389 results about "Scene matching" patented technology

The invention, which relates to the technical field of intelligent interaction, discloses an AI-based adaptive scene intelligent interaction system comprising a multi-modal data acquisition module, a modal preprocessing module, a multi-modal embedded coding module, an intention fusion and representation module, a service scene matching module and a service execution and reinforcement learning module. The method comprises the following steps: acquiring multi-modal original data in a user interaction process, including voice signals, text input and user behavior tracks, and synchronously recording an acquisition timestamp; according to the method, through a multi-modal unified embedding and dynamic weighting mechanism, the problem of characteristic dimension imbalance is effectively solved, and the user intention recognition accuracy is improved; meanwhile, reinforcement learning and a multi-factor scoring model are combined, personalized scene matching and dynamic response are achieved, the adaptive capacity and service accuracy of the system in a complex environment are improved, and therefore the stability and user experience of the intelligent interaction system are remarkably optimized.

The invention relates to the technical field of virtual power plant regulation and control, and provides a virtual power plant multi-scene cooperative regulation and control method and system for power supply insurance, and the method comprises the steps: periodically obtaining the supply and demand time series data of a virtual power plant, and carrying out the supply and demand fluctuation characteristic analysis of the supply and demand time series data of the virtual power plant, and generating supply and demand fluctuation time series data; according to the supply and demand fluctuation time sequence data, scene matching analysis is carried out based on a pre-constructed multi-scene regulation and control strategy library to obtain an initial scene regulation and control strategy; performing scene adaptability simulation analysis on the initial scene regulation and control strategy, and optimizing the initial scene regulation and control strategy according to a simulation analysis result to obtain a target scene regulation and control strategy; and performing layered and partitioned disassembly on a regulation target in the target scene regulation strategy to obtain a layered and partitioned regulation target, and executing virtual power plant regulation according to the layered and partitioned regulation target. According to the method, the collaborative optimization capability and the operation toughness of the virtual power plant in a complex and uncertain environment can be remarkably improved, and reliable technical support is provided for power supply insurance.

The invention relates to the technical field of artificial intelligence, in particular to a scene application operation maintenance platform based on an artificial intelligence model, which comprises a reasoning state sensing module, a load balancing scheduling module, an intelligent resource allocation module, a deployment configuration adjustment module and a scene feedback integration module. According to the method, by sensing and reasoning process resource consumption and response time migration trend, abnormal tasks caused by resource fluctuation can be accurately recognized, the state monitoring sensitivity is improved, task allocation records are analyzed to construct conflict structures, the resource conflict recognition and visualization capability is enhanced, the model operation level and the GPU vacancy rate are combined, a task migration path is optimized, and the task migration efficiency is improved. Node resource balance is realized, storage frequency, memory margin and network delay are fused during deployment, migration stability is guaranteed, drift tasks are identified through response logs and call records, an operation and maintenance list is generated, operation and maintenance accuracy and multi-scene matching capability are improved, and reasoning task stability and resource scheduling intelligent level are enhanced.

The invention belongs to the technical field of vehicle control, and particularly relates to a vehicle-mounted image recognition and target detection system based on deep learning, and the system comprises a distributed monitoring module which collects the operation, obstacle and traffic signal information of a target vehicle through multi-modal classification and scene matching, completes the marking of a shielding region and the matching of information through the combination of shared data, and achieves the recognition of the target vehicle. Forming an enhanced monitoring set; the label planning module constructs an enhanced topological space based on the enhanced monitoring set, and adjusts moving tracks in different scenes by combining with vehicle and pedestrian track probability distribution fed back by dynamic intention recognition; the action recognition module predicts trajectory parameters and collision probabilities of non-target vehicles and pedestrians by using Bayesian and multi-modal algorithms; the decision-making module generates a real-time control instruction through particle swarm optimization and fuzzy control, and optimal control parameters are fed back through simulation; according to the invention, intelligent track planning and real-time control in a complex scene are realized, and the detection precision and control robustness of the shielded and label-free area are improved.

The invention discloses a power market large language model evaluation method and device based on dynamic scene perception, and the method comprises the steps: building a large model evaluation system covering the three dimensions of understanding, generation and safety according to a power market application scene; the method comprises the following steps: constructing a test question bank, evaluating a power market large language model according to various evaluation methods, endowing three dimensions with different weights based on a large model evaluation system in the evaluation process, constructing a test question-scene label fine tuning data set, and performing scene classification fine tuning on the model by using a fine tuning method. Evaluating scene classification performance of the fine-tuned model, and distributing corresponding initial evaluation dimension weights for different scenes; according to the requirements and differences of different scenes, the initial evaluation dimension weight is adjusted, the scene is judged to be matched with the corresponding evaluation dimension weight according to the input content, weighted summation is carried out from the three dimensions of understanding, generation and safety, and an evaluation result is obtained. According to the method, accurate evaluation of the performance and reliability of the large language model in the power field can be realized.

The invention relates to the technical field of artificial intelligence, and provides a marketing data generation method and device based on portrait data, equipment and a medium, marketing association data can be collected and purified based on a three-level data gateway, feature fusion is carried out by using a star-shaped collaborative network constructed based on a dynamic weight mechanism and a federated learning mechanism, and the marketing data generation efficiency is improved. The problems of data dimension limitation and data island are solved; scene recognition is performed based on a marketing data graph constructed by a secondary scene classification tree including a gift scene, and the problems of low utilization efficiency of unstructured data and insufficient crowd portrait granularity are solved; the marketing strategy is generated by using the target engine matched with the scene, so that the problems of scene engine deficiency and gift scene adaptation imbalance are solved; and generating the target marketing data according to the target marketing strategy and the marketing data graph. The problems of low operation efficiency and insufficient content accuracy are solved.

The invention relates to the technical field of scene matching navigation, and provides a multi-mode scene matching navigation positioning method and device for a self-adaptive variable-structure unmanned aerial vehicle. According to the method, geographic coordinates corresponding to all pixel points in an aerial image are determined according to pose information in the aerial image of the unmanned aerial vehicle, and the geographic position of the unmanned aerial vehicle with errors is corrected based on difference information between the pixel coordinates of the central point of the aerial image of the unmanned aerial vehicle and the geographic position corresponding to the center of a camera. Based on the corrected geographic position of the unmanned aerial vehicle, determining a reference image pixel coordinate corresponding to the angular point pixel coordinate of the aerial image, and obtaining a simulated aerial image under the view angle of the unmanned aerial vehicle through perspective transformation so as to determine a homography transformation matrix; according to the method, the satellite reference image pixel coordinates of the interest point in the aerial image are determined based on the homography transformation matrix and the perspective transformation matrix, and finally the longitude and latitude of the interest point are determined, so that the limitation of the traditional method in view angle difference compensation, computing resource constraint and cross-modal processing is solved, and the universality, robustness and positioning accuracy of the system are improved.

The invention provides an efficient data retrieval method and system oriented to a large language model. The method comprises the following steps: determining an intention vector set matched with a user intention; determining a specified number of candidate text fragments with the highest correlation with the user intention in an index database; obtaining multi-dimensional feature evaluation data of each candidate text fragment, and constructing a multi-dimensional feature numerical matrix of each candidate text fragment according to the multi-dimensional feature evaluation data; according to the multi-dimensional feature numerical value matrix, selecting selected text fragments from the candidate text fragments; sorting the selected text fragments, and sequentially performing scene matching on the selected text fragments and a pre-cached high-frequency query scene according to the sorting, so as to extract cache content matched with the target high-frequency query scene from the cache as a query result when a matching result represents that the current query scene accords with the target high-frequency query scene. The invention provides an efficient data retrieval method, and the quality and precision of data retrieval are improved.

The invention discloses a multi-scene term translation model training method and processing method based on a large model, and the method comprises the steps: firstly constructing a term-scene-translation triple data set, and formatting the term-scene-translation triple data set into a training sample containing an instruction, context input and expected output through a structured term translation instruction set; then, the samples are used for fine tuning of the basic large language model, and a preliminary adaptive model is obtained. And finally, introducing a reinforcement learning method based on a verifiable reward mechanism to optimize the fine tuning model, and iteratively improving the performance of the model in the aspects of scene matching degree, specification conformity, cross-scene consistency and the like by calculating a reward value. According to the method, the problems that a traditional term translation model is poor in flexibility and insufficient in specialty are effectively solved, and the multi-scene term translation which is high in accuracy and conforms to specific industry specifications can be automatically generated.

The invention relates to the technical field of dangerous cargo management, in particular to an intelligent compliance verification system for dangerous cargo boxing operation. Comprising a data acquisition preprocessing unit; the law and regulation graph construction iteration unit is used for extracting a'law and regulation term number-verification dimension-constraint condition 'triple of the law and regulation in the dangerous cargo boxing field and introducing an improved three-dimensional dynamic weight conflict resolution algorithm according to the efficacy level, the release subject and the application range indexing; a multi-agent collaborative verification unit; a risk early warning report generation unit; and a man-machine cooperation closed loop unit. According to the method, the problems of lagging of laws and regulations and clause conflicts are solved by adopting triple extraction of'laws and regulations clause numbers-verification dimensions-constraint conditions', a three-dimensional dynamic weight conflict resolution algorithm based on laws and regulations effectiveness-scene matching degree-release time and a technical means of periodically optimizing weight parameters through a reinforcement learning model.

The invention discloses a method for monitoring and identifying dangerous behaviors of a smart park, and relates to the field of intelligent security and protection. The method comprises the following steps: acquiring an environment data stream through a sensing device deployed at a key point location of a park and carrying out standardization processing on the environment data stream; the data stream is sent to an edge computing node, a target is recognized through a lightweight target detection model, the detection confidence coefficient is obtained, a motion track is generated through a multi-target tracking algorithm, the rule matching degree is calculated according to a predefined rule base, and dangerous behaviors are preliminarily recognized; calculating a comprehensive model prediction credibility based on the detection credibility and the rule matching degree, and generating a final comprehensive credibility through a multi-dimensional credibility fusion model in combination with the time sequence consistency credibility and the scene matching credibility; and dangerous behavior trend prediction is carried out according to the comprehensive confidence, and an alarm instruction is generated to trigger disposal linkage, so that advanced prevention and quick response of dangerous behaviors are realized, and the intelligent level and operation and maintenance efficiency of park security and protection are greatly improved.

The invention provides a business travel data management method and system based on cloud computing and scene matching and computing equipment. The method comprises the following steps: acquiring at least one geographic image uploaded by a user in a business travel plan execution process; generating a compliance geographic region set according to the travel plan; matching the geographic image with the compliance geographic region set to obtain a first matching result; identifying and extracting visual scene features in the geographic image, and performing consistency cross validation comparison on the visual scene features and a geographic scene expected based on geographic coordinates to obtain a first comparison result; generating an authenticity verification result of the travel behavior based on the first matching result and the first comparison result; and when the authenticity verification result is that the verification is passed, automatically generating an electronic audit voucher containing a spatial trajectory chain and a geographic image. According to the technical scheme of the invention, the accuracy of authenticity verification of travel behaviors can be improved, a high-credibility electronic audit voucher is automatically generated, and a data closed loop is provided for intelligent travel management.

The invention relates to the technical field of information extraction, and discloses a long text news event element extraction method and system based on multi-agent collaboration, and the method comprises the following steps: constructing a large language model multi-agent system architecture; the central coordination agent receives the unstructured news text; the text segmentation agent outputs a logic unit and time description thereof; an event extraction agent extracts an event subject, an action and an object, and complements time and entity role information; the scene matching agent judges the fine-grained news scene category to which the scene matching agent belongs; the information extraction agent generates a structured event record; and the central coordination agent performs quality auditing and exception handling on each step of output to ensure the integrity and consistency of the output data. According to the method, the static relationship between the event main bodies can be constructed according to the structured event information through the relationship construction and context sorting agent, the event situation change is summarized based on the timeline, and the feasibility and the visualization of news event analysis of the system are improved.

The invention belongs to the technical field of commercial entertainment recommendation, and discloses a matching method for recommending online and offline script playing methods based on an AI algorithm, and the method comprises the steps: collecting user immersion experience data, carrying out the multi-dimensional deconstruction of the immersion experience data, and obtaining a user preference pedigree; performing script emotion tension and interaction density analysis on the script content to form a cross-scene experience map; designing a dual-channel sensing network, and performing cross fusion on the user preference pedigree and the cross-scene experience atlas based on the dual-channel sensing network to generate a personalized matching matrix; establishing an immersion degree evaluation parameter set, and performing scene adaptability calculation on the personalized matching matrix to obtain a scene conversion coordination degree index; generating a script recommendation sequence according to the scene conversion coordination degree index, and realizing real-time recommendation adjustment and experience feedback collection through context awareness; the difference between online and offline script experiences is effectively quantified, and a scientific cross-scene matching basis is provided.

The invention discloses an agricultural scene recognition and verification method and system based on multi-modal data, and belongs to the technical field of agricultural scene recognition. Multi-dimensional agricultural scene data such as images, weather and soil are collected through multiple sensors, and a standardized data set is formed through preprocessing such as data cleaning, calibration and space-time registration; using a soft voting integrated classifier to carry out agricultural scene identification on the image and time sequence features extracted from the standardized data set; based on the recognition result, the scene analysis model management library combines the scene matching degree and system resource dynamic selection, adjusts a scene analysis model and parameters thereof, and then completes multi-modal analysis to obtain a multi-modal data set containing rich information; and finally, carrying out multi-dimensional quality evaluation on the multi-modal data set. According to the method and the system, multi-modal data is effectively fused, the agricultural scene recognition accuracy is improved, computing resources are reasonably allocated, comprehensive and reliable data support is provided for agricultural production decisions, and the adaptability of the system to complex agricultural scenes is enhanced.

The invention relates to the technical field of image processing, in particular to an in-orbit multi-stage real-time automatic dimming device and method for a spaceflight area-array camera. According to the device, an imaging module obtains an original scene image according to set integral time and transmits image data to an image coarse adjustment module; the image coarse adjustment module counts gray level distribution of an original image in real time, calculates first updating integral time, packages image data without overexposure or underexposure and sends the packaged image data to the image fine adjustment module; after the image fine adjustment module receives the image, scene recognition and gray level evaluation are carried out through a deep learning model, exposure parameters are dynamically adjusted according to the scene type, and image exposure control is achieved; and the image coarse adjustment module and the image fine adjustment module form a two-stage closed loop, so that the imaging module obtains integral time matched with a scene in real time. The method has the advantages that the integral time of the detector is automatically adjusted according to the detail content of the target scene, and the target image with the best image impression and detail is captured.

The invention relates to the technical field of twin power station simulation, and discloses a twin power station collision visual simulation method based on physical simulation. The method comprises the following steps: acquiring real-time physical parameters and an operation state of the twin power station, and establishing a dynamic simulation model fitting the reality according to the real-time physical parameters and the operation state; analyzing a historical collision simulation record, dividing a historical strategy into multiple cognitive levels in combination with a deviation degree between a simulation result and an expected target and a simulation strategy reliability index, and constructing a structured multilayer knowledge base; and for a preset target collision scene in the model, generating an adaptive simulation strategy of each stage, and screening an adaptive historical strategy from the knowledge base according to the reliability and the scene matching degree. The two strategies are fused and adjusted through a coordination mechanism to adapt to real-time changes of the power station, a control instruction is generated after an optimized simulation strategy is determined, collision simulation execution is driven, and finally a visual result is output and an actual simulation state is obtained. According to the method, the accuracy and adaptability of collision simulation can be improved.

The invention discloses a building light environment neural network control system based on BIM, relates to the technical field of building intelligent control, and is used for solving the problem of poor intelligent illumination adaptive control. The method comprises the following steps: constructing a structure enhanced semantic map based on a BIM model, extracting a spatial structure attribute and a communication relationship to generate a node vector and an edge weight, and describing an illumination propagation relationship; the method comprises the following steps: constructing an original state vector by fusing sensing data and structural information, carrying out time sequence modeling by adopting a gated recurrent neural network, dividing four types of subspaces to express environment evolution characteristics, identifying a lighting task scene according to a current activity and time period, matching a control target, reasoning an adjustment direction, and screening a candidate action path; according to the method, the optimal strategy is generated and executed, user feedback is collected to construct behavior response factors, user preferences are updated, a preference closed-loop mechanism is constructed, illumination control cooperatively driven by structure perception, state evolution and user preferences is achieved, and the comfort, intelligence and energy saving performance of the building light environment are improved.

The invention relates to the technical field of service quality evaluation, in particular to a multi-dimensional after-sales service evaluation scoring system, which comprises a responsibility description comparison module, a node process propulsion module, a scoring dimension generation module, a scoring data acquisition module and a responsibility attribution judgment module. According to the invention, through the description and comparison of responsible persons of audio and text dual channels, the verification of node sequence consistency and conclusion content consistency, the linkage judgment of the fusion task state and personnel identity, and the linkage circulation logic of on-site sign-in data, and the dynamic mapping of the scoring dimension in combination with multivariate parameters such as the service type and the emergency degree. Scoring data are collected in a layered mode, score differences and time sequences are fused, responsibility traceability labels are established, multi-dimensional data synchronous verification, flow node collaborative propulsion, dynamic scoring scene matching and responsibility affiliation transparent management are achieved, the dispute recognition capacity in a complex service scene is enhanced, and the responsibility traceability is improved. And the adaptability of the whole-process tracing fineness of the work order and the service link evaluation is improved.

The invention provides a non-abandoned collection model generation method and system based on a GAN style constraint mechanism, and relates to the technical field of digital protection and display of intangible cultural heritage. According to the method, the generative adversarial network is driven by integrating the user portrait data and the non-missing basic data, the artistic style is accurately controlled in combination with a style constraint mechanism, dynamic scene matching display is realized by utilizing an augmented reality technology, and the method has the advantages of improving non-missing digital modeling efficiency, enhancing user interaction experience and guaranteeing culture inheritance accuracy.

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 a mismatching measuring and eliminating method for scene matching navigation, and belongs to the technical field of image matching. Scene matching and inertial navigation are combined together to form a combined navigation system, and two pieces of information including an inertial navigation attitude and an inertial navigation position are introduced into scene matching to complete elimination of mismatching of scene matching; the method comprises the following steps: firstly, completing homography matrix calculation by using an inertial navigation attitude, a camera internal reference matrix, a translation amount of an inertial navigation position on a reference map and a corresponding relation between a camera and an inertial navigation coordinate system; carrying out eigenvalue decomposition and comparison on a homography matrix subjected to scene matching calculation to finish first-step elimination; secondly, performing chi-square detection by using position measurement provided by scene matching to complete second-step elimination; the inertial navigation attitude is introduced to eliminate mismatching, so that the problem of chi-square detection failure under the condition that scene matching is not matched for a long time is solved.

The invention relates to the technical field of information, in particular to an intelligent short message platform management and control method and system based on multi-level risk control. The method comprises the following steps: reading a to-be-sent short message text, comparing the to-be-sent short message text with a preset interception keyword library, and intercepting a to-be-sent short message if a matched interception keyword exists; identifying the semantics of the short message to be sent, and comparing the semantics with a plurality of preset prohibited scenes to obtain a scene matching degree; if the scene matching degree is higher than a preset threshold value, intercepting the short message to be sent; according to the recognized semantics, marking a text corresponding to the semantics; reading the unmarked text, and if the word number of the unmarked text exceeds a preset word number threshold value, obtaining characters with similar fonts and characters with similar pronunciation; trying to perform semantic recognition according to the combination of the characters with similar fonts and the characters with similar pronunciation of the unmarked text; comparing with a plurality of preset forbidden scenes; and if the matching degree of any second scene is higher than a preset threshold value, intercepting the short message to be sent.

The invention discloses an intelligent storage automatic control optimization method and system based on dynamic environment perception, and relates to the technical field of intelligent storage control. According to the method, a specific scene type of a specified warehouse is firstly obtained, a corresponding dynamic perception allocation scheme is determined through scene matching, and then a collaborative operation association object is determined, so that accurate adaptation of dynamic environment perception and a warehouse scene is realized; then, collecting dynamic environment sensing data of a storage area where a collaborative operation association object is located, and after collaborative association processing is carried out, generating a structured fusion data set adapted to different time sequence scenes; dynamic storage parameter calibration is carried out, an initial control strategy is generated, and effectiveness evaluation is completed; whether the path planning scheme and the operation execution sequence are dynamically adjusted or not is decided according to the evaluation result, dynamic adaptation and accurate regulation and control of intelligent storage automatic control are achieved, and the adaptability of storage control, the smoothness of process operation and the stability of operation execution in a complex dynamic scene are effectively improved.

The invention discloses a face in-vivo detection method and system based on scene optimization, a medium and a product, and relates to the field of computer systems based on a specific calculation model, and the method comprises the steps: extracting a mixed image sample, and applying physical drive transformation to obtain an enhanced image sample; scene comparison loss is calculated; based on living body classification loss and scene comparison loss, training the basic model to obtain a teacher model; inputting the scene sample into a scene branch of the teacher model to obtain a scene target vector; inputting the mixed image sample into a scene branch to obtain a scene vector set; calculating the vector similarity between the scene target vector and the vector in the scene vector set, screening out a corresponding image sample, and obtaining a scene matching data subset; taking the teacher model as supervision, executing knowledge distillation, and generating a living body detection model; and inputting a to-be-detected face image to the living body detection model to obtain a classification result. By implementing the method, the living body detection performance of the model in a specific deployment scene can be improved.

The invention discloses a spaceborne SAR scene matching curve imaging electromechanical cooperative wave control optimization method based on dynamic programming, and belongs to the technical field of spaceborne SAR. The method comprises the following steps: firstly, acquiring a beam pointing attitude angle time sequence before decomposition by using a satellite-borne SAR non-along-track multi-target imaging satellite-ground configuration joint design and optimization method, and solving a phased array electric scanning angle; secondly, establishing an electromechanical collaborative dynamic planning model comprising a platform mechanical scanning state transition equation, a cost function and a constraint function; initializing parameters such as a mechanical scanning state track, a control input sequence and the like to obtain an initial track and cost; optimal control solution is executed through dynamic planning, and convergence is achieved through inner and outer layer iteration (backward transfer calculation of a control law, forward transfer updating of a track and outer layer correction constraint violation); and finally, outputting a mechanical scanning attitude angle time sequence and a phased array electric scanning angle time sequence, and completing electromechanical cooperative wave control optimization.

The invention relates to the field of advertisement pushing, is used for solving the problem of poor cost control and conversion rate of advertisements caused by insufficient optimization degree of an advertisement putting process, and particularly relates to an accurate advertisement distribution and pushing system and method based on multi-source portrait and scene matching. According to the method, multi-aspect matching is carried out through scene matching and user portraits, so that the pushing crowd of the advertisement can be ensured to be adaptive to the promotion requirements of the advertisement to the greatest extent, the conversion rate during advertisement promotion is improved, and meanwhile, matching of multiple feature points is carried out according to the user portraits, the pushing priorities of the users are established, and graded promotion is realized according to the priorities; the problems that traditional pushing classification edges are difficult to define, potential customers are prone to being lost or cost is increased due to a large amount of invalid pushing are solved, when the user is portrayed, whether the use habit of the user is greatly changed or not is judged through weight distribution of existing portraits and new stage portraits, and the user experience is improved. And the timeliness and the accuracy of updating the user portrait are improved.

The invention discloses a power industry construction unit safety management and control dynamic evaluation method and system, and the method comprises the steps: constructing a full-scene safety standard knowledge base covering power transmission and transformation and power distribution scenes, and building cross-scene semantic association through a knowledge graph; real-time resource information of a construction unit is obtained, and a basic element parameter model is constructed and dynamically updated through a multi-granularity strategy; based on a project scene matching standard security element set, evaluating the security risk of the whole process through a deep Q network algorithm fusing transfer learning and reinforcement learning, and generating a quantitative index and a resource configuration scheme; and generating project-level, process-level and operation-level three-level management and control instructions and pushing the instructions to the terminal. According to the method, scene self-adaption, resource state real-time mapping and risk disposal closed-loop feedback of safety assessment are realized, and the risk identification timeliness, the resource configuration accuracy and the field instruction executable performance are improved.

The invention discloses a prefabricated part production management system, and relates to the technical field of constructional engineering. The system comprises a BIM model integration module, a scene matching module and a production monitoring module. The BIM model integration module is associated with a production progress plan, extracts component information to generate initial production data, and can parametrically modify the model according to interference conditions and progress deviation to generate optimized production data; the scene matching module detects time sequences and space interferences of different construction stages in combination with the production progress, generates a three-dimensional thermodynamic diagram with progress nodes, and supports installation sequence simulation to output an optimal scheme; the production monitoring module associates the components with the BIM model through a unique electronic tag, full life cycle tracing is achieved, and the tag contains raw material and production process information. According to the system, the production collaboration and controllability of the prefabricated parts are improved, and the system is suitable for the field of prefabricated buildings.

The invention discloses a rainfall dynamic simulation and regulation method, and relates to the technical field of artificial rainfall management and control, and the method comprises the steps: recognizing a catalyzable cloud layer of a rainfall demand region through a convolutional neural network, and generating a plurality of candidate catalysis regions according to the catalyzable cloud layer; the processing end dynamically simulates the spatial distribution of natural precipitation and post-catalysis precipitation of each candidate catalytic area based on a WRF-Chem model, outputs a selection influence factor for each candidate catalytic area, analyzes the selection influence factor and position information of the candidate catalytic area through the upper layer of the double-layer engine framework, and obtains the spatial distribution of the post-catalysis precipitation; and outputting a catalytic area sorting table by utilizing a lower layer of the double-layer engine framework, selecting a corresponding number of catalytic area information according to a rainfall demand, importing the selected catalytic area information into a large database for scene matching, and outputting an optimal regulation and control strategy for each selected catalytic area based on a genetic algorithm. According to the regulation and control method, rainfall simulation is carried out on each candidate catalytic area, it is ensured that the optimal area is selected for rainfall enhancement operation, and the overall rainfall uniformity and efficiency are improved.