4812 results about "Search algorithm" patented technology

The invention provides a space field game behavior dynamic evolution and strategy deduction optimization method and system, and relates to the technical field of space field game behavior optimization, and the method comprises the steps: analyzing historical behavior data through a deep neural network, constructing a dynamic relation graph of participants, inputting a multi-agent reinforcement learning model, and outputting a game behavior model. Then, a multi-layer game scene evolution tree is constructed, the instant income and the long-term risk are calculated in combination with a dual neural network, dynamic updating is performed by using a search algorithm of a hierarchical attention mechanism, and candidate evolution paths are generated; and finally, evaluating the candidate paths by using a strategy evaluation model constructed by a space-time diagram neural network, selecting an optimal strategy path, and generating a strategy deployment scheme implemented by stages in combination with a knowledge inference engine. According to the method, the evolution trend of the game behavior in the space field can be effectively predicted, a feasible strategy deployment scheme is provided, and the scientificity and effectiveness of strategy decision are improved.

The invention provides an intelligent fault diagnosis method for a numerical control machine tool, which is applied to a control system of the numerical control machine tool. The method comprises the steps of collecting multi-source sensor data in the operation process of the numerical control machine tool, wherein the multi-source sensor data comprises at least two of a vibration signal, a temperature signal, a current signal and a sound signal; performing preprocessing and feature extraction on the multi-source sensor data to obtain preprocessed data; based on the preprocessed data, constructing and training a machine tool dynamic model to obtain a target machine tool dynamic model; when abnormal operation of the numerical control machine tool is detected, fault state estimation is carried out based on the target machine tool dynamic model and a model prediction control method, and a fault estimation result is obtained; and based on the fault estimation result, constructing a fault evolution path by adopting a Monte Carlo tree search algorithm, and outputting a fault diagnosis result. The accuracy and efficiency of fault diagnosis of the numerical control machine tool can be improved.

The invention provides an electromagnetic interference suppression method and system for a chip, and relates to the technical field of intelligent chips, and the method comprises the steps: obtaining layout data of a target chip, and based on the layout data, extracting structure parameters and device parameters through an image segmentation algorithm, and constructing a multi-physics field coupling simulation model; the method comprises the following steps: determining an optimal excitation source corresponding to a target chip through a machine learning algorithm, calculating to obtain transient multi-physical field distribution by adopting a time domain finite element method and through an adaptive time step control strategy, extracting time-frequency domain multi-scale distribution characteristics, and constructing a spectrum fingerprint database of electromagnetic interference by utilizing a deep convolutional neural network. Generating an electromagnetic interference identification result; and based on an electromagnetic interference identification result, determining an optimal suppression circuit topology through a suppression topology search algorithm, determining optimal suppression device parameters through a multi-objective optimization algorithm in combination with electromagnetic compatibility index constraints, and generating an electromagnetic interference suppression strategy.

The invention provides an intelligent logistics optimization method, device and equipment and a storage medium, and the method comprises the steps: carrying out the multi-source fusion processing of the Internet of Things sensor data and historical business data of a multimodal transportation reloading node in a logistics process, and obtaining a spatial-temporal feature vector; performing operation adaptability evaluation on the reloading nodes by using a P median model according to the spatial-temporal feature vectors to obtain a comprehensive score reflecting the node operation condition; a Tabu search algorithm is adopted to carry out combinational optimization on operation parameters of the reloading nodes based on the spatial-temporal feature vectors and the comprehensive scores, and an initial reloading scheme is obtained; and according to the initial reloading scheme, performing multi-agent cooperative scheduling on the reloading nodes on the digital twin platform to obtain a logistics resource optimal allocation scheme. Through combination of multi-source fusion and an efficient optimization algorithm, dynamic operation analysis and collaborative scheduling of multimodal transport reloading nodes are realized, logistics timeliness and resource utilization rate are improved, and the method is suitable for a complex logistics network and a changeable market environment.

The invention provides a private domain live broadcast room hotspot prediction and adaptive routing method based on swarm intelligence, and relates to the technical field of data processing, and the method comprises the steps: constructing a dynamic interest community through a dynamic graph neural network, extracting group interaction mode features through a multi-layer graph attention network, and carrying out the group interaction mode features; the method comprises the following steps: generating time sequence correlation features in combination with an improved bidirectional gating loop unit, extracting high-order semantic features, performing hotspot prediction through a multi-task learning framework, constructing a multi-level network topology model according to a prediction result, and performing path exploration in combination with an improved heuristic search algorithm to obtain an optimal transmission path. Flow distribution is carried out through a weighted polling algorithm, a deep learning distributed load balancing controller and a congestion control algorithm are combined, the data transmission rate is dynamically adjusted, and self-adaptive routing pushing of live broadcast content is achieved.

The invention provides an intelligent incubation bin anti-interference control method and system based on error self-learning, and relates to the technical field of intelligent control, and the method comprises the steps: respectively calculating a temperature deviation value and a humidity deviation value according to an optimization parameter sequence of a temperature prediction error and an optimization parameter sequence of a humidity prediction error; performing normalized weighted summation on the temperature deviation value and the humidity deviation value, and performing dynamic scaling through a Gaussian kernel function to obtain a correction factor; dynamically adjusting the activation function slope of a neural network hidden layer according to the correction factor and a BP neural network, reconstructing the weight of the fuzzy rule base based on the numerical distribution characteristics of the correction factor, and generating the corrected weight of the fuzzy rule base; and based on the corrected fuzzy rule base weight, constructing a three-dimensional parameter adjustment curved surface, and dynamically adjusting proportion, integral and differential parameters of a PID controller through a curved surface gradient search algorithm to generate a control signal. According to the invention, the control accuracy is improved.

The invention provides a wafer factory-level real-time scheduling method in semiconductor manufacturing based on deep reinforcement learning, which relates to the technical field of semiconductors, and comprises the following steps: constructing a state space vector by collecting equipment state, batch information and process flow information based on deep reinforcement learning, inputting a pre-trained deep reinforcement learning network model, and constructing a state space vector; the model includes a value assessment network and a policy network. The model generates matching probability distribution of batches and devices, a hierarchical reinforcement learning method is adopted to decompose the distribution into two sub-tasks of batch selection and device allocation, and a plurality of candidate matching schemes are generated. And then evaluating the candidate schemes by applying a multi-target reward and punishment function, selecting an optimal scheduling scheme in combination with a Monte Carlo tree search algorithm, and issuing a processing instruction. And real-time processing data is collected in the working procedure processing process to update the state space vector, so that real-time scheduling is realized.

The invention provides an enhanced generation method based on question matching retrieval, and belongs to the field of matching generation, and the method comprises the following steps: S1, a semantic feature coding stage: carrying out real-time feature extraction and vector space mapping on a natural language query input by a user by adopting a deep neural network model, generating high-dimensional distributed representation with semantic representation capability; s2, a knowledge base intelligent retrieval stage: executing multi-dimensional semantic matching in the vectorized knowledge base based on an approximate nearest neighbor search algorithm, and screening out a candidate knowledge set highly related to query semantics through a similarity measurement function; s3, retrieval matching results are automatically associated to the structured knowledge base through the established semantic-knowledge mapping relation, the preprocessed standardized response content is directly obtained, and the response content adopts a multi-modal data organization form and comprises a structured data entity and retains a rich text expression form.

The invention discloses an electromechanical equipment noise reduction method based on machine learning, and relates to the field of electromechanical equipment noise reduction, and the method comprises the steps: collecting sound pressure and vibration signals during the operation of electromechanical equipment, and collecting the operation state data of the electromechanical equipment; sparse reconstruction is carried out on the noise features; carrying out sound source localization on the reconstructed noise features by using a beam forming algorithm to obtain a noise distribution diagram; constructing a noise reduction model based on deep learning to obtain noise signals after noise reduction; extracting the frequency of the noise signal in a preset frequency range through a peak search algorithm; analyzing the acquired running state data by using a time-varying linear predictive coding algorithm TVLPC to obtain a noise frequency offset caused by the abnormal running state of the electromechanical equipment; generating an optimal solution by using a particle swarm algorithm; and according to the optimal solution of noise control, noise dominant frequency components are extracted for separation, and a plurality of phase-reversal noise reduction control signals are obtained. Aiming at low noise reduction precision of electromechanical equipment in the prior art, the noise reduction control precision is improved.

The invention provides an APT (Advanced Persistent Threat) attack detection method based on a large language model, which comprises the following steps of: S1, extracting original system call event data from a kernel audit log of an operating system, and preprocessing the data; s2, constructing a multi-model collaborative detection architecture based on a large language model, and realizing fine-grained classification of network entities according to the preprocessed data through prompt construction, model fine tuning and a confidence scoring mechanism; s3, constructing an adaptive graph search algorithm based on multi-modal feature correlation modeling, driving attack path topology reconstruction, and realizing maximum reduction of a malicious sub-graph topology structure; s4, carrying out combination with MITRE ATTamp; the CK tactical knowledge base constructs a cyclic enhancement analysis framework, a cyclic enhancement technology is adopted to drive a large language model to execute hierarchical association reasoning, a mapping relation from malicious subgraphs to attack tactics and tactical chains is derived step by step, and finally an attack report summary and a targeted defense strategy are generated. According to the invention, APT attack detection with high accuracy and high interpretability is realized.

The invention discloses a high and steep slope dangerous rock falling intelligent monitoring and early warning method and system, and the method comprises the following steps: S1, collecting and preprocessing real-time monitoring data, and generating a standardized input sample set; s2, constructing a long-term prediction network model, and outputting a state prediction sequence of multiple time steps in the future; s3, optimizing structure parameters and training parameters of the prediction network model based on a bald eagle search algorithm; s4, executing multi-step state prediction by using the optimized model, and outputting a crack trend, a displacement trend and an abnormal probability value; s5, constructing an early warning risk scoring function, and fusing multiple prediction indexes to generate a risk scoring value; and S6, setting a multi-level early warning threshold value, outputting an early warning level, and issuing the early warning level in linkage with voice, a terminal and a platform. According to the invention, through constructing the intelligent monitoring and early warning method fusing the long-term prediction network model and the bald eagle search algorithm, accurate prediction and multi-stage linkage early warning of the high and steep slope dangerous rock falling risk are realized.

The invention relates to a large model agent interactive question and answer task decision-making method, device and equipment and a medium. The method comprises the following steps: constructing an agent strategy model based on a partially observable Markov decision-making process; acquiring an instruction input by a user, and generating an information search track by adopting the agent strategy model; constructing a decision tree of an information search trajectory by adopting a Monte Carlo tree search algorithm to obtain a preference data set labeled by a Q value; setting a large language model, and training the large language model by adopting the step-level optimization objective function according to the preference data set marked by the Q value to obtain a trained large language model; constructing a Q value model according to the trained large language model, and then outputting Q value estimation according to the Q value model; and according to Q value estimation and information search trajectory selection actions, generating a question and answer process. According to the method, the expression and generalization ability of the large model agent in the autonomous reasoning and decision-making process can be remarkably enhanced.

The invention discloses a contrast learning unsupervised cross-modal hash retrieval algorithm based on a graph attention mechanism, and relates to the technical field of cross-modal retrieval. According to the graph attention mechanism-based contrast learning unsupervised cross-modal Hash retrieval algorithm, feature extraction is performed on image and text data through a pre-trained CLIP model to obtain image modal features and text modal features, and additional positive samples are generated through data enhancement; the similarity is calculated through Euclidean distance and cosine similarity on the basis of a graph attention network, and high weight is given to positive samples and low weight is given to negative samples by comparing adjacent matrixes. The method has the advantages that the features of the image mode and the text mode can be effectively fused and mapped to the common Hash space, a graph attention mechanism can adaptively adjust the connection weight between the features of the different modes, samples with high semantic correlation are highlighted, and therefore the semantic consistency between the image and the text is ensured.

The invention provides a government affair information intelligent retrieval and generation system applying an RAG technology, and relates to the technical field of government affair information intelligent retrieval and generation systems. Comprising an information collection module, a data preprocessing module, a knowledge base construction module, an RAG core engine module, an intelligent retrieval module, an information generation module, a user interaction module, a safety and authority management module and a system monitoring and maintenance module, and all the modules work cooperatively to achieve efficient retrieval and intelligent generation of government affair information. Wherein the information acquisition module can improve the information retrieval accuracy from a plurality of government affair data sources, the query intention of a user can be accurately understood by combining an RAG technology with a knowledge base and a powerful retrieval algorithm, the most relevant content can be quickly positioned from massive government affair information, and compared with a traditional retrieval system, the accuracy of a retrieval result is greatly improved, and the retrieval efficiency is greatly improved. And the information screening time and energy of the user are effectively reduced.

The invention relates to the technical field of low-altitude aircraft navigation, and discloses a low-altitude aircraft track real-time planning method and system. The system comprises a flight situation awareness module, a track constraint calculation module, a real-time track planning module, a conflict prediction module and a track dynamic correction module. The flight situation sensing module generates a flight situation matrix through multi-source data fusion; a track constraint calculation module extracts static obstacle contours and dynamic obstacle tracks according to the static obstacle contours and the dynamic obstacle tracks, and generates a multi-dimensional track constraint set in combination with aircraft performance parameters; the real-time flight path planning module builds a three-dimensional flight path search domain by using an adaptive space division technology, and iteratively solves an optimal flight path sequence by using an intelligent search algorithm; the conflict prediction module combines the real-time dynamic obstacle trajectory to calculate the space-time proximity, and generates a conflict early warning map; and the track dynamic correction module re-draws an obstacle avoidance constraint area according to the map, and triggers local track correction. The system improves the comprehensiveness, real-time performance and safety of flight path planning, and guarantees the stable operation of the low-altitude aircraft.

The invention relates to a time delay optimized satellite network adaptive routing method and system, and belongs to the technical field of satellite communication. The method comprises the steps of multi-hop information age construction, multi-path routing, link state sensing, flow segmentation adaptive routing, dynamic routing decision and the like. A multi-hop information age model is adopted to consider the time-varying property of a link state, the real-time property of path information is accurately estimated, and the timeliness of data updating is ensured; a multi-path search algorithm predicts link occupation frequency according to ephemeris information to construct a plurality of disjoint transmission paths, so that the traffic congestion problem is relieved, and the link resource utilization rate is optimized; a flow segmentation adaptive routing algorithm adjusts the path flow distribution proportion, reduces the data packet loss rate and the information age, and guarantees the stability of time delay sensitive data transmission; according to the method, resource utilization is balanced in a high-dynamic satellite network, link congestion is effectively prevented, the overall transmission efficiency and reliability are improved, and a high-adaptability and efficient transmission scheme is provided for time delay sensitive services.

The invention discloses a ship dynamic path adjusting method combining visual navigation and radar data. The ship dynamic path adjusting method comprises the following steps: S1, collecting data; s2, carrying out pretreatment; s3, performing data fusion to generate an environment perception model; s4, combining the generative adversarial network and the space-time diagram neural network to construct an environment prediction model; s5, performing initial path planning by using a heuristic search algorithm to generate an initial path; s6, refining and optimizing the path by using a fast extension random tree algorithm to generate an optimal path; s7, generating future possible environment change according to the environment prediction model, and calculating a coexistence reserve path for each prediction scene in combination with the environment perception model; s8, when the current path is predicted to have risks in the future, switching to a preset standby path in advance; and S9, automatically generating a navigation control instruction, and transmitting the instruction to a ship automatic driving system. According to the invention, intelligent optimization of the ship path is realized through a multi-sensor fusion and environment prediction method.

The invention discloses a financial document automatic auditing method and device and a medium, and relates to the technical field of financial reimbursement auditing. The method comprises the following steps: receiving a financial document to be audited and an associated attachment document, respectively extracting a first entity set, and extracting a second entity set from unstructured content; constructing a dynamic knowledge graph state space based on the entity set, wherein the dynamic knowledge graph state space comprises an entity vector generated by an entity embedding algorithm and a relation vector generated by a relation coding algorithm; defining a reinforcement learning action space, wherein the reinforcement learning action space comprises three types of atomic operations of newly adding and deleting a triple and adjusting confidence; in combination with the real-time document flow, the historical case library and the audit result data, dynamically evolving the knowledge graph through atomic operation, and calculating a value return value of each operation; pre-judging accumulated return values of different operation sequences by utilizing a Monte Carlo tree search algorithm, and pruning a low return sequence; executing the optimized operation sequence to update the knowledge graph; and finally, based on the updated atlas, triggering a logic verification rule to generate an auditing result.

The invention discloses an intelligent substation safety measure checking method and system, and the method comprises the steps: obtaining a secondary system topological structure, equipment information and historical safety measure ticket data of a substation, and constructing a quaternary knowledge graph; according to the quaternary knowledge graph, extracting multi-modal features of the maintenance task and identifying the type of a maintenance scene by combining a memory guide reflection decision reasoning mechanism, optimizing a rule reasoning process through a distributed guide local search algorithm, and generating an optimal safety measure operation set for a specific maintenance scene; automatically identifying a correlation loop and determining a minimum safety isolation range through a memory guide decision reasoning mechanism, dynamically generating a minimum safety operation set according to the real-time state of the equipment, and adaptively adjusting operation steps; and an operation dependency relationship model is constructed in combination with unwrapping variational multi-graph representation learning and a distributed guide local search algorithm, and operation sequence compliance verification, risk level assessment and dynamic visual early warning are realized. According to the invention, accurate formulation, dynamic adjustment and risk early warning of safety measures of the intelligent substation are realized.

The invention provides an automatic driving vehicle scheduling method and device, equipment and a storage medium, and the method comprises the steps: carrying out the multi-dimensional collection and preprocessing of an automatic driving vehicle, and generating a comprehensive data set; and based on the comprehensive data set, carrying out dynamic allocation and fleet collaborative planning on loading and unloading tasks to generate a task allocation scheme. And through a hierarchical search algorithm and a multi-objective optimization algorithm, the path of the vehicle is planned and optimized in combination with the task allocation scheme and the comprehensive data set, and an optimized path scheme is generated. And according to the task allocation scheme, the optimized path scheme, the integrated data set and the vehicle real-time state data, utilizing a predictive analysis model to evaluate and deal with potential risks in the transportation process, and generating and executing a real-time scheduling instruction. According to the invention, the scheduling method of multi-dimensional data acquisition and processing, dynamic task allocation, path optimization and risk prediction is combined, the scheduling efficiency and safety of the autonomous vehicle are comprehensively improved, and efficient logistics management is realized.

The invention provides a multi-stage embedded control equipment state sensing and energy cascade scheduling system. Comprising a master control decision center module, a distributed edge embedded node module, an equipment full-dimension state sensing module, an energy dynamic optimization scheduling module, a fault prediction and self-healing control module, a cross-protocol communication interconnection module and a man-machine cooperative command module. According to the invention, through constructing a three-layer time domain control chain of edge node nanosecond-level signal processing, cloud second-level optimization scheduling and equipment hour-level strategy presetting, seamless cooperation of turbine bearing pedestal micro-vibration monitoring and a power grid peak regulation strategy is realized, and real-time wavelet noise reduction preprocessing of embedded nodes is combined with cloud LSTM life prediction. A taboo search algorithm is driven to dynamically reconstruct a power supply scheme, and the pain point of control response lag in a high-fluctuation scene is solved.

The invention belongs to the field of digital delivery, and particularly relates to a digital delivery topology mapping method and system for multi-source real-time data fusion, and the method comprises the steps: obtaining factory building distribution, equipment distribution and operation control logic and preset function block operation logic, and constructing a hierarchical clustering function mapping space through combining an association analysis and clustering algorithm; in response to a target function demand, obtaining a layered response mapping path in combination with a deep search algorithm; layered synchronous response and distributed node anomaly monitoring are realized based on the path, the three-dimensional simulation model and the display system equipment performance and the network state. Tracing abnormities based on a monitoring result in combination with a hidden Markov algorithm and a forward reasoning model, performing iterative verification after conflict resolution until the function is free of abnormities, and updating a mapping space; and adjusting the demand repeating steps to obtain a complete and updated mapping space, and realizing accurate function and picture collaboration under multi-source data fusion.

The invention provides a mining intelligent watering dust fall method and system, electronic equipment and a storage medium, and relates to the field of watering dust fall. The method comprises the following steps: acquiring multi-source heterogeneous environment sensing data; preprocessing the multi-source heterogeneous environment sensing data to obtain preprocessed multi-source heterogeneous environment sensing data, and constructing a mining area holographic environment model based on the preprocessed multi-source heterogeneous environment sensing data; based on the holographic environment model of the mining area, extrapolating and estimating the dynamic change trend of the wind dust concentration by using a space-time sequence prediction algorithm to obtain a wind dust concentration distribution prediction result of each area of the mining area in a future time period; according to the technical scheme, dust suppression is taken as an optimization target, water mist parameters and a nozzle arrangement scheme are taken as decision variables, a mining area holographic environment model and a wind dust concentration distribution prediction result are taken as constraint conditions, an optimal spraying strategy meeting dust falling requirements is solved through a heuristic search algorithm, and the dust falling effect is improved and the water consumption is reduced through the technical scheme.

The invention provides a distribution line fault location optimization method based on single-ended traveling wave location, and relates to the technical field of traveling wave detection. The method comprises the following steps: carrying out segmented modeling on a line, calculating the traveling wave propagation speed and wave impedance of each segment, and correcting the traveling wave propagation speed and wave impedance; collecting a voltage / current traveling wave signal of a detection point, and extracting a line mode component; performing wavelet transformation on the line mode component, detecting the wave head position of the initial traveling wave through a modulus maximum search algorithm, and recording the arrival time of the initial traveling wave, a corresponding first energy value and polarity; calculating a reflected wave time window based on segmented modeling parameters, screening reversed polarity waves with qualified energy attenuation, and calculating a fault distance; if no effective reflected wave is detected in the reflected wave time window, triggering a pseudo double-end mode; a result is verified through quadruple constraints; the problems of poor parameter adaptability, low signal processing precision, strong reflected wave dependence and incomplete verification in traveling wave distance measurement of a complex distribution line are solved, and the accuracy and reliability of fault distance measurement are improved.

The invention discloses a V2X-based global path planning method for a mine unmanned driving cloud control platform, and relates to unmanned driving. The V2X-based global path planning method comprises the following steps: constructing a traffic semantic map data structure # imgabs0 #; according to task issuing or operation plan adjustment, generating path request data R, and performing time constraint, resource constraint and path optimality constraint verification on the path request data R; according to the R and # imgabs1 #, adopting a heuristic search algorithm based on a graph theory to carry out optimal path search on the road topological structure, and generating a global path P containing a node sequence and driving parameters; and acquiring obstacle data detected by the vehicle end through a local sensor, performing obstacle avoidance correction through a local path optimization algorithm according to the obstacle data and the global path P, and generating a local path meeting the safety distance constraint and the path deviation constraint. According to the method, on the premise of meeting multi-dimensional coupling constraints such as time-space, priority-resource, safety-efficiency and the like, the optimal driving path dynamically adapting to the complex environment of the well industry and mining is generated.

The invention discloses an energy storage frequency modulation instruction prediction method and system based on a gravitational method, and relates to the technical field of energy storage frequency modulation prediction. Optimal parameter optimization is carried out on the VMD through an improved sparrow search algorithm; and outputting each optimal sub-sequence to obtain a final prediction result. According to the energy storage frequency modulation instruction prediction method based on the gravitational method provided by the invention, the independence of each sub-sequence after sequence decomposition is improved by evaluating the similarity between the sub-sequences, the decomposition effect is improved through the improved sparrow search algorithm and adaptive optimization of VMD decomposition parameters, each IMF sub-sequence is independently predicted through the GRU network, and the prediction efficiency is improved. The method improves the reliability and accuracy of overall prediction, and achieves better effects in the aspects of decomposition effect, reliability and accuracy.

The invention discloses a lithium ion battery state of health (SOH) estimation method based on feature optimization, and the method comprises the following specific steps: obtaining the experimental data of a lithium ion battery, extracting initial health features reflecting the degradation of the battery, and carrying out the normalization processing of the initial health features; performing screening and dimension reduction on the preliminary health features by adopting a Spearman correlation coefficient and a grey correlation analysis method, and selecting six features with the highest SOH correlation degree; using an improved fully adaptive noise set empirical mode decomposition and sample entropy method to decompose and reconstruct health features, adopting t-SNE to perform transformation and dimension reduction on sample entropy features, using k-means + + clustering analysis, optimizing feature data and dividing the feature data into a training set and a test set; establishing a mapping relation between the optimized health features and the SOH through a bidirectional long short-term memory network (BiLSTM); a sparrow search algorithm (SCSSA) fusing sine, cosine and Cauchy variation is adopted to optimize BiLSTM parameters; and constructing an SOH estimation model based on SCSSA-BiLSTM, and obtaining a battery health state estimation value.

The invention provides an electric power inspection path planning method and device and electronic equipment, and relates to the technical field of unmanned aerial vehicle electric power inspection. The method comprises the following steps: acquiring obstacle information of an electric power facility environment, wherein the obstacle information comprises the type and position of an obstacle; based on the type of the obstacle and a preset safety distance coefficient, determining a differentiated safety distance, the type of the obstacle including a power transmission line, a transformer substation, a tower and other obstacles; based on the obstacle information and the differentiated safety distance, obtaining an initial global path through a path search algorithm; based on a preset multi-objective optimization function, the initial global path is optimized, a Pareto optimal path set is generated, and the multi-objective optimization function comprises a path length objective, a safety margin objective and an electromagnetic safety objective; and determining a target global path from the Pareto optimal path set based on a preset inspection task mode. According to the invention, the inspection efficiency and adaptability can be improved while the safety is guaranteed.

The invention relates to a public data automatic classification and grading method and system, and belongs to the field of data automatic classification and grading. Comprising the following steps: constructing a classification and grading vector knowledge base; for input data to be classified and graded, similarity matching is carried out on the basis of a nearest neighbor search algorithm and the content of the classification and grading vector knowledge base, and matching information of the field to which the data belongs and the grading rule is obtained; carrying out pre-training on the large model, and then carrying out adaptive fine tuning on the pre-trained large model based on the public data classification and grading vector knowledge base; and inputting data to be classified and graded and matching information of the classification and grading vector knowledge base into the large model for automatic classification and grading, and outputting a classification and grading result. According to the method, the problem that the existing classification and classification lack an automatic method and need to depend on a large amount of manual customized services is solved, automatic public data classification and classification are realized, and the generalization and efficiency of classification and classification are improved.

The invention discloses an edge calculation method and system applied to construction site safety management. The method comprises the following steps: S1, collecting and preprocessing multi-modal data; s2, extracting and sharing risk features, and generating a preliminary risk feature map; s3, generating a local abnormal projection result by using a distributed high-dimensional abnormal mode projection module, and splicing to form a high-dimensional risk feature curved surface; s4, monitoring the data flow by using a dynamic event sensing module, identifying a high-risk event and outputting a high-risk event mark; s5, calling a dynamic risk weight tree search algorithm in a path optimization module to generate a dynamic security path; s6, generating a local risk prediction result by using the space-time hybrid prediction model; s7, generating a global dynamic prediction result through a layered space-time cooperation mechanism; and S8, providing real-time risk early warning and path optimization suggestions for construction management personnel through the mobile terminal. According to the invention, edge calculation and artificial intelligence technologies are combined, so that construction site dynamic risk identification and safety management are realized.