16results about How to "Reduce deployment" patented technology

The invention discloses a structure sensor optimal arrangement method and system based on distributed deep reinforcement learning. The method comprises the following steps: constructing a deep reinforcement learning agent based on a non-decreasing quantile function network; constructing an interactive environment of sensor optimization arrangement based on a reverse order decision theory, and defining a state, an action, a reward function and a termination constraint; establishing a structural discrete state space equation based on a modal coordinate transformation theory, and calculating a trace of an estimation error covariance matrix by using a three-step recursive estimation algorithm; based on the trace and a sensor deployment cost function, constructing an optimization function aiming at minimizing an estimation error; and finally, training an intelligent agent by adopting a distribution reinforcement learning algorithm based on Huber quantile regression, and searching an optimal sensor arrangement scheme meeting cost and modal observability constraints by maximizing accumulated rewards. According to the invention, the optimal arrangement of multiple types of sensors under the constraint of fixed deployment cost is realized, and the precision and efficiency of structural state estimation are improved.

The invention provides a log analysis method and system based on a large language model, and belongs to the technical field of log analys.The method comprises the steps that anti-fact rewriting is conducted on a to-be-analyzed log, and multiple log variants are obtained; log parameters are extracted from the log variants through the large language model to serve as intermediate variables, and the confidence coefficient of each intermediate variable is determined; performing correctness scoring on each intermediate variable based on statistical information of a historical log corpus knowledge base; determining a comprehensive score of each intermediary variable according to the confidence coefficient of the intermediary variable and the correctness score of the intermediary variable; and screening the medium variable with the highest comprehensive score as a log parameter of the log to be analyzed, and generating a log template of the log to be analyzed according to the log parameter of the log to be analyzed. According to the log analysis method, the stable semantic structure of the log can be more emphasized in the log analysis process, and the accuracy and generalization ability of log analysis are improved.

The invention discloses a black vehicle-oriented real-time point cloud fusion detection method, system and device, and a medium. The method comprises the following steps: step 1, obtaining point cloud data through a laser radar; 2, scanning the point cloud data, marking the continuous point cloud blocks with the reflection intensity of 0 as unlicensed car identifiers, and storing the unlicensed car identifiers in point cloud attribute positions; step 3, performing dual-thread compensation, performing clustering operation on the point cloud data set with the unlicensed car identifier by using DBSCAN, and performing imaging processing on a clustering operation result by using Point Pill to obtain a three-dimensional image model; forcibly outputting an unlicensed vehicle detection frame image according to an image processing result or improving the confidence coefficient as an unlicensed vehicle compensation confidence coefficient, and preferentially adopting a model result; when the two conditions do not belong to the above two conditions, performing weighted fusion on the operation result; and step 4, fusing operation results in the step 3 into a complete three-dimensional frame image. Therefore, the problem of missing detection is solved, and the detection rate and detection efficiency are improved.

The invention relates to the technical field of surveying and mapping devices, and discloses a surveying and mapping device for road planning, which comprises a support rod and a surveying and mapping device arranged above the support rod, and also comprises a mounting part arranged on the support rod; the adjusting part is mounted on the supporting rod; the supporting part is arranged on the supporting rod; the mounting part comprises a limiting assembly, and the limiting assembly is mounted on the supporting rod; the pulling assembly is arranged on the supporting rod; the limiting assembly comprises a connecting rod slidably connected to the inner wall of the supporting rod, a connecting block is fixedly connected to the top of the connecting rod, and a containing groove is formed in the connecting block. By arranging the mounting part, the problems that in the using process of an existing surveying and mapping device, fasteners need to be screwed one by one during disassembly and assembly, the steps are tedious and time-consuming, during multi-point-position circulation surveying and mapping, time-consuming for repeated disassembly and assembly and erection is long, rapid entering and transition are inconvenient, and the overall operation progress is seriously slowed down are solved.

The application belongs to the technical field of computing resource scheduling, and provides an edge device computing resource scheduling system and method based on artificial intelligence, aiming to solve the scheduling failure problem caused by the mismatch between edge device computing release prediction and real state, comprising: extracting AI predicted computing release time and real release time from historical scheduling failure data in the past period, calculating the deviation, and outputting the deviation distribution set through DBSCAN clustering with time stamp and deviation as features, analyzing the relevance of the deviation and the corresponding time stamp, constructing a time-computing deviation coupling model, comparing the current time range deviation output based on the model with the real deviation, calculating the compensation time to optimize the AI prediction when there is a scheduling risk, and generating a scheduling scheme in combination with the task demand and the real-time state of the device.

The application provides a hardware perception-based model quantization method, system, medium, product and terminal, a hybrid execution mechanism is constructed between a high-level software framework and a bottom layer hardware behavior model, only core calculation operators in a quantization calculation graph of an initial quantization model which need to be executed on target hardware are handed over to a hardware behavior model for bit precision simulation, real calculation characteristics of the target hardware are simulated in a quantization process, and other auxiliary, non-computation-intensive operators are reserved in the high-level software framework for execution, so that the simulation verification efficiency is improved, the simulation precision and the execution efficiency are considered, and the goal that the quantized model runs more efficiently and has lower precision loss on the target hardware is achieved.

This invention relates to the field of vehicle collision detection technology, and specifically discloses a vehicle collision detection method and system based on an improved dual-stream convolutional network, comprising the following steps: Step S1: Real-time acquisition of an original video stream containing the target vehicle via an onboard camera; Step S2: Preprocessing the original video stream to extract spatial frame sequences and optical flow frame sequences; Step S3: Inputting the spatial frame sequences into the spatial flow branch of the improved dual-stream convolutional network. This vehicle collision detection method and system based on the improved dual-stream convolutional network achieves a significant improvement in detection accuracy and extremely low end-to-end processing latency, providing sufficient warning and response time for active safety systems. Its dual-stream parallel processing and adaptive feature fusion design effectively balance computational efficiency and feature integrity, enabling the system to maintain a low false alarm rate in complex driving scenarios and clearly distinguish between emergency braking and real collision risks.

The application discloses a data file encryption method and device, a computing device and a storage medium. The method comprises the following steps: receiving a file uploading request of a data file, storing a plurality of data fragments of the data file in a plurality of data nodes in a distributed storage system respectively; obtaining load information of a data node in which any data fragment of the data file is stored; determining an object of performing encryption processing according to the load information of the data node in which the data fragment is stored; and if it is determined that the object of performing encryption processing is the data node in which the data fragment is stored, controlling the data node to perform encryption processing on the data fragment. Through the above method, only a part of computing resources needs to be dynamically reserved in the data node, and the data node can complete the encryption processing of the data fragment when the load of the data node is low, without loading the data fragment to an external computing node for encryption processing, so that network bandwidth resources can be saved, and the situation that the data node has too large throughput can be avoided.

The invention discloses a biogas synchronous edulcoration-CO2 electrocatalytic reduction integrated purification method, and belongs to the technical field of gas purification, a biogas purification device is adopted to treat biogas to be treated, the biogas to be treated is in full contact with an electrolyte through a microbubble aeration device, H2S and NH3 in the biogas to be treated are absorbed, and the rest of gas rises, so that the biogas to be treated is purified. The gas is in countercurrent contact with the electrolyte sprayed by the spraying head, H2S and NH3 in the gas are further removed, the gas passes through the position where the three electrodes are located, CO2 in the gas is selectively reduced into CH4 through an electrocatalytic reduction reaction, and the purified biogas is discharged into a methane collecting tank from an exhaust port I; the method is simple to operate and mild in reaction condition, purification of the biogas and purification of impurities are realized, and the purified biogas is convenient for subsequent collection and utilization.

This invention discloses a method and system for joint prediction of meteorological, wind, solar, and energy storage power based on ultra-long context, belonging to the field of power system prediction technology. The method constructs a multi-agent collaborative architecture for data cleaning, time alignment, context generation, and joint prediction, achieving automatic processing and efficient organization of historical data on wind power, solar power, and energy storage. By introducing an ultra-long context input mechanism spanning seasons and years, it overcomes the limitations of traditional model context length, achieving unified modeling and collaborative representation of multi-source data from different frequencies. Based on this, it utilizes a large time-series model to conduct zero-sample joint prediction, and combines physical constraints such as wind power curves, solar irradiance, and energy storage state of charge to construct a wind-solar-storage coupling consistency correction mechanism to optimize the prediction results. This invention effectively improves the multi-source covariate fusion capability and prediction accuracy, possessing good generalization performance and engineering adaptability.

This application discloses a method for correcting misjudgments in high-speed multi-type event detection based on LoRA, relating to the field of intelligent transportation technology. The method includes: acquiring real-time event information on the highway, identifying it using the YOLO algorithm, and outputting the original preliminary event detection results and environmental parameters; standardizing the preliminary detection results and extracting semantic and scene features of the event targets from the preliminary detection results to obtain standard preliminary detection results; performing secondary verification and misjudgment correction using a LoRA-optimized large model based on the standard preliminary detection results to obtain the event detection results; uploading the event detection results to the road segment central monitoring platform, and retaining misjudged, missed, and new scene samples for iterative optimization. This application aims to resolve the contradiction between the low reliability of event detection in complex highway environments and the inability to deploy high-precision large models at low cost and high efficiency under existing hardware conditions.

This application relates to the field of performance testing technology, providing a simulation testing method, apparatus, device, and storage medium. The method is applied to an electronic device and includes: receiving an interface call request sent by a test system, the interface call request being used to request a target service system to execute a target business function; processing the interface call request using a simulation system within the electronic device to generate simulation results, the business processing logic of the simulation system being consistent with the business processing logic of the target service system; and finally sending the simulation results to the test system. This method not only reuses the business processing logic of the target service system without relying on reverse engineering, thus effectively reducing development cycle and adaptation costs, but also effectively avoids the testing limitations caused by the closed nature of the target service system in related technologies, achieving full-process testing independent of the target service system.

This invention discloses an indoor data acquisition and positioning method, positioning system, and storage medium based on a neural network algorithm. It employs a multi-terminal collaborative architecture to achieve a closed-loop positioning process for small-scale indoor scenes. The system uploads indoor planar images via a front-end device and performs scale calibration to establish a mapping relationship between pixel coordinates and physical space. In acquisition mode, it acquires multi-source signals in real time and expands the effective sample. After back-end data processing and storage, the algorithm extracts signal features through a neural network, performs adaptive weight allocation and feature fusion, and trains to generate a fingerprint database model. In positioning mode, it performs signal matching and coordinate calculation based on this model, and finally, the positioning results are visualized and displayed on the front end. This invention requires no dedicated acquisition equipment or base station deployment, and has the advantages of high positioning accuracy, low deployment cost, convenient maintenance, and user-friendly operation, making it suitable for various small-scale indoor positioning needs.

The invention discloses a power distribution network equipment fault classification method based on adaptive period selection, and the method comprises the steps: carrying out the preprocessing of an original signal of a power distribution network, and generating standardized time series data; dynamically extracting periodic characteristics of the signals from the standardized time sequence data by using FFT and EMA; integrating the periodic features and the standardized time sequence data based on a lightweight periodic module to generate an optimized feature vector; processing the optimized feature vector by adopting bidirectional position coding to obtain an optimized feature vector after time dependence enhancement; and carrying out classification processing on the optimized feature vectors through a classifier, and finally outputting a fault category with the highest probability. According to the invention, the efficiency, accuracy and robustness of intelligent power grid power distribution equipment monitoring can be greatly improved, the problems of fault classification instantaneity and reliability in complex power grid environments (such as cities, villages and industrial parks) are solved, the maintenance cost of a power grid is reduced, and the power supply stability is improved.

The invention discloses a method and a system for eliminating NAT (Network Address Translation) limitation by transforming external TCP (Transmission Control Protocol) connection of an enterprise into QUIC (Query Unified Integrated Circuit) connection, and belongs to the technical field of computer networks. According to the method, client access is guided to an intranet agent through DNS redirection; the client establishes standard TCP connection with the intranet agent; the intranet agent maps the TCP stream data to the QUIC connection or stream actively sent to the public network agent as required; and the public network agent restores the TCP request and relays the TCP request to the target service, and returns in response to the original path. According to the invention, a segmented optimization architecture of the intranet TCP and the public network QUIC is creatively adopted, so that the dependence of the traditional scheme on the long connection of the public network TCP is fundamentally eliminated, the core crux of unstable NAT penetration is solved, the inherent false dead connection problem of the TCP protocol is thoroughly avoided, the completely transparent and zero-transformation access of the client and the intranet service is realized, and the access efficiency of the client and the intranet service is improved. And the system has excellent stability, safety and deployment convenience.

The application belongs to the technical field of industrial control, and discloses a coal conveying system intelligent monitoring system for thermal power plants based on multi-source sensing and edge computing; the system comprises the following steps: collecting multi-modal sensing data through a heterogeneous sensor array, using a physical constraint correlation model to perform deep collaborative extraction on cross-modal features such as vision, acoustics, vibration, temperature and current, and dynamically identifying equipment abnormal patterns accordingly; and introducing a hierarchical fusion analysis architecture to provide multi-level confidence guarantee for fault diagnosis. Through a simulation deduction mechanism driven by digital twinning, the system response can be pre-evaluated before the execution of the control strategy, the optimal solution with the best comprehensive performance is selected, and the reliability of the control decision is ensured; the application also realizes an incremental correction mechanism for real-time feedback of state deviation, avoids control deviation caused by model mismatch, and enables each abnormal symptom of the coal conveying link to be accurately captured and adaptively handled, thereby significantly improving the system safety and the intelligent level of operation and maintenance.