202 results about "Scene analysis" patented technology

The invention relates to the technical field of data analysis, in particular to an operation decision intelligent analysis method and system based on cross-domain data fusion. The method comprises the following steps: firstly, based on an enterprise multi-domain ontology knowledge base, performing entity identification and relation mapping on heterogeneous data from different business systems through a semantic mapping-based multi-source heterogeneous data dynamic fusion algorithm, and establishing a unified data model; then, a causal reasoning and deep learning fused hybrid intelligent decision engine is adopted to analyze and process the model; then, a multi-level causal relationship network among business variables is constructed through a causal relationship discovery algorithm by utilizing an analysis result of the hybrid intelligent decision engine, and an adaptive business scene analysis model based on reinforcement learning is used to dynamically adjust an analysis strategy according to business environment changes; generating a Pareto optimal decision scheme set through a multi-objective optimization algorithm, and outputting operation decision suggestions; according to the invention, the comprehensiveness and accuracy of intelligent analysis of enterprise operation decisions are improved.

The invention discloses a big data platform asset intelligent sensing method based on LLM and a customizable MCP, and the method comprises the steps: defining a standard resource library of a model context protocol, constructing a model context protocol server, integrating a plurality of scanning tools, and carrying out the automatic adaption; receiving a natural language demand input by a user, and calling a pre-trained LLM model; the scene analysis module converts a natural language demand into a standardized scene description object; the intelligent scanning module is used for describing an object according to a standardized scene, generating an optimal tool execution chain, executing a complete scanning task, obtaining a task scanning result and unifying formats of heterogeneous data in the task scanning result, and the conflict resolution module is used for removing conflicts; the multi-source data association module fuses network topology resources and flow data captured by a probe to construct a dynamic asset atlas including asset attributes, service dependence and vulnerability information, and the report generation module generates a structured report. The method can provide a global risk perspective and decision support.

The invention relates to the field of image processing, and discloses an object monitoring method and device based on multi-source data and a storage medium, and the method comprises the steps: synchronously collecting multi-channel video streams, environment parameters and equipment position information, and carrying out the time alignment and data association processing, and forming associated data; performing feature matching on the multiple paths of video streams, fusing position information and environment parameters, and establishing a mapping relation between video feature points and a unified space coordinate system; splicing the multiple paths of video streams in real time according to the mapping relation to generate a panoramic video stream; performing dynamic scene analysis based on the panoramic video stream and the environmental parameters, and identifying a target type and a state to obtain an analysis result; and generating an equipment regulation and control strategy based on the analysis result, and generating and issuing a regulation and control instruction for adjusting the working parameters of the multi-view image acquisition device based on the equipment regulation and control strategy. According to the invention, the splicing precision, the real-time performance and the target identification accuracy of panoramic monitoring can be improved, and the dynamic adaptive regulation and control of the equipment can be realized.

The invention particularly relates to an atomic business intelligent arrangement device and method. The atomic business intelligent arrangement device comprises an atomic business relation graph intelligent construction module, an atomic business fine management module, a business intelligent arrangement and visual arrangement module and a man-machine interaction module. According to the atomic service intelligent arrangement device and method, full-chain intelligentization from user demand identification, service scene analysis to process automatic arrangement is realized, the service handling efficiency and the service quality are improved, manual correction is supported, the error rate is reduced, the reusability of atomic services is improved, policy scenes can be helped to rapidly fall into the ground, and the service quality is improved. And diversified operation requirements of the user are met, so that the user satisfaction is improved.

The invention relates to the technical field of power grid identification, and discloses a county power distribution network multi-dimensional operation mode identification method, system and device based on typical scene analysis, and a medium, and the method comprises the steps: obtaining the historical operation data of a county power distribution network, carrying out the extraction of multi-dimensional features based on the preprocessed historical operation data, and obtaining the multi-dimensional operation mode of the county power distribution network; carrying out clustering analysis on the operation data of the power distribution network through an improved time sequence clustering algorithm, and determining a typical scene of each category; and constructing a county power distribution network operation mode recognition model, classifying the operation states of the power distribution network, constructing a mapping relationship between the operation mode and a planning decision, and generating an optimal planning scheme. According to the invention, precise recognition of the operation mode of the county power distribution network is realized, the recognition accuracy of the operation mode of the power distribution network is improved, and precise planning is supported; the power distribution network planning cost is reduced and the resource utilization efficiency is improved.

The invention discloses a network construction type novel energy storage multi-objective optimization coordinated scheduling method, which comprises the following steps: S1, energy storage classification and data acquisition: (1) according to application scene function requirements, dividing energy storage types according to rated continuous discharge duration; (2) data acquisition; s2, calling an energy storage scene and combining with weight parameter setting to construct a multi-objective optimization model: (1) establishing the multi-objective optimization model with the purposes of minimizing the system operation cost, maximizing the energy storage utilization rate and improving the power grid stability; (2) setting constraint conditions; s3, a dynamic optimization algorithm: (1) adopting an improved multi-target particle swarm algorithm or a genetic algorithm, introducing an adaptive weight mechanism, and combining real-time data to dynamically adjust the weight of a target function; (2) aiming at uncertainty factors, embedding a scene analysis method or a robust optimization strategy, and judging whether the comprehensive efficiency is optimal or not; and S4, scheduling execution and monitoring.

The invention discloses a building surveying and mapping system based on an unmanned aerial vehicle remote sensing technology, and belongs to the technical field of building surveying and mapping. Comprising the following modules: a multi-sensor data acquisition module and an unmanned aerial vehicle data acquisition platform based on an integrated RGB camera, a laser radar and a multi-spectral sensor; the preliminary image processing module is used for generating standardized image data for scene analysis; the intelligent scene recognition module outputs a scene classification result and preliminary complexity grade evaluation; the self-adaptive decision module is used for generating an initial image processing parameter combination according to a scene recognition result; the dynamic image processing module is used for executing regional image registration, correction and seamless splicing based on the initial parameter combination to generate a building orthoimage; and the real-time quality evaluation module monitors and evaluates geometric accuracy and visual quality in the image processing process in real time.

The invention provides an environmental protection planning dynamic generation method and system based on a multi-modal large model. The method comprises the following steps: firstly, obtaining environmental protection planning target scene demand information covering text and image forms; calling the multi-modal large model to carry out cross-modal scene analysis, and generating an analysis result containing the core demand elements and priority ranking; generating a preliminary environmental protection planning scheme through a cross-modal planning content generation module based on the analysis result; collecting scheme pilot run feedback information and inputting the information into the multi-modal model for feedback adaptation analysis, and generating an adjustment rule; and finally, dynamically adjusting the initial scheme according to an adjustment rule to obtain a final scheme. According to the invention, scene requirements can be comprehensively and accurately captured, a scientific planning scheme is accurately generated, dynamic optimization is realized, and environmental protection planning efficiency and quality are improved.

The invention relates to a part generation method based on atomization disassembly and encapsulation data. The part generation method comprises the following steps: step 1, analyzing a demand scene; step 2, carrying out atomization disassembly; step 3, performing standardization verification; step 4, packaging the function logic layer; step 5, packaging the element part layer; 6, packaging an interface layer; according to the method, the problems of function solidification and difficult splitting of a traditional data product are solved, and efficient circulation and value release of data elements are promoted; and reusability and circulation efficiency of data parts are improved through the element part layer.

The invention provides a real-time video monitoring intelligent analysis system based on deep learning, and relates to the technical field of computers, and the system comprises a video collection unit which is used for obtaining a real-time monitoring video stream to be analyzed, and extracting a target video frame sequence from the real-time monitoring video stream; the scene analysis unit is used for processing the target video frame sequence by using a pre-trained scene analysis model to obtain a dynamic object mask and a scene semantic graph corresponding to the real-time monitoring video stream; the acquisition unit is used for acquiring thermal infrared characteristics and dynamic environment variable information of a monitoring area corresponding to the real-time monitoring video stream; and the semantic segmentation unit is used for inputting the dynamic object mask, the scene semantic graph, the thermal infrared features and the dynamic environment variable information into a pre-trained semantic segmentation model to obtain a semantic segmentation result output by the semantic segmentation model. By applying the method provided by the invention, the monitoring video can be accurately analyzed.

The invention discloses a new-generation emergency command system scene analysis system integrated with a convolutional neural network, particularly relates to the technical field of computer vision and intelligent emergency command, and is used for solving the problem that the dynamic association and situation evolution logic analysis capability among emergency scene elements is insufficient in the prior art. Visual element features are extracted from a video stream through a feature extraction module by using a convolutional neural network, an association graph construction module constructs a space-time association graph based on the features, a movement evaluation module calculates element group movement feasibility indexes, and a situation analysis module analyzes divergence features and antagonism degrees in combination with movement track convergence. And the risk identification module identifies a derived risk aggregation mode according to the situation prediction, and the semantic generation module finally outputs high-level semantic description information containing situation prediction to realize closed-loop analysis from scene perception to cognitive decision.

The invention relates to the field of advertisement putting optimization, in particular to an advertisement putting parameter adaptation method and system fused with scene analysis, and the method comprises the steps: obtaining multi-dimensional features of a plurality of known scenes in historical advertisement putting data; in combination with a preset feature embedding model, mapping the multi-dimensional features of the plurality of known scenes into high-dimensional vectors, and generating a known scene vector set to construct a scene vector space; in response to the received new advertisement putting request, obtaining multi-dimensional features of a new scene; based on the same feature embedding model, mapping the multi-dimensional features of the new scene into a high-dimensional vector, and generating a new scene vector; determining at least one optimal associated scene from the known scenes according to the new scene vector; generating an initial advertisement putting parameter of the new scene in combination with the historical advertisement putting parameter of the at least one optimal associated scene; and obtaining effect feedback data of the initial advertisement putting parameters in real time, and generating optimized advertisement putting parameters according to the initial advertisement effect feedback data.

The invention discloses a network data dynamic encryption updating method, system and device and a storage medium, and relates to the technical field of network security, and the method comprises the steps: generating an encryption key in a mode of combining a quantum random number generator and a machine learning algorithm; monitoring and analyzing an application scene of the original network data in real time to obtain a scene analysis result; evaluating the encryption algorithm in real time to obtain an algorithm evaluation result; selecting an encryption algorithm according to the scene analysis result and the algorithm evaluation result to obtain a target encryption algorithm; encrypting the original network data based on the encryption key to obtain encrypted network data; monitoring security threats in the network in real time to obtain alarm information; determining whether to update an encryption mode according to the alarm information; and continuing to encrypt the original network data under the condition of determining to update the encryption mode. Therefore, the security of the data is improved.

The invention belongs to the technical field of data processing, and particularly discloses a computer vision model service scenarized configuration generation method, which comprises the following steps: constructing a basic information database comprising a plurality of computer vision models, the database being used for storing model identification information and feature parameters; extracting structured features of the scene according to a scene analysis engine, wherein the structured features are used for describing operation conditions of the scene; calculating a model matching score according to the structured features and the feature parameters, and obtaining a recommendation model through a multi-condition weight matching function; executing a model selection strategy based on the matching score, and obtaining a model matched with the scene; and generating a configuration file conforming to a predetermined specification according to the recommendation model. The objective of the invention is to solve the problems of low efficiency and error proneness caused by the fact that a computer vision model in the prior art depends on a manual selection model and cannot quickly select and adapt to an optimal model in diversified scenes.

The invention discloses an enhanced GAN extreme scene generation method and system considering weather factors, and relates to the technical field of scene generation in new energy power system planning and operation, and the method comprises the steps: extracting key factors from multi-time-delay meteorological data, forming an extreme label, converting the extreme label into a generation condition, and achieving the output through two-stage confrontation generation, wherein a scarce sample is stably amplified by WGAN-GP, a joint curve is generated according to conditions, and related structures, climbing and power boundary constraints are synchronously embedded to ensure statistical and physical consistency. According to the method, the authenticity of the distribution tail, the cross-variable correlation and the time structure can be considered when the sample is scarce. Extreme situation deduction of a novel power system can be supported while extreme situation coverage and credibility are improved, and an existing new energy scene analysis and planning process is integrated.

The invention discloses a smart home control method and system based on graph retrieval enhanced generation and multi-agent gaming, and the method comprises the steps: constructing a basic knowledge graph based on a current smart home environment, carrying out the semantic enrichment of service entities in the basic knowledge graph, generating an enhanced knowledge graph, and storing the enhanced knowledge graph in a vector database; constructing an example library containing a plurality of formalized observation-logic hypothesis pairs; acquiring a situation snapshot of the user, and outputting a structured scene analysis report based on the situation snapshot and the vectorization database through a front-end LLM; collaborative review is carried out by combining a factor-tracing reasoning agent, a user preference agent and a conflict detection agent with a scene analysis report and an example library, and a final decision is output by a core decision LLM and mapped into an equipment control instruction to call corresponding equipment for execution. According to the method, the fuzzy instruction of the user is combined with the knowledge graph, so that the intention recognition accuracy is improved; the multi-agent parallel auditing mechanism improves the robustness and safety of decision making.

The invention discloses an HR3C scene-based fatigue prediction method and system, and relates to the technical field of data processing, and the method comprises the steps: collecting HR3C pipeline operation data to construct an application scene data set, carrying out the scene analysis to establish a ground state scene, and generating a limit state scene after key features are recognized; establishing environment change rate characteristics based on ground state and limit state scenes; and obtaining pipeline information to configure a dynamic fatigue prediction model, inputting related data to execute fatigue prediction, and performing pipeline management according to a prediction result. The technical problem that the prediction result and the actual service state have large deviation due to the fact that the fatigue evolution process of the HR3C pipeline under the variable working condition and the environment disturbance condition is difficult to accurately reflect in the prior art is solved, and the aim that the fatigue evolution process of the HR3C pipeline under the variable working condition and the environment disturbance condition is obtained by constructing the scene-based fatigue modeling system and introducing the dynamic environment change characteristics. And the accuracy of HR3C pipeline fatigue damage prediction is improved.

A container simulation network system based on a link autonomous agent is composed of a visual interaction module, a control plane module, a network simulation module and a resource management module. The modules perform information interaction and task collaboration through a uniform interface so as to realize automatic construction and management of the simulation network, a two-layer architecture design of network bridge containers and user containers is adopted, and each user container corresponds to one network bridge container; a link autonomous agent module is deployed in the network bridge container and is used for collecting a link state, calculating a performance adaptation degree and executing a scheduling operation bit; the visual interaction module realizes visual monitoring and interaction control of a simulation state; the control plane module is responsible for simulation scene analysis, link parameter generation and global scheduling management; and the network simulation module is responsible for calling a container generation tool to generate a specific container instance and carrying out resource monitoring and adjustment on the container.

The invention discloses an immersion type 3D scene intelligent optimization generation system and method based on AIGC, and particularly relates to the crossing field of computer graphics and artificial intelligence technologies. The system comprises a scene analysis and semantic modeling module, a style control module, a semantic interactive editing module, an immersive feedback acquisition module and an intelligent optimization loop module. According to the core method, a scene semantic map is constructed by analyzing user input, and scene visual consistency is guaranteed by using a global style control vector; realizing incremental updating driven by a local instruction through a semantic graph interface; and scene automatic iterative optimization is driven by fusing user behavior data and dominant feedback. According to the method and the system, the full-process intelligentization from generation, editing to optimization is realized, and the efficiency, the consistency and the user intention fitting degree of 3D scene creation are improved.

The invention relates to a medical quality control dynamic scoring system, device and equipment based on multi-modal perception and federal evolution, and the method comprises the steps: obtaining original scene information through multi-modal input based on a medical equipment recognition model, carrying out the scene analysis and semantic fusion, generating structured medical scene data, and carrying out the dynamic scoring of the medical quality control according to the structured medical scene data. Multi-source evidence verification is carried out through a medical scoring rule base, a quality evaluation result is generated through weight dynamic adjustment, difference analysis is carried out through comparison with a manual correction result according to the quality evaluation result, and model parameters and scoring rule updating are generated. Multi-modal input is fused based on a medical special perception layer, anti-interference scene analysis and equipment semantic binding are achieved, multi-source evidences are verified in real time through a dynamic scoring engine, deduction weights are dynamically adjusted, subjective item scoring floating is compressed, an identification model is updated through federation, and a rule base is thermally loaded in real time, so that the anti-interference scene analysis and equipment semantic binding is achieved. And automatic closed-loop processing of problem items is realized.

The invention provides a compliance strategy generation and risk early warning method and system based on multi-agent cooperation, and relates to the field of artificial intelligence and enterprise compliance management, and the method comprises the steps: collecting compliance basis documents through an information arrangement agent, and constructing a structured knowledge base; monitoring the change of the compliance basis document in the knowledge base through an information updating agent; receiving a business scene described by a user in a natural language through a scene analysis agent, and retrieving a compliance basis related to the business scene from a knowledge base; converting the retrieved compliance basis into an executable logic rule through a strategy generation agent; and acquiring real-time operation data from the business system through the risk early warning agent, comparing the operation data with the logic rule, and triggering risk early warning if violation occurs. According to the invention, automatic extraction, structured arrangement, dynamic updating, intelligent association and real-time judgment of compliance knowledge are realized, and the intelligent level and operation efficiency of enterprise compliance management are improved.

The invention discloses a method for automatically generating a security arrangement playbook based on a large model. The method comprises the following steps: acquiring user input description information in a current script generation task in real time through a Dify orchestration platform in a security orchestration playbook automatic generation system; processing description information input by a user through a scene analysis method in an artificial intelligence large model to obtain key field playbook parameters, and transmitting the key field playbook parameters to an SOAR log management tool; and carrying out generation processing through a playbook automatic generation method to obtain a target security arrangement playbook, and feeding back the target security arrangement playbook to a user. The problems that the workload is large, logic vulnerabilities are prone to occurring or actual requirements are not met, and the script generation efficiency is low due to manual script writing are solved, the script generation workload is reduced, and the script generation efficiency and accuracy are improved.

A multi-level and multi-parameter coupling regulation method includes the following steps: acquiring multi-source scene data, analyzing the multi-source scene data, and generating scene analysis information; calculating adjustment weights of preset adjustable parameters according to the multi-source scene data; calculating adjustment values of the adjustable parameters through a swarm intelligence optimization algorithm according to the scene analysis information and the adjustment weights; adjusting the adjustable parameters according to the adjustment values.

The application provides a self-obstacle-avoiding global mowing device, which comprises a vehicle body and a mowing mechanism, the mowing mechanism is located on the vehicle body, the mowing mechanism comprises a power member, a switching member and a plurality of cutter members, the switching member can switch different cutter members to cooperate with the power member; the control unit comprises a data acquisition module for acquiring a region scene image and a torque value of the cutter member, a global scene analysis module for recording the obstacle position and the hard object mark in the form of coordinate points in the global scene image, and a complex mowing path planning module for planning a complex mowing path according to the size of the global scene, the mowing width of the cutter member, the obstacle coordinate and the hard object coordinate and taking the total time consumption as a factor; the application has the advantages that the obstacles can be automatically identified and bypassed, the corresponding planning path can be planned, and the hard objects can be preliminarily screened to facilitate the subsequent route planning during the complex mowing.

The invention relates to a dual-agent driving decision planning method and system based on a multi-modal large model, and the method comprises the steps: Q1, scene construction: converting the information of a map, a vehicle and other traffic participants into a Frenet coordinate system in a unified manner, integrating the information into a multi-layer comprehensive aerial view, and generating corresponding text prompt information; q2, a trajectory generation intelligent agent: performing comprehensive processing on the input corresponding text prompt information based on a multi-modal large language model, identifying a key obstacle of a vehicle through scene analysis, performing behavior decision, and generating a driving trajectory; and Q3, judging the generated driving track by using a track judgment agent, analyzing problems existing in the track by using an anti-thinking chain, optimizing the generated driving track, and updating the track. According to the method, a closed-loop mechanism of generation-judgment-optimization is formed, uncertainty of single agent decision is effectively avoided, and it is guaranteed that the finally output path is safe and reliable.

The invention relates to the technical field of AI interaction, in particular to a user portrait construction and personalized interaction system based on an AI dynamic prompt project, which comprises a scene analysis module used for recognizing an interaction scene and a core optimization target in the scene according to user input; the co-processing module is used for carrying out quantitative evaluation and sorting on the long-term features and the real-time features according to respective stability parameters of the long-term features and the real-time features and correlation parameters of the long-term features and the real-time features and the core optimization target, and generating a hierarchical feature set; the instruction generation module is used for adapting an instruction template according to the interaction scene and fusing the hierarchical feature set into the instruction template to generate a structured driving instruction; and the interaction execution module is used for executing the driving instruction to generate and output final personalized interaction content. According to the method, accurate balance of long and short term features of the user in personalized prompt generation can be realized through a scenarized dynamic weight distribution and feature conflict coordination mechanism.

The invention discloses a millimeter wave detection complex scene analysis method and a millimeter wave detection complex scene analysis system, relates to the technical field of millimeter wave analysis, and is used for solving the problem that the probability of misjudging a target state by a system is increased due to a target speed identification error. Calculating a radial speed of a target object; acquiring acceleration and tension data of a conveyor belt; calculating acceleration power spectral density and tension change rate; evaluating the running state of the conveyor belt; and finally, the target actual speed is obtained by combining the correction speed and the impact factor, the object state is judged according to the minimum speed resolution threshold value of the millimeter wave radar, a conveyor belt control instruction is output, and stable operation of the system is guaranteed.

This invention discloses a low-light scene analysis method based on multimodal feature fusion and clustering, belonging to artificial intelligence technology. It constructs a single-branch feature extraction network based on Transformer; a multimodal feature interaction and fusion module to achieve multimodal feature interaction and fusion; a multimodal fusion feature clustering module to cluster the features after multimodal interaction and fusion, utilizing the semantic and spatial distances between different features to achieve clustering while simultaneously downsampling the features, mitigating the problem of loss of detailed edge information in structured downsampling; and a multi-scale feature aggregation and decoding module to receive feature information from the encoding network and classify each feature pixel according to the semantic distance of the multi-scale features. This invention can fully utilize visible light and thermal image information and can be applied to scene analysis and navigation of unmanned systems in diverse and complex low-light scenes.

The high-speed train defect identification method and device, equipment and medium provided by the application relate to the technical field of computer vision. In the application, firstly, global scene analysis is performed on a target high-speed train image to obtain image scene description data; secondly, based on the image scene description data, local anomaly analysis is performed on the target high-speed train image to obtain a candidate abnormal region set; then, anomaly semantic verification is performed on each candidate abnormal region in the candidate abnormal region set to obtain a defect identification result of each candidate abnormal region; finally, based on the defect identification result of each candidate abnormal region, a target abnormal region is determined in the candidate abnormal region set, and spatial positioning processing is performed on the target abnormal region to obtain a spatial positioning result. Based on the above, the problem that the reliability of defect identification is relatively low in the prior art can be improved.