219results about How to "Save time resources" patented technology

The invention discloses a small-scale greenhouse gas emission prediction method based on a pre-training model, and belongs to the field of greenhouse gas emission prediction. According to the method, a greenhouse gas monitoring micro station is arranged in a park, data such as greenhouse gas concentration and meteorological factors are collected in real time, and high-precision traceability of a greenhouse gas emission source and accurate accounting of greenhouse gas emission can be achieved in combination with a high-precision meteorological numerical value mode, a backward trajectory tracking algorithm, a receptor model and a diffusion model; the greenhouse gas emission amount is predicted in real time by calling the pre-training model, and the prediction efficiency and accuracy are remarkably improved. Different from a mode that a traditional model depends on a large amount of historical data training, the pre-training model can be finely adjusted on the basis of a small amount of existing data, so that effective learning and prediction are realized. According to the method, a large amount of time and computing resources are saved, the environmental change can be quickly responded, and real-time and accurate emission prediction is provided.

The invention discloses a bearing vibration signal processing model construction method, a model and a signal classification method, which are used for constructing time domain and frequency domain loss functions by calculating a plurality of feature vector distances so as to train an encoder. According to the method, by minimizing the difference value of different distances in the time domain and frequency domain loss functions, an encoder can be guided to extract similar feature representations in signals, so that the similar feature representations are gathered in a feature space, and dissimilar features are separated. When a trained encoder (a convolutional neural network with an attention mechanism) processes a vibration signal of an unknown fault type, some feature representations in the signal are different from those of other signals, so that the feature representations are gathered in a feature space and are far away from the feature representations of other signals, and the fault detection accuracy is improved. Therefore, the recognition of the vibration signal of the unknown fault type is effectively realized.

The invention discloses a network information intelligent analysis method and system based on a large model, and the method comprises the steps: firstly carrying out the preprocessing of network information, constructing a prompt word template through a multi-step thinking method, and processing the network information; calling the large model to obtain a network information analysis result presented by JSON format data; performing format judgment on the analysis result by using a feedback optimization method, and if the format requirement is not met, repeatedly calling the large model, and updating the network information analysis result; and performing JSON format matching on the analysis result of which the output format of the large model still does not meet the requirement to obtain an analysis result meeting the JSON template. According to the method, emotion analysis can be more accurately carried out on the network information, and the efficiency and accuracy of analysis work are remarkably improved.

The invention relates to a laser cutting-off technology, in particular to a laser bonding wire cutting-off device for chip detection, which comprises a workbench and the like, a fixed block is arranged above the workbench, and the fixed block moves through a first linear module mounted on the top surface of the workbench; a laser cutting device used for cutting off a welding wire is installed on the fixing block, and a datum plate is fixedly connected to the top face of the fixing block. The laser cutting device is provided with wiping blocks and other parts, the vertical positions of the wiping blocks are adjusted through the adjusting part, so that one wiping block can make contact with the bottom face of the lens of the laser cutting device, and when tiny impurities or dust on the lens of the laser cutting device needs to be wiped, the second linear module is started; the second linear module drives the moving block to move, so that one wiping block can wipe the lens of the laser cutting device, wiping can be completed in a laser cutting gap, manual wiping is not needed after each time of use, and time and manpower resources are saved.

The invention belongs to the field of safe transmission of video monitoring code streams, and particularly relates to a method for realizing disaster recovery and rapid recovery of national standard superior services by using Redis record cascade and equipment information. A Redis storage management system is constructed to record cascading and equipment information, and when a service is restarted abnormally, link recovery or waiting for registration online is quickly judged according to Redis information. Multi-thread storage is adopted for first retrieval of shared device information, Redis and reported information at this time are compared during second retrieval, a lower level synchronously retrieves resource information from an upper level, a database and Redis are accurately operated according to information content by means of a Kafka component, all the steps work cooperatively, and the efficiency is improved. Disaster recovery and rapid recovery are effectively achieved when existing national standard cascade services face various emergencies such as service faults and network anomalies, the overall performance, the disaster recovery capacity and the data accuracy are improved, and the requirements of a video monitoring system for efficient, stable and reliable operation and accurate resource sharing are met.

The invention relates to the technical field of computers, and discloses a performance parameter tuning sequence determination method and device, equipment and a medium, and the method comprises the steps: carrying out the sampling of the performance parameters of a deep learning model, and obtaining a plurality of sampling point data; generating a configuration file based on the sampling point data; after the operation in the test environment is finished each time, obtaining a test result; generating a first-order sensitive index and a total sensitive index corresponding to each performance parameter based on a performance index generated by the performance parameters; and determining a tuning sequence of the performance parameters based on the first-order sensitive index corresponding to each performance parameter and the total sensitive index. Through sensitivity analysis of a scientific system, key parameters having significant influence on the reasoning performance of the deep learning model are accurately identified and determined. And the tuning sequence of the performance parameters is determined, so that the tuning process is more efficient. Parameter adjustment is carried out according to the tuning sequence, blind trying of different parameter combinations can be avoided, and tuning time and computing resources are saved.

The invention provides a process abnormity early warning method and system in a process industry process, and belongs to the technical field of process abnormity early warning, and the method comprises the steps of data acquisition, modeling, model training, incremental learning, third prediction, difference value calculation, judgment, alarm and the like. The system comprises a data acquisition module, a modeling module, a model training module, an incremental learning module, a third prediction module, a difference value calculation module, a judgment module and an alarm module. According to the method, from the perspective of process related parameters, changes of various factors are adapted through the incremental learning algorithm, that is, new knowledge can be learned based on new data, and accurate prediction of process abnormal phenomena is realized.

The invention discloses a simulated medical picture visualization system and method based on an MR technology, and relates to the field of medical simulation teaching, and the system comprises a medical image data acquisition module, a medical image data processing module, a simulated medical picture dynamic adjustment module and an equipment real-time monitoring module. According to the method, the quality of the medical image of the pre-visualized medical image is screened, the quality of the medical image for mixed reality simulation is ensured, the sense of reality of a mixed reality scene is improved, and the virtual environment is more changeable and flexible by monitoring the real environment light parameters in real time and dynamically adjusting the virtual environment light parameters. By monitoring the real-time transmission parameters of the MR equipment, the stability and instantaneity of the user in the using process are ensured.

The invention relates to the technical field of artificial intelligence, and provides a fault recovery method and device, electronic equipment and a storage medium, and the method comprises the steps: stopping executing a local training process corresponding to a training subtask under the condition that the training process is detected to be abnormal or a remote node fault notification is received, checking the health state of the local hardware to obtain a checking result; and performing dynamic networking based on the check result, and restarting the local training process after the networking is successful to recover the execution of the training sub-task. According to the method, the fault discovery time can be shortened and the fault recovery efficiency can be improved by introducing the remote node fault sensing capability, and potential hardware problems can be identified before the training process is restarted by checking the health state of local hardware, so that the occurrence of repeated faults is avoided.

The invention discloses a pavement pit slot crack three-dimensional model establishment method based on point cloud data, and belongs to the technical field of engineering measurement and pavement detection, and the method comprises the following steps: S1, data acquisition and processing; s2, preprocessing the original point cloud data; s3, identifying the point cloud of the crack of the pit slot; and S4, establishing a three-dimensional model of the pit slot crack. According to the method, three-dimensional modeling is carried out by using the point cloud data, pits and cracks on the road surface can be accurately identified and positioned, and the problem that tiny damage is easily ignored in a traditional manual detection method is avoided; through the automatic point cloud data processing and clustering thought and the triangulation algorithm, manual intervention is reduced, the data processing efficiency is improved, and an accurate three-dimensional model can be quickly generated.

The invention discloses a section line generation method and device in an intelligent design process and a storage medium, and belongs to the technical field of industrial manufacturing, the method comprises the steps that digital model data of a part is acquired, and the digital model data comprises a product surface and a material pressing surface of the part; a first boundary of the product surface is determined, a second boundary is determined according to the first boundary, the first boundary is the outer boundary of the product surface, and the second boundary is the boundary which fills up the concave-convex contour of the first boundary; determining a reference plane according to the second boundary, wherein the reference plane respectively intersects with the product surface and the material pressing surface; and generating a section line of the part according to the product surface, the material pressing surface and the reference plane. The invention aims to reduce the working procedures of the part process design flow, save time resources and human resources and improve the design production efficiency of parts.

The invention relates to a network intrusion detection method based on hierarchical Bloom filtering and a large language model, and belongs to the technical field of network security. The method comprises the following steps: carrying out preliminary rapid screening on input network communication data by using a hierarchical Bloom filter, training a network intrusion detection model, carrying out anomaly detection through a final network intrusion detection model obtained after training, and generating a detailed report of detected anomaly by using a large language model. According to the method, time and computing resources are saved, the overall efficiency of detection is also improved, real-time anomaly detection and response can be realized, the interpretability of a detection result is enhanced through a report generated by a large language model, and the specific condition and possible influence of an abnormal behavior can be understood conveniently.

The invention relates to a lamp panel detection method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring multiple groups of to-be-detected area standard images and multiple groups of to-be-detected area scanning images of a target lamp panel, wherein the to-be-detected area standard images and the to-be-detected area scanning images are in one-to-one correspondence; a target area standard image is positioned from the to-be-detected area standard images, and a target area scanning image corresponding to the target area standard image is positioned from the to-be-detected area scanning images; aligning the standard image of the target area with the scanning image of the target area; and detecting the target area scanning image based on the aligned target area standard image. By adopting the method, false detection can be reduced.

The utility model provides a laser beauty instrument handle. The handle comprises a handle body, a laser cylinder, a focusing cylinder and a protection head which are detachably assembled into a whole in sequence, the laser cylinder is columnar, and a replacement cylinder mounting cavity is formed in the laser cylinder; the replacement cylinder is movably mounted in the laser cylinder, a cylinder body of the replacement cylinder is columnar and comprises a first end and a second end in the axial direction, and a first lens mounting cavity, a dye body mounting cavity and a second lens mounting cavity are sequentially formed in the cylinder body in the direction from the first end to the second end; the first lens and the second lens are respectively mounted in the first lens mounting cavity and the second lens mounting cavity; the dye body is mounted in the dye body mounting cavity; and the first lens mounting cavity, the dye body mounting cavity and the second lens mounting cavity are communicated. When the laser element is damaged or the lens needs to be replaced, the replacement cylinder is replaced.

The invention relates to a nuclear power reactor building outer dome and an installation method thereof.In the nuclear power reactor building outer dome, on one hand, steel molds are prefabricated into steel mold units in a factory in a blocking mode, after the steel mold units are transported to a site, the steel mold units are spliced into a finished steel mold on the site, and then the steel mold is integrally hoisted to the top of a corresponding reactor building; and a scaffold does not need to be erected on the inner dome, so that the construction process is greatly simplified, a large amount of time and manpower resources are saved, and the construction efficiency is improved. And on the other hand, all the steel molds are directly hoisted after off-site assembly is completed, a scaffold for supporting the outer dome does not need to be erected outside the inner dome, the construction period is effectively shortened, and the project progress is accelerated.

The invention discloses a vehicle sensor calibration method, system and device and a storage medium, relates to the field of automatic driving, and solves the problem of low vehicle sensor calibration efficiency. In the scheme, first data of scanning each calibration device in the calibration system by a total station is received, and calibration prior information is determined according to the first data; receiving second data acquired by each sensor for the calibration device; and determining calibration parameters of each sensor according to the calibration prior information and the second data. Visibly, the parameter information of each sensor can be accurately solved through the point cloud data obtained through scanning of the total station and the second data collected by the sensors, so that the calibration accuracy of the sensors is improved; according to the scheme, calculation can be automatically completed through the processor, a large amount of time and manpower resources are saved, the working efficiency is improved, and the method can be suitable for mass production.

The invention provides an antibody screening method and system based on a differential evolution algorithm and an artificial intelligence model. The method comprises the following steps: S1, acquiring data of an antibody-antigen compound sequence and affinity; s2, encoding the antibody and antigen sequences by using a language model, and converting the encoded antibody and antigen sequences into a matrix M; s3, training a machine learning model by using the matrix M and the affinity parameters to obtain a trained optimal machine learning model; s4, predicting the antigen affinity of the antibody by utilizing the trained optimal machine learning model; and S5, carrying out affinity optimization on the antibody sequence based on a differential evolution algorithm, and carrying out further filtration by predicting the druggability of the antibody so as to screen the antibody with high affinity and good drug properties.

The utility model relates to the technical field of cold chain transport cases, and discloses a cold chain transport case capable of monitoring temperature in real time, which structurally comprises a transport case body, one side of the transport case body is fixedly connected with a refrigeration mechanism, and the top end of the transport case body is fixedly connected with two temperature sensors. The two temperature sensors are electrically connected with a PLC through wires, and the PLC is electrically connected with a temperature adjusting module, a wireless network module and a traffic light alarm device. The real-time temperature monitoring and early warning system is provided with the high-precision temperature sensor and the intelligent traffic light alarm device, so that real-time monitoring and early warning of temperature abnormity are ensured, and cargo loss is reduced to the greatest extent.

The utility model relates to the technical field of wound treatment, and discloses a wound negative pressure suction device which comprises a containing frame, the inner bottom wall of the containing frame is fixedly connected with a negative pressure assembly, the two sides of the inner wall of the containing frame are fixedly connected with reinforcing assemblies, one side of the containing frame is fixedly connected with a clamping strip, and the other side of the containing frame is fixedly connected with a negative pressure assembly. A collecting barrel is arranged in the containing frame. According to the wound negative pressure suction device, through the arrangement of the shunting assembly and the suction assembly, when the wound negative pressure suction device is used, according to the number of wounds of a patient, the number of suction pipes connected with three-way pipes is adjusted, and corresponding valves are opened, so that different suction pipes can treat different wounds, and the negative pressure of the wounds is increased; secretions or waste liquid generated after flushing can be sucked out and discharged into the collecting barrel, integrated treatment is provided for multiple wound surfaces, meanwhile, the multiple wounds can be treated through the negative pressure pump, the workload of medical staff is reduced, time and medical resources are saved, and the burden of patients is relieved.

The invention provides a man-machine collaborative program repairing method based on a large language model, relates to the technical field of automatic program repairing, and remarkably improves the accuracy and success rate of program repairing. According to the method, a large language model is guided through preliminary analysis of manual teaching, errors in a program can be more accurately positioned and repaired by combining a repairing process of multiple iterations and more information introduced step by step, misjudgment and omission are reduced, and the program repairing efficiency is greatly improved. Compared with a traditional method, blind exploration of a large language model on complex problems is avoided through a man-machine cooperation mode, time and computing resources are saved, and adaptability of program repair is enhanced. According to the method, program defects of various types and complexity can be handled, and simple grammar errors or complex logic errors can be effectively handled through a step-by-step deep repair process.

The invention discloses a pedestrian tumble detection method based on NanoDet-Plus and fine tuning CLIP. The method comprises the following steps: S1, collecting data to make a data set; s2, a NanoDet-Plus target detection model is trained, and a trained algorithm model weight file is obtained; s3, performing pedestrian recognition on the image by using a target detection model NanoDet-Plus, and independently storing the recognized pedestrian image as an image file; s4, training the CLIP model and the improved Resnet50 model; s5, performing preliminary classification prediction on the pedestrian attitude image by using a CLIP model, performing further classification prediction by using an improved Resnet50 model, and connecting two classification prediction results so as to obtain a pedestrian attitude prediction result; and judging the current state of the pedestrian according to a posture prediction result, and taking feedback measures. And the recognition accuracy of the pedestrian falling event in the complex scene is improved.

The invention discloses a power transmission line field data acquisition system based on unmanned aerial vehicle vision. According to the invention, the CNN is used to extract the features of the visual image and the infrared image, and the 3D-CNN is used to process the laser radar data, so that the system can capture the multi-aspect information of the power transmission line. Information about the state of the power transmission line can be provided from different angles through data of different modes, fusion of the multi-mode data is beneficial for improving the accuracy and robustness of fault detection, the LSTM network is used for processing time series data, the system can better understand the change of the data along with time, the LSTM can process the series data, and the fault detection accuracy and robustness of the power transmission line can be improved. And the change trend of the fault along with time is captured, so that the fault is predicted and diagnosed more accurately. The weights of different modal data are determined through the single-layer sensor, and the system can automatically learn the importance of each modal data to final fault detection so as to reduce prediction errors, thereby improving the accuracy and reliability of fault detection.

The invention discloses a DNA fragment, a recombinant vector, a reagent combination and a kit for detecting hantavirus HTNV subtypes based on an RPA-CRISPR / Cas12a system, and application of the kit to the non-diagnostic purpose of detecting the hantavirus HTNV subtypes. The detection accuracy of the Hantaan virus can reach 100%, a target substance can be accurately detected, and the probability of false positive or false negative is reduced; meanwhile, the specificity is high, only the Hantaan virus can be detected, and Xinjiang hemorrhagic fever virus, Marburg virus and Ebola virus which have genetic relationship with the Hantaan virus cannot be detected; the method has the characteristics of high efficiency, economical efficiency, convenience and environmental protection of detection at the same time.

The invention belongs to the field of methods for evaluating rare events in electronic engineering, and particularly relates to a failure area evaluation method applied to a high Sigma scene in the electronic engineering. A failure area evaluation method applied to a high Sigma scene in electronic engineering comprises the following steps: establishing an initial sample, establishing a prediction model, screening a high-risk sample, simulating the high-risk sample and calculating an offset delta d, and determining an approximate failure area sample, a secondary approximate failure area sample and a common area sample. Spearman rank coefficients are calculated for the samples of the three areas through a formula, different weights are given to the Spearman rank coefficients, and a final weighted rank correlation evaluation result is calculated. According to the evaluation method, through a sequencing function of a weighted rank correlation evaluation substitution model, the accuracy of sequencing representation of a high Sigma failure area is remarkably improved; and unnecessary simulation times are greatly reduced, and computing resources are saved.

The invention discloses an application-aware configuration parameter self-evolution tuning method and system. The method comprises the following steps: training an I / O performance prediction model of a known application by using I / O characteristic parameters of the known application, and performing secondary training on the I / O performance prediction model of the known application by using a transfer learning method to obtain an I / O performance prediction model of an unknown application; iteratively acquiring each round of parameter configuration of all I / O characteristic parameters in the parameter search space of the to-be-optimized application, transmitting the parameter configuration to a search engine to generate a next round of parameter configuration, and transmitting the parameter configuration to a corresponding I / O performance prediction model to obtain a performance prediction result until the maximum number of iterations or specified duration is reached; and selecting an optimal performance prediction result from the performance prediction results of each round, and taking the corresponding parameter configuration as an I / O characteristic parameter tuning result of the to-be-tuned application. According to the method, the I / O performance prediction model for the unknown application can be rapidly trained, the accuracy of the I / O performance prediction model is effectively improved, and the I / O performance of the storage system is improved.

The invention belongs to the technical field of information processing, and discloses a continuous task planning system and method based on asynchronous causal inference reflection, based on a large language model, through an actor module, a task target and environment observation are used as input, and a next prediction action is used as output; extracting association information between objects from environment observation of a current track through an association reasoning module, performing causal inference of a first level of hierarchical causal inference, and determining association between the objects; performing hierarchical causal inference on a second level through an intervention plan generation module according to a new trajectory obtained by anti-fact inference and previously extracted associated information, generating a plan of next intervention implementation, and verifying the reason of task failure; the anti-factual inference module regenerates a task trajectory according to a task target and task trajectory condition to perform hierarchical causal inference of a third level, and determines the reason of the failure of the previous attempt. And the success rate of long-term task planning is obviously improved.

The invention discloses a construction method for realizing a homopolymer-homopolymer-double block copolymer ternary entangled polymer mixture by adopting a dissipative particle dynamics method. The method comprises the following steps: acquiring a characteristic ratio and a balance bond length of a simulation system, and generating a series of homopolymers and double-block copolymers taking the bond length as a step length; according to the chain length, the chain number and the number density of a simulation system, putting a series of obtained random walking homopolymers and copolymers into a simulation box; introducing interaction potential among particles, introducing bond-forming interaction potential by using simple harmonic spring potential, and introducing bond-angle interaction potential by using a bending potential function; the rejection parameters are gradually increased, and the difference of the rejection parameters between the stages should be kept equal or approximately equal; setting a rejection parameter as a final use value; and detecting whether the conformation of the chain is balanced. The three-element entangled polymer mixture constructed by the invention can accurately describe the dynamic and rheological properties of an entangled polymer blend, and can be used for researching the structure and rheological properties of an entangled polymer interface.

The invention discloses a remote control switching reclosing function device, comprising a signal detection unit used for receiving and identifying a reset signal and a closing signal and isolating the reset signal and the closing signal; the signal delay circuit is connected with the signal detection unit and is used for delaying the pulse widths of the reset signal and the closing signal according to the closing requirement of the relay; the relay driver is connected with the signal delay circuit and is used for receiving the reset signal and the closing signal and driving the relay; two groups of relays are adopted, one group of relays is connected to external control, and the other group of relays is connected to the LED lamp; the two groups of relays are completely the same and are adjacent in installation position. According to the invention, the operation of the reclosing device is realized through the remote control signal, so that the problems in the prior art are solved, and the operation efficiency and safety of a power system are improved.

The embodiment of the invention provides a hyper-parameter determination method and device of an image processing model and electronic equipment, and relates to the technical field of deep learning, and the method comprises the steps: obtaining an image size of a sample image used for training the image processing model, and taking the image size as a target sample size; according to a first association relationship between a pre-established sample size set and an input size set, determining an input size associated with the target sample size as the size of an input image of the image processing model; calculating the batch size of the training image processing model according to the determined size of the input image and the size of a storage space allocated for the image processing model in the equipment for training the image processing model; and calculating the number of iterations of the training image processing model based on the specified information. According to the scheme, the efficiency of determining the hyper-parameters of the image processing model can be improved.