77results about How to "Response in real time" patented technology

The invention discloses a weather detection method and system based on a weather rainfall radar and a storage medium. The system comprises the weather rainfall radar, a weather data centralized control management center and a radar time sequence chart neural network detection algorithm arranged in the weather data centralized control management center. According to the method, different cloud layer characteristic weathers are collected through a meteorological rain detection radar, and obtained electromagnetic wave scattering parameters of the different cloud layer characteristic weathers are input into a radar time sequence chart neural network detection algorithm for analysis and calculation, so that polarization phase differences corresponding to the different cloud layer characteristic weathers are obtained to detect the height and vertical distribution of cloud layers; and when the height and the vertical distribution of the cloud layer exceed boundary threshold values, sending out risk early warning information of different weather types, and automatically detecting spectral widths of different cloud layer characteristic weather and corresponding polarization phase differences. And the weather detection precision and efficiency are improved.

The invention provides a subway pedestrian anomaly detection method and system based on an improved yov8 network and a clip model, and relates to the technical field of subway pedestrian detection, and the method comprises the steps: obtaining a video frame in a subway scene, and carrying out the preprocessing of the video frame; inputting the preprocessed frame data into an improved yolov8 neural network model to obtain an abnormal behavior target detection frame, and performing target candidate tracking on the target detection frame by using a target tracking algorithm to obtain a candidate image of a target area of a specific behavior; the method comprises the following steps: inputting a candidate image containing a target area into a clip model, inputting a set detection text into the clip model, mapping the detection text and the candidate image of the target area, performing text-image similarity matching, and optimizing a similarity matching threshold by adopting a Q-Learning reinforcement learning immune algorithm to obtain a similarity matching result; and the abnormal behavior detection accuracy is improved.

The invention provides a direct-current loop resistance testing system and method and a loop resistance tester, and relates to the technical field of resistance testing systems.The direct-current loop resistance testing system comprises an intelligent testing module, a temperature calibration module, a data analysis module, a data comparison module, a data calibration module, a data transmission module and a data display module; and the performance and the reliability of the direct-current loop resistance test system are obviously improved. The calibration coefficient is dynamically generated and repeatedly calibrated, so that manual intervention is reduced, and the precision of measured data and the automation degree of the system are improved. The mean value and the standard deviation are calculated through the moving average method, the dynamic threshold value is generated, abnormal data can be detected and recognized more accurately, it is ensured that the system can respond to and process abnormal conditions in real time, and the real-time performance and the response speed of the system are improved. In addition, a temperature calibration method is adopted to effectively compensate the influence of temperature change on the measurement resistance, and the adaptability and robustness of the system are enhanced.

The invention discloses a carbon sink trading market supply and demand matching method, a computer device and a medium. The method comprises the following steps: collecting multi-source data information of a target carbon sink project, and preprocessing the multi-source data information to obtain a first data set; analyzing the first data set based on a constructed market multi-dimensional feature space model to obtain target market information corresponding to the target carbon sink project; target market information is analyzed based on the constructed market dynamic network model, target intention party information is identified, and the target intention party information comprises key market participants and transaction modes thereof; and analyzing the target intention party information based on a preset supply and demand matching algorithm to obtain market supply and demand matching information of the target carbon sink project. The method not only can accurately describe a complex carbon sink trading market structure, but also can process large-scale and high-frequency trading demands, and makes a real-time response to the dynamic change of the market, so that the supply and demand matching of the carbon sink trading market is more accurate, timely and effective.

The invention discloses a handheld security check imaging method based on MEMS assistance, and aims to combine an MEMS sensor with an MIMO radar plate to form a handheld convenient security check imaging method with high precision and low cost according to the rapid and fine security check requirement of people in a security check place with dense people. Firstly, the radar board transmits a linear frequency modulation signal and receives an echo signal reflected by a target, and during the period, the MEMS sensor feeds back the motion track and position information of the handheld security check system in real time, and provides accurate attitude and position information. And secondly, position calibration is carried out on the track information of the MIMO radar antenna through the data of the MEMS sensor, and the zero drift of the accelerometer is compensated based on the wavelet packet change, so that the error caused by the system motion and the position change is reduced. And finally, residual phase errors are estimated and eliminated based on a particle swarm algorithm, non-contact three-dimensional imaging of the target is realized in combination with a wavenumber domain imaging algorithm on this basis, and hazardous article inspection is realized in combination with technologies such as deep learning and image recognition.

The invention discloses an industrial Internet of Things edge gateway system and a data processing method thereof, and the system comprises a data conversion module which is used for converting source JSON data in a specific format into target JSON data in a template format through a predefined template; the data acquisition module uses a configurable data acquisition pipeline as a northbound application node, binds the northbound application node with a southbound equipment node, receives data transmitted by equipment, and dynamically adjusts a data acquisition strategy through configuration management; the northbound AI reasoning module is used for being bound with a southbound equipment node and performing edge side model reasoning according to data transmitted by equipment to obtain an anomaly detection result; and the internal API of the edge gateway system is used for writing in the internal data point of the PLC or sending an information notification to a specified cloud end. By adopting the technical scheme of the invention, the data processing capability is improved, and the intelligent level of the system is enhanced.

The invention relates to the field of network security monitoring, in particular to a network security threat monitoring method based on deep learning, which comprises the following steps: collecting flow data on a network node, and preprocessing the collected flow data; constructing a deep neural network model to analyze the flow data, and adjusting model parameters through an optimization algorithm to minimize a loss function; evaluating the network security threat according to an output result of the deep learning model; determining response measures according to the threat assessment result, and taking corresponding response operations for different threat levels; according to the method, the timeliness of the response is evaluated according to the time interval from threat detection to response measure implementation, the deep neural network is utilized, the deep features can be automatically extracted from the network flow, and the recognition precision of the complex attack mode is remarkably improved. Compared with a traditional rule-based detection method and a simple machine learning algorithm, the scheme can effectively reduce the false report rate and the missing report rate, and improve the accuracy of network threat monitoring.

The invention provides an intelligent security and protection system, method and device based on AI analysis and a storage medium, and relates to the technical field of intelligent security and protection. Monitoring video information in an intelligent security and protection area is collected, and behavior information of a target person in the monitoring video information is extracted; constructing a behavior topological graph according to the behavior information, extracting abnormal behavior nodes from the behavior topological graph, and determining a behavior abnormal deviation degree according to behavior deviation characteristics among the abnormal behavior nodes; obtaining an audio data stream, converting the audio data stream into a security audio segment of a target person, and constructing an audio feature matrix based on the security audio segment; the intrusion probability of the target person to the sensitive area in the intelligent security protection area is determined according to the behavior anomaly deviation degree and the audio feature matrix, and an intrusion report is sent to a security protection center based on the intrusion probability, so that the behavior mode of the target person can be accurately identified, and the intrusion threat of the target person is evaluated in combination with the audio features; the safety and reliability of the intelligent security and protection system are improved.

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 the technical field of sensing equipment synchronous control, and discloses a camera synchronous triggering system and method and computer equipment. The system comprises a vehicle-mounted upper computer which is used for sending a working mode control instruction to a synchronous trigger box; the positioning device is used for sending a first pulse signal corresponding to the position information to the synchronous trigger box; the laser radar is used for sending a second pulse signal corresponding to the distance information to the synchronous trigger box; the synchronous triggering box is used for determining a target execution strategy corresponding to the target working mode according to the working mode control instruction and a pre-stored corresponding relation between the working mode and the execution strategy; the processor is used for determining a trigger signal according to the target execution strategy so as to control the camera to perform exposure; and the camera is used for receiving the trigger signal to realize exposure control. By implementing the technical scheme of the invention, the highly synchronous control of the exposure opportunity of the camera is realized, and exposure at a specific position and specific time is ensured.

The invention discloses a plant leaf disease and insect pest detection method and system, and relates to the field of intelligent agriculture, and the method comprises the following steps: calibrating a binocular vision system; collecting plant leaf disease and insect pest images by adopting a calibrated binocular vision system; using improved YOLOv8-seg to segment the disease and insect pest area of the image to obtain a left mask, a right mask and an integral plant leaf; calculating an overlapping region of the left mask and the right mask as a final pest and disease damage region; calculating the percentage of the final pest and disease damage area in the whole plant leaf as the plant leaf pest and disease damage degree; respectively acquiring geometric center coordinates of the left mask and the right mask; performing three-dimensional correction on the left mask and the right mask; and calculating three-dimensional coordinates corresponding to the left mask and the right mask after three-dimensional correction. The invention provides a plant leaf disease and insect pest detection method and system, and provides convenience for intelligent agricultural disease and insect pest diagnosis and agricultural mechanization operation.

The invention discloses a photovoltaic product supply chain traceability method and system, and the method comprises the steps: constructing a supply chain network model through a graph algorithm, defining a transmission protocol and a privacy protocol for the edges in the supply chain network model, obtaining a data chain, constructing a constraint rule for the data chain, and obtaining a traceability result of the supply chain network model; learning the constraint rule by using a deep learning algorithm to obtain a constraint model, constraining a generation stage of the data chain through the constraint model, extracting a serial number feature, a time feature and a position feature of a product through the constrained data chain, performing multi-dimensional index coding based on a KD tree, obtaining a traceability query index, and obtaining a traceability request. And traversing the KD tree based on the traceability query index to obtain a traceability path. The method not only can locate the source in time, but also can detect the abnormal environment, has good interpretability, and can be directly applied to a photovoltaic product supply chain traceability system.

The invention provides an industrial network security service providing method adaptive to multiple types of service scenes, which comprises the following steps of: constructing a knowledge graph based on a space and time association relationship between scene characteristics of production services; on the basis of the knowledge graph, performing model training in combination with a relational graph convolutional network, a gating loop unit, a multi-head attention mechanism and a near-end strategy optimization algorithm to obtain a basic network security service supply model; according to a meta reinforcement learning algorithm, performing meta training on the basic network security service supply model to obtain a standard network security service supply model; and providing industrial network security services for different types of business scenes through the standard network security service supply model. According to the method, the safety and the flexibility of the industrial network are remarkably improved.

The invention provides a high-temperature superconducting pinning suspension active and passive hybrid vibration isolation system and method, and relates to the technical field of vibration isolation, the vibration isolation system comprises a measurement and control system and a suspension system, and the vibration isolation method comprises the steps that a floater of the suspension system is stabilized at a balance position relative to a stator through suspension pretreatment; during external excitation, vibration data generated by a floater connected with a vibration isolated body relative to a stator is collected and processed, a controller processes the vibration data through an internally preset PID control algorithm and outputs a control signal, and a coil on the floater receives and outputs the control signal and executes the control signal to achieve a preset state. Self-stabilization suspension of the system can be achieved without external energy input, meanwhile, the solenoid coil is wound on the outer side of the permanent magnet, real-time active control over suspension force is achieved by adjusting transmission current of the coil, and therefore the problems that the vibration isolation frequency band of a passive vibration isolation system depends on the inherent frequency, and rigidity and damping cannot be adjusted are solved.

The invention discloses a ground resistance detection method and system for an electrostatic management tester, and relates to the field of ground resistance detection, and the key points of the technical scheme are that Op collection nodes are arranged in parallel on a circuit, voltage data on each node are obtained through a data collection device, and preliminary processing is carried out to obtain an initial data set; an optimization detection algorithm is used for the initial data set, and voltage data are converted into comprehensive direct-current voltage and comprehensive alternating-current voltage; setting an advanced detection threshold value, judging the obtained comprehensive direct current voltage and the comprehensive alternating current voltage with the advanced detection threshold value, performing mode selection according to a judgment result, giving an alarm if the mode is abnormal, and switching to a resistance detection mode and performing voltage measurement if the mode is normal. The device is suitable for detecting grounding resistance by an electrostatic management tester, and high-quality detection is realized.

The invention relates to a custom Mock platform and a network request simulation processing method based on the custom Mock platform, and the platform comprises a Mock rule management unit which obtains a configuration field of a Mock rule based on an interactive configuration interface; the extraction rule configuration unit is used for obtaining configuration fields of extraction rules based on the interactive configuration interface, extracted parameters are stored in a variable pool, and the parameters in the variable pool are used for being converted into dynamic variables; the custom variable unit is used for customizing a constant based on the interactive configuration interface and / or dynamically generating a custom variable by utilizing a custom function provided by the Mock platform; the user-defined function unit is used for receiving and approving the uploaded user-defined function and adding the user-defined function into a function library of the Mock platform; and the Mock response unit is used for editing based on the interactive configuration interface or generating dynamic response header information based on a self-defined function, and generating a response message based on a preset template. According to the invention, a complex business scene can be accurately simulated and responded in real time, so that the authenticity and effectiveness of the test are greatly improved.

The invention relates to the technical field of optimal scheduling, in particular to an optimal scheduling method based on physical ID drive and a digital twinborn model. The method comprises the following steps: carrying out landform modeling on a target fruit farm to obtain a three-dimensional landform model of the fruit farm; carrying out planting area data acquisition on the target fruit farm to obtain detailed data of a planting area of the fruit farm; performing intelligent semantic annotation on the three-dimensional terrain model of the fruit farm according to detailed data of a planting area of the fruit farm to obtain a digital twin model of the fruit farm; performing passable path intelligent identification on the fruit farm digital twinborn model, and performing identifier endowing to obtain a fruit farm path network diagram and a fruit farm path ID list; performing picking operation area division on a target fruit farm based on the fruit farm digital twinborn model, and performing identifier endowing to obtain a fruit farm picking area division map and a fruit farm acquisition area ID list; according to the method, the resource scheduling efficiency is remarkably improved, including reasonable distribution of personnel, effective use of tools and optimal scheduling of transport vehicles.

The invention relates to the technical field of charging pile protection, and provides a charging pile protection method and system for coping with extreme weather. The method comprises the following steps: constructing a charging pile topology network; constructing a connection equipment topology network; the charging pile topology network and the connection equipment topology network are connected to generate a double-layer topology network; performing real-time monitoring by using a double-layer topology network to obtain extreme weather information and a double-layer operation monitoring data set; inputting the extreme weather information and the double-layer operation monitoring data set into a grading protection model for analysis, and generating a grading protection strategy; and performing hierarchical protection on each charging pile of the charging pile topology network according to the hierarchical protection strategy. The technical problem that the charging pile is damaged due to the fact that the charging pile is not protected in time in extreme weather is solved, and the technical effects that extreme weather information is monitored in real time through a double-layer topology network, a grading protection strategy is formulated, and normal operation of the charging pile under the extreme weather condition is effectively protected are achieved.

The invention discloses a real-time intelligent disaster early warning method based on multi-modal data, and belongs to the technical field of disaster early warning, and the method comprises the steps: 1, constructing a multi-modal data platform; 2, obtaining a first training model by using a graph neural network technology, and obtaining a second training model in combination with a federated learning technology; 3, integrating the first training model and the second training model to obtain a disaster propagation model; 4, deploying a response function on edge equipment close to the data source; and 5, continuously collecting data of actual disaster occurrence, comparing the data with a model prediction result, and carrying out early warning response. According to the invention, by integrating disaster data obtained by a plurality of data sources, more data sources can be obtained, the comprehensiveness and accuracy of early warning can be improved, and the generalization ability of the model can be significantly improved by constructing the disaster recognition model by using the graph neural network and federated learning, so that the system can cope with various complex and changeable disaster scenes.

The invention belongs to the technical field of rocket launching, and particularly relates to a rocket fairing noise reduction method and device and a storage medium. The method comprises the following steps: acquiring a noise signal; performing spectral analysis on the acquired noise signal by adopting fast Fourier transform to obtain a frequency domain signal, the frequency domain signal comprising amplitude and phase information under different frequency components; setting a boundary of each frequency band, extracting feature information of each frequency band, and extracting noise components of each frequency band according to the feature information of each frequency band; the noise reduction strategy of each frequency band is optimized through an artificial bee colony algorithm, and the optimal frequency and amplitude of the anti-phase sound waves of each frequency band are obtained; and according to the output of the artificial bee colony algorithm, anti-phase sound waves are generated, and active noise reduction is carried out on the fairing. According to the method, the overall noise reduction performance of the fairing can be improved, the response speed is high, the requirement for computing resources is low, and the method is suitable for scenes with strict requirements for time and resources in rocket launching scenes.

The invention relates to the technical field of power grid automation, in particular to a distribution network holographic data intelligent identification processing system which comprises an inertial data analysis module, a real-time monitoring module, a distribution network data analysis module, a label setting module and a data identification module. The method comprises the following steps: retrieving an equipment fault position existing in a power distribution network through an inertial data analysis module, analyzing fault characteristics of the equipment fault position to obtain a fault characteristic curve of the equipment fault position, and then collecting and processing holographic data of the power distribution network in real time to obtain a data fluctuation set; the method comprises the following steps: calculating a similarity value of a unit interval and a data fluctuation set, setting a data label for a target monitoring position based on the similarity value, calculating a priority value based on a priority identifier in the data label, and finally performing priority processing on real-time operation data of the target monitoring position according to the size of the priority value, thereby facilitating optimal allocation of resources and improving the accuracy of the real-time operation data. And the abnormal data can be quickly responded and processed.

The application discloses a motion self-learning model applied to photoelectric devices in the industry control and instrument industry, comprising a S1 setting function corresponding relation, a function class; S2 divides and identifies a region and a background according to a sensing distance, classifies detection functions in common motion identification in the function class according to monotonicity, and all physical parameter sensing function information in the function class is included in a monotonic event function; S3 converts physical parameter sensing into an electric signal, outputs the signal after signal processing and logic control, defines motion process identification as expansion of a local event function in a uniform space-time, and the independent variables of the function include time, spatial distance and reflection intensity. The consistent principle of the sensor, data processing, independent device and self-learning system is proposed. Different implementation cases are used to illustrate each part of the principle. Differences from other similar application devices are analyzed.

The present invention provides a carbazole terpyridine ruthenium complex or its salt, or its solvate. The present invention also provides a method for synthesizing the carbazole terpyridine ruthenium complex. The present invention further provides a probe. The present invention further provides the use of the above-mentioned carbazole terpyridine ruthenium complex or its salt, or its solvate, and / or the above-mentioned probe in detecting oxygen. The present invention provides a carbazole terpyridine ruthenium complex oxygen detection probe and its preparation method and application, which uses carbazole terpyridine as an optical signal reporting group to prepare a molecular fluorescent probe that can be used to detect oxygen, and generates singlet oxygen by reacting with oxygen under light conditions and continuing to react with singlet oxygen to obtain an oxidation product with significantly enhanced fluorescence, thereby achieving highly selective, in-situ, and real-time response to oxygen.

The invention discloses a method and system for constructing a digital human system for ship equipment, and relates to the technical field of ship equipment. The method comprises: collecting real-time data of a ship power system, a navigation system, a propulsion system, a steering gear system, a power supply system, a life-saving equipment system, a gas detection system, an oil mist concentration detection system, and a temperature monitoring system to obtain equipment-level data; sensing the ship's embodied environment based on screened key equipment indicator data according to a real-time online monitoring system for the marine environment and an intelligent shipping embodied large model to obtain the ship's embodied environment conditions; based on the sensed ship's embodied environment conditions, calling a ship equipment interface to obtain real-time ship navigation reference data; establishing a navigation digital twin model according to the real-time ship navigation reference data; simulating and predicting the behavior of ship equipment using the established navigation digital twin model, and establishing a fault diagnosis system and a health management system.

The invention belongs to the technical field of deep learning, and discloses an auxiliary training branch-based lightweight forest fire detection model design method and system, and the method comprises the steps: collecting forest fire image samples through an inspection unmanned plane and a monitoring holder, and constructing a forest fire data set; a Mosaic-4 data enhancement method is used for the collected mountain fire data set; preprocessing the collected complete data set by adopting a histogram equalization algorithm, and marking a preprocessed image sample; dividing the marked image samples into a training set and a test set in proportion; designing a lightweight convolutional neural network main body model structure; and training the lightweight forest fire detection model based on the auxiliary training branches by using the forest fire data set, and finally obtaining a lightweight forest fire detection model. According to the method, a lightweight convolutional network design and auxiliary training branch design method and a Mosaic-4 data enhancement and histogram equalization algorithm are combined, so that the detection speed is greatly improved under the condition of ensuring the detection accuracy.

A beamforming design method for intelligent metasurface arrays based on orthogonal time-frequency-space technology includes: aggregating multipath channels under direct illumination into cascaded channels with near-field direction vectors; randomly initializing the phase shift of intelligent metasurface units; establishing an effective channel matrix based on the cascaded channels; then establishing a multi-user and rate objective function; maximizing the objective function through phase shift optimization using a hierarchical search strategy; repeatedly executing the hierarchical search strategy to search and optimize subsequent blocks until the number of intelligent metasurface units in each block reaches a preset value; then performing an exhaustive search in the final block, thus completing one iteration; repeating the iteration until the maximum number of iterations is reached; outputting the final optimal phase shift; and implementing beamforming design based on the final optimal phase shift. This invention can reduce the complexity of beamforming design and ensure reliable transmission of high capacity during high-speed movement.

The invention discloses a control method and device of a battery management system, a medium and photovoltaic cleaning equipment, the method is applied to the photovoltaic cleaning equipment, and the photovoltaic cleaning equipment comprises a battery, a motion controller connected with the battery through a secondary loop relay, and a motor connected with the battery through a main loop relay. A solar MPPT connected with a battery through a charging relay comprises the steps that after a wake-up source is detected, a secondary loop relay is controlled to be closed, and a battery management system enters an initial state; obtaining a motor driving instruction and a solar MPPT charging port voltage; and switching the opening and closing states of the main loop relay and the charging relay according to the motor driving instruction and the voltage of the solar MPPT charging port, so that the battery management system enters a corresponding working state. Therefore, the working state of the battery management system is changed by switching the opening and closing states of the main loop relay and the charging relay, so that the photovoltaic cleaning equipment can utilize light energy at the maximum efficiency and respond to an operation instruction in real time.

The invention belongs to the technical field of cargo transportation systems, and particularly relates to a waybill-free express cargo identification system based on data labels, which comprises a data acquisition module and a model training module, according to the invention, through combination of the image and the sensor data, real waybill-free operation is realized, and a user does not need to manually fill in a paper waybill. The design not only improves the operation efficiency, but also conforms to the digital and paperless development trend of the modern logistics industry. A traditional system often depends on paper waybills or needs to manually input a large amount of information, and operation is tedious. A user interface and a feedback module are designed in the system, a user is allowed to confirm and correct an identification result, and feedback data are used for subsequent model retraining and optimization. The self-improvement mechanism enables the system to continuously learn and improve the recognition accuracy. However, many existing systems lack an effective user feedback mechanism and are difficult to realize continuous improvement.

The invention discloses a driving device and a driving method for a spatial light modulator. The driving device comprises a main control unit, a signal conversion unit, a level conversion unit, a signal modulation unit, a signal driving unit, a power supply unit and an LCoS spatial light modulation unit, the main control unit is used for converting and adjusting the signal output by the signal conversion unit; the main control unit outputs a first digital signal, a second digital signal and a first control signal; the signal conversion unit is used for reference input of the signal modulation unit; the signal conversion unit receives the first digital signal and the second digital signal and generates a first pixel level signal and a first black field level signal; the level conversion unit is used for controlling the driving signal of the LCoS output by the modulation unit; compared with the prior art, the driving level of the driving signal can be dynamically configured according to the driving requirement of the spatial light modulation unit, the driving mode is more flexible, and the driving circuit can adapt to different types of LCoS products and is high in compatibility.

The invention discloses a hot pot induction cooker firepower control method based on vision, and the method comprises the following steps: collecting table region image data through a camera, and transmitting the data to a data processing part through a network, a high-performance computer calculates and analyzes image data acquired from a camera to obtain an analysis result, firepower is controlled through a network according to the analysis result, and an intelligent induction cooker is in communication connection with the computer, receives a control signal to adjust the firepower and receives a feedback signal. The feedback signal receiving is mainly characterized in that after the firepower of the intelligent induction cooker is adjusted, the boiling condition of the bottom of the hot pot is continuously collected through cameras, the cameras in the S1 are in communication connection with a computer through a network, and the multiple cameras are installed above the dining area of the hot pot. According to the invention, human intervention actions can be reduced, energy consumption is saved, and the dining experience of customers is improved.