a machine learning method; decomposing the problems into lot scheduling sub-problems by using an adaptive overlap decomposition method; and solving the scheduling sub-problem by utilizing BAHA so as to obtain a complete bonding process scheduling scheme. And complementing a global scheduling scheme of semiconductor sealing and testing, and checking related operation data in a scheduling process by utilizing a GUI module. The problem that the delivery time penalty time is affected due to multiple constraints, various product types and a large number of scheduling units lot existing in semiconductor packaging test area scheduling is solved, the delivery time penalty of a semiconductor packaging test production workshop is reduced, and high-efficiency workshop scheduling control is achieved.

The invention belongs to the technical field of peanut processing, and relates to a method for extracting peanut meal protein by using a natural eutectic solvent. The mixture of choline chloride and glycerol is marked as CG, or the mixture of choline chloride and xylitol is marked as CX; stirring the CG or CX, and cooling to room temperature; adding water for dilution to obtain a natural deep-eutectic solvent CG or a natural deep-eutectic solvent CX; mixing the natural eutectic solvent and the peanut meal in proportion, heating and stirring, cooling to room temperature, and centrifuging; adding ethanol into the supernate for regeneration, and carrying out ethanol washing on centrifugal precipitation; and freezing and drying. The natural eutectic solvent method has higher extraction rate, and the extracted crude protein has higher purity. The method mainly overcomes the problems of long extraction time, environmental pollution and damage to the protein to a certain degree in the traditional extraction method of the peanut meal protein, and provides reference for realizing high-valued conversion of the peanut meal, reducing resource waste and promoting the application of the natural deep-eutectic solvent in the aspect of extraction of natural biological compounds.

The invention discloses a bank industry data governance system, and the system comprises a data extraction module which is used for extracting and storing business data; the data sampling module is used for sampling the extracted service data and asynchronously reporting the sampled data to the kafak; the governance rule configuration module is used for configuring a data governance rule and a data sampling rule; the index calculation module is used for acquiring the sampled data, verifying the data and writing the non-compliant data back into the relational database; the data query module is used for querying non-compliant data, acquiring permission setting and controlling a visible data range; and the notification module is used for issuing the data governance progress to the corresponding principal. According to the method, data autonomy of each team in the data production process is realized, additional data governance specialists are not needed, human input is reduced, and time and cost overhead caused by repeated analysis of total data is solved.

The invention discloses an optical fiber coloring ultraviolet curing monitoring and correcting system and method for a communication optical cable. The system comprises an optical fiber to be colored and cured; a mirror; a photoelectrochemical ultraviolet detection module; the wavelength detection module is used for realizing ultraviolet wavelength detection; the ultraviolet curing module is used for realizing 360-degree omnibearing curing of the optical fiber; the optical power calculation module is connected with the photoelectrochemical ultraviolet detection module and is used for processing the electric signal output by the photoelectrochemical ultraviolet detection module and giving out the output power of the ultraviolet light; and the signal feedback module is used for judging whether the power and the wavelength of the received ultraviolet light meet curing requirements or not and making corresponding feedback. According to the invention, the output power and wavelength of the ultraviolet light can be conveniently monitored, when the power of the ultraviolet light is insufficient, additional compensation of the ultraviolet light is added, correction can be carried out at the first time, and the waste of time, personnel and cost in the process of stopping production to adjust the power of the ultraviolet lamp of the host due to insufficient power is solved.

The invention discloses a control method for automatic alignment of an automobile compartment, relates to the technical field of new energy compartment detachable automobiles, and is applied to a compartment detachable automobile. The control method for automatic alignment of the automobile carriage comprises the steps that when an automobile backs up for alignment of an automobile frame and the carriage, whether the state of the automobile meets the automatic alignment condition or not is judged; if the vehicle state meets the automatic alignment condition, controlling the vehicle to reverse at a preset vehicle speed; determining the position deviation between the carriage bearing position and the carriage of the automobile; and according to the position deviation of the carriage bearing position and the carriage of the automobile, the steering angle of a steering wheel is controlled, and after the position deviation of the carriage bearing position and the carriage of the automobile reaches a preset alignment condition, the automobile is controlled to stop backing. Automatic control is adopted, the problems of long operation time and low accuracy of manual alignment of the chassis and the carriage bottom plate are solved, alignment of the vehicle chassis and the carriage is accurately controlled, accidents caused by unbalance loading of the vehicle are avoided, and the driving safety is improved; and the integration, automation and intelligence degrees are high.

The utility model discloses a ton bag tipping device which is used for tipping materials in a ton bag, the ton bag comprises a bag body, a plurality of lifting belts connected to the side wall of the top end of the bag body and a plurality of material pouring belts connected to the side wall of the bottom end of the bag body, the ton bag comprises a bottom plate, a containing assembly and a tipping assembly, the containing assembly comprises a containing cylinder, and the tipping assembly is arranged on the bottom plate. The containing barrel is connected to the bottom plate, an opening of the containing barrel is arranged away from the bottom plate, the tipping assembly comprises a support, a first driving part and a second driving part, the support and the containing barrel are arranged at an interval and can rotate relative to the bottom plate, and the first driving part is arranged relative to the opening of the containing barrel, connected to the support and used for hanging or releasing the lifting belt; and the second driving piece is arranged close to the support relative to the first driving piece and connected to the support, and the second driving piece is detachably connected with a material pouring belt and used for tipping the ton bag. According to the utility model, the problems of long operation time and low efficiency caused by the fact that two persons are required to cooperate for operation and manual picking is still required when a forklift is used for tipping the ton bag can be solved.