1150 results about "Edge based" patented technology

The invention discloses an urban rainwater pipe network blockage risk early warning method and system based on edge calculation, and relates to the technical field of urban drainage monitoring. The problems that existing pipe network blockage detection lags behind, and the early warning precision is insufficient are solved. Dynamic hydraulic parameters and sediment migration state data of a pipe section are collected in real time through edge calculation equipment deployed at a pipe network node, the hydraulic state deviation rate is calculated, and the local blockage risk is rapidly recognized; when the deviation rate exceeds a threshold value, a dynamic sensing network is established by the trigger nodes, and a pipe network hydraulic topological relation is established by integrating the liquid level and flow velocity characteristics of the upstream and downstream nodes; executing distributed collaborative analysis based on the topological relation, identifying an abnormal attenuation area, calculating a sediment dynamic equilibrium index, and generating a blockage diagnosis parameter; and further combining real-time rainfall intensity to predict an overflowing capacity attenuation curve, generating a multi-stage early warning instruction according to an attenuation slope, and distributing the multi-stage early warning instruction to an operation and maintenance terminal, thereby realizing accurate early warning and active regulation and control of the blockage risk of the rainwater pipe network.

The invention provides a real-time link anomaly detection and sampling method and device based on edge AI driving, and is applied to the technical field of artificial intelligence. Segmenting and aggregating the original link tracking data in each time window through a dynamic time window division mechanism to generate a statistical feature vector of link topology in the time window, the original link tracking data including a link topology relationship, a service calling level and a performance index; inputting the statistical feature vector into an anomaly detection model obtained by pre-training for analysis to obtain a path anomaly probability score; and dynamically adjusting the sampling strategy of the next time window according to the path anomaly probability score. Through edge AI calculation nodes, dynamic time window division, segmented aggregation and dynamic adjustment of a sampling strategy, the real-time performance of anomaly detection and the reliability and practicability of anomaly detection are improved, adaptive sampling is realized, and the sampling precision and efficiency are ensured.

The invention provides an underground equipment fault real-time diagnosis method and system based on edge calculation, and relates to the technical field of coal mine safety production, and the method comprises the steps: collecting multi-modal data through a distributed sensor network, extracting multi-scale time sequence features, projecting the features to a Lie group manifold space, constructing a coupling mapping relation matrix, obtaining fusion features, and carrying out the real-time diagnosis of an underground equipment fault; and constructing a causal directed acyclic graph based on a topological connection relationship and a Granger causal coefficient, executing Bayesian probabilistic reasoning, determining an execution strategy in combination with entropy similarity matching, and performing deep time-frequency analysis and causal chain verification. High-precision real-time diagnosis of equipment faults in an underground complex environment is realized, and the fault early warning accuracy is improved.

The invention discloses a multi-scale SAR (Synthetic Aperture Radar) image ship detection method and system based on edge enhancement and diffusion denoising, and mainly solves the problems that the existing SAR ship detection method is sensitive to noise and poor in small target feature extraction capability. According to the implementation scheme, the method comprises the following steps: obtaining an SAR image, carrying out standardized preprocessing, inputting the SAR image into a deep convolutional neural network, extracting a multi-scale feature map, and carrying out dynamic channel fusion enhancement on a low-layer feature map in the multi-scale feature map to obtain a fused high-quality feature map; performing differential edge enhancement on middle and high-level feature maps in the multi-scale feature map, and inputting the enhanced feature map and the fused feature map into a diffusion model detection head for training; and inputting a to-be-detected SAR image into the trained diffusion model detection head, outputting a preliminary target bounding box and a category confidence coefficient, performing score screening and non-maximum suppression operation on the preliminary target bounding box and the category confidence coefficient, and generating a final ship target detection result. According to the method, the precision and robustness of SAR image ship detection are remarkably improved, and the method can be used for ocean monitoring and military reconnaissance.

The invention provides a low-airspace public route construction method, device and equipment, and relates to the technical field of aircraft airspace route planning. The method comprises the following steps: determining a plurality of target candidate nodes corresponding to a city based on ground traffic topological characteristics of the city corresponding to a low airspace and spatial distribution characteristics of low-risk ground features; performing triangulation on the plurality of target candidate nodes to obtain an initial edge set; wherein the initial edge set comprises a plurality of initial edges; performing sparse processing on the plurality of initial edges based on a greedy tensor graph algorithm, and determining a plurality of target edges from the plurality of initial edges; wherein the length of the shortest path between the two nodes of the target edge is greater than the greedy tensor coefficient times of the Euclidean distance between the two nodes; and constructing a low-airspace public route based on the plurality of target edges. By adopting the technical scheme provided by the invention, the utilization efficiency and safety of the low airspace can be effectively improved by constructing the unified low airspace public route.

The invention discloses an intelligent accompanying robot system, and the system comprises the following modules: a multi-mode interaction module which is composed of a voice recognition unit, a voice synthesis unit, an emotion recognition unit, and a visual recognition unit, and is used for collecting the voice, facial expression, motion, and environment information of a user; the localized AI decision module is used for deploying a lightweight DeepSeekR1 large language model based on an ESP32-S3 edge computing chip, performing real-time processing on the multi-modal data and generating a social guidance strategy and an emotion intervention instruction; the data management module comprises a user behavior database and a privacy protection unit and is used for desensitizing the data and realizing sensitive data isolation through local storage; according to the method, the single-person intervention cost is reduced, and the time consumption of manual scene simulation is reduced.

The invention discloses a mine roadway deformation data real-time analysis system based on edge calculation, which relates to the technical field of intelligent sensing and data analysis fusion, and comprises a data acquisition module for acquiring roadway deformation initial data through a sensor, simulating a deformation field evolution process by adopting a finite element analysis method, and analyzing the deformation field evolution process; the feature extraction module is used for extracting key stress distribution parameters from the non-uniform field distribution features, if the parameters exceed a preset threshold value, the key stress distribution parameters are marked as a risk concentration area, a monitoring position candidate set is determined, and the signal quality evaluation module is used for evaluating the monitoring position candidate set according to the monitoring position candidate set. Acquiring a signal-to-noise ratio and an integrity data quality index of a sensor signal, and performing priority grouping on the monitoring points through a clustering algorithm to obtain a dynamic priority evaluation result; according to the mine roadway deformation data real-time analysis system based on edge calculation, intelligence, real-time performance and high reliability of mine roadway deformation monitoring are achieved.

The invention provides a complex curved surface component mechanical arm measurement path planning method based on curved surface curvature self-adaption. The method comprises the steps that S1, the edge contour of a to-be-measured model is recognized; s2, based on the edge contour, generating a curved surface curvature adaptive scanning viewpoint set; s3, planning a scanning path based on the scanning viewpoint set through the mechanical arm scanning path optimization model, and decoupling a path planning problem into a measurement viewpoint scanning sequence optimization sub-problem and a mechanical arm scanning posture joint path optimization sub-problem; and S4, solving the measurement viewpoint scanning sequence optimization sub-problem through a preset algorithm model, and determining an optimal scanning viewpoint sequence. And S5, based on the optimal scanning viewpoint sequence, the mechanical arm scanning posture joint path optimization sub-problem is solved through a preset algorithm model, and a mechanical arm joint motion path is generated.

The invention discloses a satellite-ground cooperative processing method and system based on edge computing, and the method comprises the steps: constructing a satellite-ground integrated cloud edge-end cooperative architecture which comprises a satellite terminal layer, a ground edge layer and a cloud computing layer, connecting a satellite end with a ground cloud center through a distributed foundation edge service station, and achieving the unified scheduling of satellite-ground resources and task cooperation; establishing a task processing position decision model based on task data volume, link bandwidth and computing power, dynamically selecting a satellite end, an edge station or a cloud center for processing in combination with response time constraint and energy consumption cost, and optimizing time delay and energy consumption; the method comprises the following steps: constructing a geoscience sky intelligent learning framework, performing ground center offline training to optimize a deep learning model, performing pruning quantification to generate a lightweight model, issuing the lightweight model to a satellite end, performing on-line reasoning by the satellite end, and collecting and returning data to form a closed-loop learning process; an offline-online cooperation mode is introduced, high-real-time tasks are preferentially processed online, non-real-time tasks are cached and downloaded in batches, and a strategy is dynamically adjusted to improve the system efficiency and the resource utilization rate.

The invention provides a trailing suction dredger loading operation parameter optimization real-time recommendation system and method, and relates to the technical field of trailing suction dredger dredging engineering.The system comprises a data collection layer, a real-time calculation layer, an intelligent recommendation layer and a feedback control layer, the data collection layer is used for collecting trailing suction dredger loading operation parameters in real time, and the real-time calculation layer is used for calculating the real-time calculation layer; the real-time calculation layer can calculate energy consumption and yield in real time based on edge calculation equipment, the intelligent recommendation layer can generate a Pareto optimal solution set through a multi-objective optimization algorithm, and the dynamic weight adjustment module adjusts energy consumption and yield weight coefficients in real time according to the construction stage. The feedback control layer issues the collected loading operation parameters of the trailing suction dredger to an execution mechanism in real time through a PLC, and monitors the operation state of equipment to update a historical database, so that collaborative optimization of energy consumption and yield can be achieved, and the optimization precision is continuously improved through historical data.

The invention discloses a mobile robot accurate docking method based on edge calculation, and aims to solve the problems that the dynamic docking precision is reduced and the collision risk is increased due to micro-motion or drifting of a target station. According to the method, unified time reference alignment is carried out on data of a camera, a laser radar, an inertial measurement unit, an ultra-wideband range finder, a station encoder and a programmable logic controller at an edge node, and a three-dimensional special Euclidean group equivariant multi-source fusion network is used for outputting relative pose estimation and covariance; the estimation in the time window is further used as a condition to be input into a conditional diffusion short-time prediction model to obtain a time-varying mean value and a time-varying covariance, an anisotropic probability tube is constructed, and prediction-measurement joint correction is carried out based on a score function; scenarized opportunity constraints are constructed under correction probability tube constraints, a tubular nonlinear model is adopted to predict, control and solve a reference trajectory, an actuator command is generated in combination with depth visual servo and compliance control, and the technical effects of high precision, robustness and safe docking under the station dynamic disturbance condition are achieved.

The invention relates to the technical field of online approval management, and discloses an online employee approval management system based on an edge calculation model. An employee terminal data acquisition layer of the system captures a multi-modal examination and approval data flow based on a priority dynamic focusing mechanism, generates an examination and approval behavior characteristic thermodynamic diagram through an edge characteristic pyramid network, and divides an abnormal region; the examination and approval knowledge graph construction layer loads a domain rule base, extracts an entity relationship through semantic dependency analysis, and constructs an examination and approval rule path graph with a weight; the map path cross validation layer synchronizes an edge node clock, and generates approval risk confidence through multi-head map attention network fusion data; the dynamic strategy optimization layer converts the risk confidence into an executable strategy and issues the executable strategy to an edge execution engine; and the approval efficiency feedback layer monitors the flow state and generates an efficiency index to dynamically optimize a data acquisition strategy. According to the system, precision, high efficiency and dynamic optimization of approval management are realized.

The invention discloses a foundation pit support deformation real-time monitoring method based on edge calculation, and aims to solve the problems of multi-node time synchronization and clock drift compensation caused by unavailability or poor quality of satellite navigation and precise time protocols in an underground or shielded environment. According to the method, through event dictionary learning, partition time slot scheduling and safety coding micro excitation time synchronization, matched filtering decoding, multi-scale arrival time estimation, paired time difference robust fusion, frequency priority and phase speed limit correction and network disconnection time holding, the time synchronization efficiency is improved; the technical effects that multi-source data alignment, deformation field reconstruction and graded early warning are completed on the unified virtual reference time scale, and high-precision synchronization and online drift compensation are obtained without depending on the external time service condition are achieved.

The invention discloses a high-precision map construction method and a related device, and relates to the technical field of automatic driving. The high-precision map construction method comprises the steps that image data representing the surrounding road environment of a vehicle is obtained, a semantic topology network diagram containing nodes and associated edges is constructed based on the image data, the nodes are information units which are extracted from the surrounding road environment of the vehicle and carry semantic and spatial features, and the associated edges are associated with the nodes. The associated edge represents a node relationship of spatial and semantic dimensions between the nodes. Then determining a target anchor point meeting a space reference condition from the semantic topology network diagram, then mapping the target anchor point to a geographic coordinate system in combination with the real-time pose of the vehicle, and finally determining the spatial position of each node according to the relationship between the target anchor point and the node in the geographic coordinate system so as to generate a high-precision map. Finally, real-time and accurate road environment structured information is provided for automatic driving, and safe and efficient driving of the automatic driving vehicle is guaranteed.

The invention discloses a power grid project cost deviation intelligent auditing method and system, and the method comprises the steps: carrying out the intelligent analysis of a cost document, carrying out the layout analysis and semantic analysis of the cost document, and extracting an entity and a relation to generate structural data; construction of a cost knowledge graph: constructing a knowledge graph with entities as nodes and relationships as edges based on the cost ontology model; cost correlation deviation detection: learning node features by using a graph neural network, calculating correlation weights through an attention mechanism, and identifying abnormal nodes and abnormal propagation paths; performing cost deviation attribution analysis, performing reverse traversal and contribution degree calculation based on a causal influence graph, and positioning direct reasons and indirect reasons of deviation; a cost auditing conclusion is generated, and auditing opinions are generated in combination with an auditing And cost auditing knowledge precipitation: precipitating the confirmed deviation case to a rule base to realize knowledge accumulation and reuse. The problems that a traditional auditing method is insufficient in correlation deviation detection capacity, attribution analysis depends on artificial experience, and auditing knowledge is difficult to precipitate and reuse are solved.

The invention provides an end side AI agent abnormal data detection method and device based on edge computing, and relates to the technical field of data processing.The method comprises the steps that real-time collection is conducted on an end side through an AI agent, and a plurality of time sequence data streams are obtained; a time sequence-frequency spectrum self-adaptive joint feature extraction module arranged in the AI intelligent body is used for dynamically analyzing the multiple time sequence data streams, edge calculation is used for carrying out anomaly analysis and recognition on the joint feature vector, an initial early warning signal is generated, inversion is carried out, an inversion result is generated for cyclic verification, and the result is fed back to the AI intelligent body for updating and optimization. And constructing an abnormal data detection report. The technical problem that in the prior art, due to the fact that abnormal data detection is low in reliability and low in response speed, real-time abnormal data is difficult to efficiently and accurately recognize is solved. The technical effect of efficiently and accurately detecting and identifying the abnormal data in a real-time environment is achieved.

The invention discloses a customer service telephone traffic platform real-time scheduling data optimization method based on edge computing, and relates to the technical field of communication. The customer service telephone traffic platform real-time scheduling data optimization method based on edge computing comprises the following steps: S1, collecting resource data and state monitoring data in an edge computing environment for preprocessing, and constructing a telephone traffic scheduling database after storing the resource data and the state monitoring data; s2, carrying out stability analysis through resource load, response time and preheating progress; s3, fusing task execution, data synchronization, business adaptation and node stability, and evaluating the preheating state of the cold seat; s4, generating a traffic adaptation index through the resource load, the preheating state and the task queue data; and S5, monitoring the resource load, the task queue and the response time of the node in real time to form a load feedback signal, optimizing a task scheduling strategy and generating a load report. The problems that resource scheduling of a customer service telephone traffic platform is insufficient and the preheating state of a cold seat is indefinite under the conditions of high load and high concurrency are solved.

The invention discloses a ventilator fault detection and early warning method based on edge calculation, and particularly relates to the field of fault detection.The ventilator fault detection and early warning method comprises the steps that a multi-dimensional sensor is deployed to collect mechanical vibration, acoustic characteristics, electrical parameters and environment working condition data, and 28 structured feature sets are formed through preprocessing and feature extraction; constructing four types of adaptive feature functions, and converting high-dimensional features into fault reliability or environmental interference correction coefficients; fusing multi-dimensional evidences by adopting an improved D-S evidence theory, resolving conflicts and judging four states of normal, slight fault, serious fault and uncertainty; and finally, through a cloud-edge collaborative architecture, the cloud end iteratively optimizes characteristic function parameters and fusion rules based on a fault case library, and safely issues the characteristic function parameters and the fusion rules to an edge end. The method depends on the low delay advantage of edge calculation, combines machine learning and evidence theories, effectively reduces misjudgment caused by environmental interference, realizes accurate fault identification and early warning, and is suitable for intelligent operation and maintenance of the ventilator under multiple working conditions.

The invention discloses a vibration event early warning diffusion method based on edge calculation, and relates to the technical field of computer application, and the method comprises the following steps: constructing a topological phase coordinate under a unified time baseline, playing back a historical track of early warning diffusion based on the topological phase coordinate, recognizing a continuous path segment with a closed trend in the track, and carrying out the early warning diffusion. Outputting a loop suspicious region; a fingerprint code of early warning information is constructed on the basis of a loop suspicious area, a three-dimensional feature set containing time sequence features, path sequence features and phase sequence features is extracted, and a corresponding loop suspicion degree spectrum is generated. According to the method, through space-time modeling, path fingerprint construction, causal discrimination and duplicate removal mechanisms, accurate identification and repeated suppression of a vibration early warning information diffusion path are realized, dynamic threshold regulation and reverse intervention are combined, loop closed-loop treatment and topology self-healing are realized, and early warning accuracy, timeliness and system stability are remarkably improved.

The invention discloses a finance and taxation data analysis method, device and equipment based on a directed acyclic graph and an intelligent agent and a medium, and relates to the technical field of finance and taxation inspection, and the method comprises the steps: constructing a target directed acyclic graph which takes knowledge blocks as nodes and dependencies as directed edges based on a historical finance and taxation data set and natural language description of the knowledge blocks; performing hierarchical division according to the in-degree of each knowledge block in the graph, and generating a reference task plan by using a target tool list based on each knowledge block in different hierarchies, a thinking chain guide instruction and a cue word generated by an example; controlling the intelligent agent to execute the planning, and packaging the reference task planning according to the obtained execution result set and the log to obtain a basic task planning template; utilizing the current finance and tax data and the current to-be-analyzed demand to finely adjust the context parameters and the analysis range in the template to obtain a target task plan; and analyzing the current finance and tax data set based on the plan to generate a target analysis result. And the finance and tax data analysis consistency and reliability are improved.

The invention discloses a collaborative secret state task matching method based on edge calculation in mobile crowdsourcing. The method comprises the steps of system initialization and key generation, requester-cloud platform registration authentication, worker-edge server registration authentication, login authentication and attribute submission, requester task issuing, cloud platform task delegation, secret state matching, worker task content decryption, worker answer submission, answer forwarding and decryption. According to the invention, cooperative authentication of workers and requesters and precise task matching based on attributes are realized under an edge-cloud architecture. A fuzzy extractor and an authentication encryption method with associated data are introduced, so that low-cost user local authentication and registration and authentication of a joining system are realized, the security is improved, and the calculation cost is reduced. Through function hidden inner product encryption and a Paillier cryptographic algorithm, task-worker matching and task answer submission are realized in a ciphertext state, and the requirements of a mobile crowdsourcing application scene with high safety, high privacy and dispersed cost are met.

The invention discloses a power distribution network short-circuit parameter real-time monitoring method, system and equipment based on edge calculation and a medium, and belongs to the technical field of power system monitoring and protection, and the method comprises the steps: collecting data information in real time at each monitoring node of a power distribution network, generating a state vector reflecting a node operation state, and inputting the state vector to an edge calculation unit, processing through an internal first calculation model to obtain a power grid fault evaluation result of a corresponding node, and compressing and uploading the power grid fault evaluation result to a cloud; based on the uploaded data and the system operation state, a system performance evaluation result is generated in real time, and an adaptive optimization algorithm is used for dynamically adjusting internal model parameters of the edge calculation unit. According to the method, the composite algorithm model fusing feature extraction and parameter calculation is deployed at the edge node, the problems that a traditional centralized processing mode is large in data transmission delay and central node calculation pressure is concentrated are solved, and the technical effect of reducing bandwidth occupation of a backbone communication network is achieved.

The invention is suitable for the technical field of artificial intelligence, and provides a robot visual navigation optimization system based on edge computing, the system comprises a visual sensor module, an edge computing unit, a navigation algorithm module, a communication module and a robot executor, the visual sensor module is used for collecting high-definition images or video streams, and the edge computing unit is used for processing the high-definition images or video streams; the edge calculation unit is responsible for locally executing a visual navigation algorithm, the navigation algorithm module is deployed on the edge calculation unit, and the navigation algorithm module is used for executing a visual odometer, synchronous positioning and map construction, environment perception, obstacle detection, local path planning and motion control; and the communication module is used for carrying out model updating, task scheduling and non-real-time data uploading with a cloud. Complex visual data processing and navigation decision are sunk to the robot body or near-end edge equipment, so that the navigation precision and the response speed of the robot in complex and diversified environments are remarkably improved, and the energy consumption efficiency is optimized.

The invention relates to the technical field of log detection, in particular to a real-time anomaly detection and tracing method and system for a cloud storage log, and the method comprises the following steps: calculating an authentication failure proportion based on a user identifier and an internet protocol address, constructing a proportion change sequence, comparing an abrupt change threshold, screening candidate sections, fitting a slope, and aggregating anomaly records; calculating a fluctuation difference sequence, extracting a causal fracture node, recombining a subsequence graph, extracting a cross-node record and correcting a timestamp, connecting a transmission edge based on the timestamp, and accounting time consumption to construct a traceability path. According to the method, the serious defect that a fixed threshold value cannot sense system load fluctuation is overcome by constructing a dynamic proportion change slope, a causal fracture node is established to drive log atlas recombination so as to break an information barrier of a cross-node distributed environment, and a median function is used for correcting time migration to eliminate disorder interference caused by clock asynchronization; and the accurate risk tracing is realized by connecting the correction timestamp with the access transmission edge.

The invention discloses a laboratory detection equipment intelligent management system based on edge calculation and reinforcement learning, and relates to the technical field of equipment intelligent management, the system comprises a multi-dimensional data acquisition module, a model training and feature library construction module, a deviation degree research and judgment module, a model optimization module and an interaction and execution module; according to the invention, through integration of the multi-dimensional data acquisition module and the model training and feature library construction module, all-directional precision monitoring of the operation state of laboratory detection equipment is realized, and various feature data in the operation process of the equipment can be captured in real time through the multi-dimensional sensor array; the model training and feature library construction module uses a deep learning model of a CNN-LSTM mixed structure, combines historical operation data and fault cases of equipment, calculates each feature weight, and dynamically constructs and updates a health state feature vector library of the equipment, thereby improving the accuracy and timeliness of equipment state evaluation, and improving the reliability of equipment state evaluation. Therefore, managers can find potential faults in advance and take preventive maintenance measures.

The invention relates to the technical field of computer task scheduling, in particular to a server integration system based on edge computing, which comprises a topological path delay analysis module, a multi-dimensional priority mapping scheduling module, a cross-architecture performance conversion module, a dynamic priority rearrangement module and a task migration optimization module. According to the method, more dynamic and flexible task scheduling is realized by accurately analyzing communication delay and network topology characteristics between edge nodes and based on comprehensive evaluation of node performance and delay weight, the intelligent level of task allocation is improved, and the limitation of static load detection is effectively avoided; the adaptability of the system to inter-node communication delay and execution time offset is enhanced, a task migration strategy is optimized, reasonable allocation and efficient execution of tasks among heterogeneous nodes are ensured, performance waste caused by improper resource scheduling is reduced, the utilization rate of resources and the overall performance of the system are improved, and the service life of the system is prolonged. And the dynamic response capability of task scheduling in the edge computing environment is enhanced.

The invention provides an industrial single-chip microcomputer automatic control method based on time sequence event triggering, and the method comprises the steps: carrying out the multi-period sampling and time window division of an original signal inputted by an industrial field sensor or a switching value, and extracting the time distribution characteristics of the signal; performing signal stability judgment and pseudo edge elimination based on the signal time distribution characteristics, and outputting a stable edge event set; performing event confirmation delay and multi-level logic judgment on the stable edge event set to generate a preliminary time sequence trigger signal; environment characteristic data are collected, real-time feedback of an actuator is obtained, and system judgment parameters are dynamically corrected; and generating a control command and verifying an execution result based on the preliminary time sequence trigger signal and actuator feedback, the method can dynamically distinguish real signals and noise under vibration interference, simultaneously ensures the real-time performance and trigger reliability of the system, and solves the problems of insufficient edge detection stability and false trigger phenomenon caused by mechanical vibration.

The invention relates to the technical field of unmanned aerial vehicle autonomous reconnaissance, in particular to an unmanned aerial vehicle reconnaissance decision-making method, device and equipment based on edge calculation and a medium, and the method comprises the steps: obtaining the multi-dimensional data of an environment and a target through an optical camera, an infrared thermal imager and a radar carried by an unmanned aerial vehicle; target identification and strategy reasoning are carried out on the multi-dimensional data, a target type and attributes are identified from the multi-dimensional data through a lightweight convolutional neural network, and a battlefield situation is obtained in advance to generate a strike strategy; obtaining a threat level and a task priority, and dynamically adjusting the flight path of the unmanned aerial vehicle and the strike strategy according to the threat level and the task priority; and obtaining a strike result corresponding to the strike strategy, analyzing the strike result in real time, judging a target destroy state through an airborne sensor, and updating a subsequent task strategy. According to the invention, the autonomy of the unmanned aerial vehicle system is improved.

The invention discloses a low-altitude aircraft intelligent identification system based on an edge network, and belongs to the technical field of intelligent identification. Comprising a data acquisition module used for acquiring time sequence sensor data; the knowledge base construction module is used for constructing a physical rule knowledge base; the penalty value generation module is used for generating a physical violation penalty value; the model generation module is used for obtaining a physical rule embedded model; and the result output module is used for outputting an aircraft intelligent identification result conforming to the physical motion law. According to the method, the physical rule knowledge base is introduced into the edge network, and the lightweight model is trained by using the dual-target collaborative loss function, so that the effect of still outputting a physically consistent aircraft recognition result under the condition of limited computing power is achieved; the problem that in the prior art, a marginal small model generates jumping, over-limit or abnormal tracks due to lack of motion law constraints is solved.

The invention relates to the technical field of power distribution network dispatching, in particular to a power distribution network dispatching optimization method and device. Constructing a dynamic digital twin of the power distribution network to be dispatched, and reversely correcting a line impedance parameter and an equipment efficiency coefficient in combination with a twin simulation result; multiple scheduling strategies are rehearsed in parallel, and the optimal scheduling strategy is selected through weighted scoring; generating a multi-energy flow collaborative scheduling instruction set according to the optimal scheduling strategy; dynamically distributing a control task by utilizing an edge node; and performing differentiated load response in combination with the power consumption behavior classification of the user. Space-time synchronous acquisition of electricity-heat-gas data is realized by deploying a multi-energy-flow sensor group, a multi-energy-flow collaborative scheduling instruction is generated by combining reverse parameter correction and scene rehearsal optimization of a dynamic digital twin, and dynamic resource allocation is realized based on an edge node dynamic allocation control task and load classification response. The new energy consumption rate capability is obviously improved, and the carbon emission intensity is reduced.