24results about How to "Maintain real-time" patented technology

The invention discloses a multi-modal information fusion and dynamic updating group behavior analysis method. The method comprises the steps of information integration and dynamic updating, multi-modal information obtaining and information preprocessing, feature extraction, feature representation conversion, alignment and fusion, and multi-modal information real-time processing and dynamic updating through a stream processing framework. A self-learning mechanism and a self-optimization mechanism are introduced; group portrait generation: extracting population statistical characteristics, geographic position distribution, behavior and emotional psychological characteristics, social relationships, social media and economic characteristics from the multi-modal data to generate a group portrait; analyzing group behaviors; and outputting visualization, and outputting group behavior prediction results in a visualized manner, including a behavior trend graph, a group clustering graph, an association rule graph and an abnormal behavior detection graph. According to the method, multi-modal data from various sources and forms are effectively integrated and analyzed, self-learning and self-optimization mechanisms are introduced, and finally group behavior analysis is performed.

The invention relates to the technical field of camera adjustment, in particular to a camera adaptive adjustment method and system and a storage medium. The method comprises the following steps: acquiring a multi-source environment sensing data set and an initial environment image; performing environment feature recognition on the initial environment image according to the multi-source environment sensing data set to obtain a scene environment feature map; performing semantic scene segmentation on the scene environment feature map to obtain a semantic scene macroscopic feature map; performing time sequence attention target tracking on the scene environment feature map to obtain a target vehicle motion feature map; performing feature constraint fusion on the semantic scene macroscopic feature map and the target vehicle motion feature map to obtain a target vehicle-scene motion semantic map; and performing parameter adjustment on the camera according to the target vehicle-scene motion semantic map to obtain a camera parameter adjustment strategy set. According to the invention, the performance and the image quality of the camera in a changeable environment are improved.

The invention discloses an environment temperature and humidity comprehensive monitoring device, and relates to the field of environment monitoring, and the device comprises a mounting rack which is used for bearing all functional parts and functional modules, and serves as a mounting main body; the monitoring unit is used for detecting temperature and humidity data in a limited range as initial detection data; the temperature and humidity acquisition part is used for analyzing interference sources and influence events in a limited range and compensating initial detection data; the monitoring unit and the temperature and humidity acquisition part are both mounted on the surface of the mounting frame; an interference source in an environment and a state of the interference source are detected and identified in real time, an interference compensation model is constructed by using historical data, influence brought by the interference source is converted into a compensation value, and detection data are corrected, so that external interference factors are compensated intelligently, and an initial detection value is corrected based on the compensation model and an actual interference value. The stability and accuracy of final temperature and humidity monitoring data are ensured, and the actual condition of the current monitoring area is accurately reflected.

The invention relates to the field of cleaning systems, in particular to a CIP automatic cleaning system of oily sludge treatment equipment. The technical problems that when an existing oil-containing sludge treatment equipment CIP automatic cleaning system cleans the oil-containing sludge treatment equipment, running of a CIP system needs to consume a large amount of water and energy, especially in the high-temperature cleaning stage, although the cleaning effect can be improved by increasing the concentration of a cleaning agent, the cleaning cost can be remarkably increased, and the cleaning effect is poor are solved. Therefore, the cleaning cost and the resource consumption are high; according to the technical scheme, the CIP automatic cleaning system of the oily sludge treatment equipment comprises a data acquisition module, a data analysis module, a strategy making module, an execution adjustment module and a cleaning module; the cleaning process is optimized through the intelligent control system, accurate detection of the equipment surface pollution degree, intelligent making of the cleaning strategy and real-time adjustment and optimization of the cleaning process can be achieved, and therefore energy consumption and water resource consumption are reduced while the cleaning effect is ensured.

The invention discloses an underwater vehicle hydrodynamic noise rapid forecasting method and system based on a BP neural network optimized by a genetic algorithm, belongs to the field of ship underwater radiation noise forecasting, and designs a BP neural network noise forecasting model optimized by the genetic algorithm. Key variables including shell thickness, molded line parameters, surface roughness, material attributes, sailing speed, fluid medium density, viscosity coefficient and underwater vehicle diving depth are selected as characteristic conditions. The initial weight and threshold value of the optimal BP network are obtained through the global optimization capacity of the genetic algorithm, the optimal initial weight and threshold value obtained through the optimization algorithm serve as the initial weight and threshold value of the BP neural network, and training is carried out to avoid falling into the local minimum value. And performing iterative training on the optimization model by using the training set to obtain a high-precision noise forecasting model, thereby realizing forecasting of the hydrodynamic noise of the underwater vehicle. The method is suitable for forecasting the hydrodynamic noise of the underwater vehicle, and has the advantages of wide applicability, high accuracy and the like.

The invention relates to the technical field of medical image analysis, in particular to a breast cancer image data intelligent analysis and grading diagnosis platform based on a large language model, and is characterized in that an organization level feature segmentation module segments an image into various lesion areas based on breast image data through feature texts extracted from image description by the large language model; and mapping to a hierarchical path of the large language model to obtain an image feature hierarchical mapping result. According to the method, through multi-dimensional means such as feature extraction and hierarchical mapping, the breast image data is subdivided into a plurality of feature hierarchies, and the feature relationship of the focus area is constructed, so that multi-level refinement in a focus diagnosis path is realized. By identifying the change trend and the abnormal point of each level feature in the focus, the potential focus area can be more accurately locked, the focus feature identification process is accelerated, and effective transmission of information and comprehensive recording of the focus structure are guaranteed.

The invention belongs to the technical field of park monitoring and early warning, and particularly relates to a network early warning method for an ad hoc network or a local area network of a park, which comprises the following steps: collecting network data of different sources in the ad hoc network or the local area network of the park in real time, and preprocessing the collected network data of the park; carrying out topological structure identification on the preprocessed network data, and constructing a graph neural network model; fusing the preprocessed network data of different sources by using a multi-source information fusion technology, constructing an early warning model, and identifying an abnormal mode or a potential risk in the network data; early warning output and response: monitoring the park network in real time by an early warning model, and dynamically adjusting the early warning model by using a gradient descent method; and when the early warning model detects an abnormal behavior or a potential fault, an early warning mechanism is triggered, and corresponding response measures are taken. According to the network early warning method for the ad hoc network or the local area network of the park, the accuracy and the efficiency of park early warning are further improved.

The invention relates to the related technical field of drive control, in particular to an asymmetric AMP architecture drive control integrated system based on an ARM + FPGA platform, which adopts an asymmetric AMP architecture based on the ARM + FPGA platform and comprises an ARM end, an FPGA end and a PCIE bare core of the asymmetric AMP architecture. The FPGA end is used for realizing a motor control flow, a sensor reading flow, a hardware alarm signal processing flow and an oscilloscope data acquisition flow; the ARM end is used for realizing processes corresponding to the lower computer, the upper computer, the ECAT module and the communication module respectively; and the PCIE bare core is respectively connected with the ARM end and the FPGA end, and is used for realizing real-time stable transmission between the FPGA and the ARM through a preset communication protocol, and ensuring a microsecond-level communication period by utilizing high-speed transmission of the PCIE bare core and the stability of a bare core timer.

The invention relates to the technical field of network security, and discloses a virus monitoring and early warning method based on network traffic deep analysis, which comprises the following steps: collecting network traffic data in real time through a deep packet inspection technology, extracting traffic features, and constructing a traffic feature sequence based on a time window; establishing an anomaly detection model based on the traffic feature sequence, and identifying an anomaly mode deviating from normal behavior distribution; according to an abnormal behavior detection result, constructing a dynamic model of an equipment infection state, and quantifying an equipment infection risk; and constructing a propagation graph model in combination with an equipment interaction relationship, and simulating a diffusion path and a propagation trend of the virus in the network based on propagation dynamics. According to the method, hidden behaviors of unknown viruses and variant viruses can be accurately monitored, a virus propagation path and an influence range are dynamically predicted, optimal allocation of defense resources is realized in combination with a game theory and linear programming, and the real-time performance of virus monitoring and defense is improved.

The invention discloses an infrared small target detection model construction method and an infrared small target detection method, belongs to the technical field of computer vision, and particularly relates to infrared image small target detection. The problems that in an existing infrared image small target detection technology, detection is difficult, light weight is not enough, and real-time performance is insufficient are solved. The method comprises the following steps: the trained infrared small target detection model is used for obtaining a finally detected small target according to an input original infrared image. The construction method of the infrared small target detection model and the infrared small target detection method are used for detecting an infrared image small target.

The invention relates to the technical field of image communication, in particular to an intelligent camera with active defense and a defense method thereof, and the intelligent camera comprises a data acquisition module, an extraction module, a judgment module, a determination module, a screening module and a defense module. According to the invention, through fusion of a multi-parameter analysis technology and combination of the target position, the attitude angle and the height of a real-time image and dynamic changes of sound intensity, accurate identification of an intrusion target is realized, and the judgment module adopts a multi-level logic of combination of boundary judgment and attitude angle change, so that the accuracy of abnormal target judgment is effectively improved; the screening module reduces erroneous judgment through comprehensive screening of sound intensity and position; the defense module calculates a launching track based on the real-time position and height of the target, fixed-point active defense is achieved through a water bomb, passivity of a traditional voice alarm mode is avoided, and the problem that the response speed is not timely due to single fingerprint recognition and vibration monitoring data is effectively solved.

The invention discloses a medical information module big data-based low-energy-consumption single-chip microcomputer system, which comprises a hardware framework and a software framework, and is characterized in that the hardware framework is a single-chip microcomputer hardware platform, and the single-chip microcomputer hardware platform comprises a processor, a memory, a peripheral interface, a power supply and a resource sharing center; the resource sharing center is in electric signal connection with a centralized prescription checking module, a health follow-up visit module, a regional inspection module, a network electrocardio module, a disinfection supply module, a medicine distribution and supply module, a medical care scheduling module, an image cloud diagnosis module, a remote consultation module and an intelligent first-aid module in sequence; the software framework comprises a low-power-consumption management module and an RTOS management module. According to the scheme, the resource sharing center is controlled to build all doctor-patient network information modules, medical information data are collected in an omnibearing mode, the overall energy consumption of the system is effectively reduced through a low-power-consumption management algorithm, and the endurance time of related equipment is prolonged, so that medical information big data are continuously captured, and good two-way services are provided for doctors and patients.

The application provides a physical constraint-based illumination adaptive spectral reflectance reconstruction system, an illumination adaptive spectral reflectance reconstruction framework (I-SRR) based on no known illumination, auxiliary hardware or multiple captures, which can accurately recover intrinsic spectral reflectance of a material from a single RGB image; a spectral imaging physical model is embedded into a deep unfolding framework, a differentiable optimization structure is constructed based on an alternating direction multiplier method (ADMM), a spectral formation and recovery process is implicitly integrated, a physically guided end-to-end learning is realized through deep unfolding optimization based on physical constraints; the application can realize robust reflectance recovery under unknown or complex illumination conditions, adaptively adjust features through a learned compensation mechanism, maintain consistent reconstruction accuracy in different color temperatures (4000K-11200K) and indoor and outdoor scenes, and realize robust reconstruction under dynamic illumination conditions.

The invention discloses a leakage risk assessment method and system for a natural gas pipeline in a limited space, and relates to the technical field of natural gas safety. Various potential risk factors influencing leakage of the natural gas pipeline in the limited space are collected; performing expert opinion consultation and multi-round feedback iteration on the collected various potential risk factors by using a Delphi method, determining a first-level evaluation index and a key risk factor, and constructing a limited space natural gas pipeline leakage risk evaluation index system according to the first-level evaluation index and the key risk factor; and constructing a leakage risk assessment model of the natural gas pipeline in the limited space to research the natural gas pipeline in the limited space. According to the method, the hierarchical Bayesian analysis, the fault tree and the Bayesian network are comprehensively applied, complex dependency and uncertainty in data can be captured and processed, adjustment and optimization can be carried out according to different conditions such as limited space environments, pipeline types and leakage source characteristics, and evaluation requirements in different scenes are met.

This invention discloses a method for an application server to obtain configuration data in a mission-critical service system. The method involves generating and updating initial configuration data, basic configuration data, and user configuration data through a configuration management system. The basic configuration data and the initial configuration data, which includes database system information and user configuration read identifiers, are sent to the application server. The application server establishes a connection with the database system based on the database system information. It determines whether to retrieve user configuration data based on the user configuration read-on-demand identifier. If the identifier is "yes," user configuration data is not retrieved, but the connection between the application server and the database system is maintained. If the identifier is "no," all user configuration data is retrieved from the database system at once, and the connection with the database system is no longer maintained. Each business sub-module is started using the basic configuration data. The database system is used for storing and accessing configuration data.

This application discloses a high dynamic range imaging system and method for mobile inspection of photovoltaic power plants. The system includes a user control and display module, a gimbal pod module, an image acquisition module, and an image processing module. The user control and display module sends gimbal control commands and imaging parameter commands; the gimbal pod module adjusts the imaging field of view accordingly to point towards the target inspection area; the image acquisition module acquires at least two frames of raw image data with different exposure parameters and / or different gain parameters for the same scene in the pointing state; the image processing module performs normalization, underexposure / overexposure determination, and suppresses abnormal pixels from participating in fusion on the raw images, obtains a fused image based on configurable fusion weights, and performs tone mapping to generate an inspection output image for display or storage. Through the above-mentioned collaborative link and processing, abnormal pixel interference can be suppressed in scenes with local saturation caused by strong direct sunlight and reflection, balancing bright and dark information and stably outputting inspection images.

The invention provides a medical image segmentation and three-dimensional visualization method based on an improved U-Net network, and aims to improve the segmentation precision and the reconstruction effect. According to the method, a polarization self-attention (PSA) mechanism is introduced into an encoder module and a decoder module of the U-Net network, the capturing capacity of a model for global features is enhanced, and the training process of the model is optimized through an improved loss function. And dynamically adjusting network hyper-parameters, such as dropout rate, batch size and optimizer type, in combination with a genetic algorithm to realize automatic optimization of model performance. In the reasoning stage, the segmentation result is rendered by using VTK and ITK tools, a three-dimensional visual image is generated, and the space structure of the target area is visually displayed. According to the method, the representation capability of the segmentation network is enhanced, model parameters are automatically optimized, the accuracy and robustness of medical image segmentation are remarkably improved, and the method is suitable for medical scenes such as tumor boundary detection and organ recognition and has important clinical application value.

The application discloses a kind of space reconfigurable mobile flexible cable anchor point platform device and working method thereof, device includes the lifting flexible cable drive rope anchor point support column module installed on omnidirectional mobile platform, 180 ° limit fixed flexible cable drive rope anchor point support column module and two groups of flexible cable rope drive module;One group of flexible cable rope drive module in which upper double cable drive rope extends downward to be fixedly connected to robot work platform after passing through the anchor point rotating pulley assembly in lifting flexible cable drive rope anchor point support column module, another group of flexible cable rope drive module in which lower double cable drive rope continues to extend upward to be fixedly connected to robot work platform after passing through the 180 ° limit anchor point pulley in 180 ° limit fixed flexible cable drive rope anchor point support column module, working method at least two groups of the space reconfigurable mobile flexible cable anchor point platform device of the application cooperate, realize the drive to robot work platform.The application realizes the purpose that large-scale space can be reconfigured.

The invention discloses a W-band SSPP filter performance evaluation method and system based on GaAs, relates to the technical field of radio frequency and microwave communication, improves the high-precision evaluation capability of frequency deviation and phase change through multi-point sampling and parallel data processing, and solves the problem that the precision of a traditional system is limited in W-band frequency response. The interference suppression module effectively obstructs thermal noise and electromagnetic interference by using a multi-layer shielding and signal isolation technology, and calculates a signal purity index Sjch by collecting parameters such as signal total intensity Xzqd, environmental noise amplitude Znf and random noise component Rns in real time so as to improve the anti-interference capability of the system; besides, the rapid data processing module adopts parallel processing and feature compression, analyzes and compresses a series of lower parameters of frequency fluctuation amplitude Fwd and phase change rate PhR, and generates advanced real-time data, so that the data processing pressure is reduced, and the system can realize more efficient real-time performance evaluation under the high-frequency requirement.

The invention discloses a translation method and system applied between an automatic wharf and a traditional wharf. The translation method comprises the following steps that S1, a loading and unloading wharf and a non-berthing wharf are determined; s2, after the to-be-translated box is simultaneously activated in the TOS system of the non-stop wharf and the TOS system of the loading and unloading wharf and it is determined that the to-be-translated box needs to be translated, translation is started; s3, the box receiving party dispatches the trailer; when the box needs to be translated to the loading and unloading wharf, the loading and unloading wharf dispatches a trailer, the trailer travels to a non-berthing wharf operation position and carries out box loading, then the trailer returns to the loading and unloading wharf and unloads the box, translation is completed, and the box is waited to be loaded into a ship; when the box needs to be translated to the non-stop wharf, the trailer is dispatched by the non-stop wharf, the trailer travels to the operation position of the loading and unloading wharf and carries out box loading, then the trailer returns to the non-stop wharf and unloads the box, and translation is completed. The berth pressure and the loading and unloading pressure of the wharf can be relieved, and the utilization rate and the operation efficiency of in-site equipment are improved.

The embodiment of the application provides a lightweight model cloud edge collaborative self-training evolution method for an industrial scene, and belongs to the technical field of intelligent industry. The method forms a lightweight representation of a mutation period in a structured log and a window statistical manner on the edge side, and only returns key samples and key contexts when a trigger condition is met, thereby reducing the return bandwidth and return cost. At the same time, the cloud end cognitive writeback provides weak supervision and enhancement instructions for the mutation samples, so that the lightweight edge model has the feasibility of low-cost self-training. Further, through the hierarchical conflict buffer and dynamic gating mechanism, the noise samples are inhibited from damaging the iteration, and the stability after going online is guaranteed through conflict suppression and stability maintenance constraints. Finally, through the effect association of the new inference log and the backflow package, reusable continuous optimization is formed, so as to realize the reduction of edge inference misjudgment rate and the improvement of inference stability, and the real-time and lightweight deployment constraints are also considered.

The invention discloses a video and image content identifying and capturing method, which relates to the technical field of image content identification, performs multi-dimensional partitioning on each frame of image, does not perform high-resolution processing on all regions, only keeps high resolution in a high-priority region, reduces irrelevant feature interference, and improves the recognition efficiency. The method comprises the following steps: carrying out secondary partitioning on a key target in a high-priority region, capturing edges, dynamic change regions and fine feature details of a moving object in combination with a bidirectional pyramid attention BiFPN mechanism, and meanwhile, capturing information details on different levels more meticulously by adopting a multi-scale feature extraction and fusion strategy in combination with the bidirectional pyramid attention BiFPN mechanism. Parameters are automatically optimized according to the feature density and the motion state in each frame of image, the adaptability to a complex environment is enhanced, optimization can be performed according to the actual demand of the current frame feature in a static scene or a rapidly changing dynamic scene, and the method has high robustness and scene adaptability.

The application belongs to the technical field of automatic control, and particularly relates to a self-adaptive regulation and control method for a heat storage coupled plate heat exchanger unit for multi-temperature zone heat supply. The method comprises the following steps: step 1, a coupling structure of a multi-section serial plate heat exchanger unit and a heat storage unit is built, each temperature level layer interface is respectively communicated with a corresponding section through a heat charging and discharging branch, and a heat charging and discharging bidirectional regulating valve is arranged on each heat charging and discharging branch; step 2, MARL heat storage collaborative regulation and control based on an average field game is executed, one MARL intelligent body is configured for each temperature zone, and a candidate action vector corresponding to a convergence wheel of a current temperature zone is taken as a final action vector of the current temperature zone; and step 3, stable regulation and control of multi-temperature zone heat supply temperature and peak-valley heat transfer across time periods of the heat storage unit to a multi-section secondary water circuit in a current regulation and control period are completed. The application can realize the stability of the regulation and control of the multi-temperature zone heat supply temperature and the cooperation of the peak-valley heat transfer across time periods of the heat storage unit to the multi-section secondary water circuit.

The invention relates to the technical field of nuclear power, and provides a nuclear power DCS cabinet and card channel signal automatic distribution method and system, and the method comprises the following steps: obtaining to-be-distributed signal measurement points, recognizing the relation attribute of each signal measurement point, and defining a distribution rule according to the relation attribute; a distribution record table is constructed for each DCS cabinet, the distribution record table comprises a plurality of matrixes, each matrix represents one type of IO card, the column of the matrix represents one IO card, and the row of the matrix represents one IO channel; and for each function group, determining the type and the number of the signal measurement point IO cards according to an allocation rule, and dynamically filling or deleting the corresponding table position of the allocation record table to realize the update of the allocation record table until all the function group signal measurement points are allocated. Signal distribution is achieved through an automatic method, compared with an existing manual distribution mode, the design time is greatly shortened, the engineering efficiency is improved, and human errors are reduced.