211 results about "Hierarchical modeling" patented technology

The invention provides an agent strategy generation and online optimization method based on dynamic scene perception, and the method comprises the steps: obtaining a scene demand description of a user, and carrying out the analysis of the scene demand description, so as to generate a target task sequence which can be executed by an agent disposed in a target scene; dynamically sensing the current environment characteristics of the target scene to generate a dynamic semantic topology network and generate a dynamic scene graph according to the dynamic semantic topology network; on the basis of the dynamic scene graph, hierarchical modeling of the incidence relation is carried out on the behavior space corresponding to the intelligent agent, and behavior semantic features containing scene perception are generated; the behavior semantic features containing scene perception are mapped to an intelligent agent strategy representation space, and a behavior feature strategy for controlling an intelligent agent to execute a target task sequence is obtained; and according to the determined scene value representation, decomposing the behavior feature strategy to obtain an advantage estimation value adapted to the scene so as to carry out online optimization on the behavior feature strategy. According to the invention, the intelligent agent strategy can accurately adapt to the requirements in the business process of an enterprise.

The invention provides an intelligent scheduling and control method and device for an integrated energy system. According to the method, power, gas and heat resource operation data are acquired, multi-scale layered modeling is performed according to a time scale and a space scale, and a power resource state space model, a gas resource flow continuity model, a heat resource heat balance model and a multi-energy coupling characteristic constraint model are established; carrying out feature extraction and dimension reduction representation by adopting a deep auto-encoder network; cooperative training of multiple groups of cognitive models is carried out through a split hierarchical federal learning framework, and a global intelligent model is obtained; constructing a neural architecture search network with a hybrid bionic learning rule, setting a hierarchical scheduling target, and generating a hierarchical intelligent scheduling strategy; and a fault-tolerant control mechanism is constructed, and error detection and correction of operation deviation are realized. According to the invention, multi-time scale collaboration, collaborative learning under multi-device group privacy protection and high-reliability fault-tolerant control are realized, and the operation efficiency and reliability of the integrated energy system are remarkably improved.

The invention provides a script generation method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence, and the method comprises the steps: determining plot data in an object text, the plot data comprising a plot sequence corresponding to the object text and plot elements related to each plot in the plot sequence; generating a script narrative outline according to the plot data; and generating the script content based on the script narrative outline. According to the script generation method and device, the electronic equipment and the storage medium provided by the invention, the plots and the plot elements related to the plots are subjected to structured processing through hierarchical modeling of the object text starting from the small speaking text, so that the complete script is generated one by one according to the obtained hierarchical script narrative outline, and the user experience is improved. The richness of the script plot, the consistency of figures and the logic continuity of the script content can be ensured, and plot faults or contradictions can be effectively avoided.

The invention relates to a chronic heart failure dynamic course evolution prediction model construction method. Comprising the following steps: uniformly mapping continuous variables including LVEF and heart rate and event variables into a time trajectory frame through an event alignment and time domain nesting strategy; using a local change rate algorithm to identify inflection points including states before acute deterioration and intervention reactions in the course of disease of each patient; constructing a state fragment set for supporting hierarchical modeling in an evolution stage; a bidirectional fusion method of trajectory clustering and medical knowledge embedding is used to construct a state space with clinical interpretability including a compensation period, edge decompensation and an acute deterioration period; taking the trajectory vector as a main input, taking a state space as a prediction target, and introducing a dual-channel structure; predicting a future path based on the current state; the disease course track change of early medication / non-hospitalization / treatment scheme change is simulated; a doctor is supported to deduce a result; the change of the output state is analyzed through perturbation of the current trajectory, and key variables are found out.

The invention discloses an ancient textile image restoration system based on artificial intelligence. The system comprises a multi-source image acquisition module, a damaged area detection module, a pattern generation module, a color restoration module, a texture synthesis module and a multi-scale fusion module. The system introduces a wavelet guidance-frequency domain attention mechanism and a rotation invariant Haar wavelet basis function to realize accurate identification and classification of a damaged area; a saliency-guided wavelet decomposition control and self-adaptive threshold denoising method is combined, so that the perception capability of slant textures and edge details is improved; the texture synthesis module constructs a hierarchical modeling strategy fusing Gram style loss, Wasserstein style loss and a total variation regular term, and realizes generation of high-quality textures with unified styles and smooth edges; the system can be widely applied to cultural relic digital repair and display scenes.

Hierarchical modelling and advanced feature engineering discover abnormalities in time series data with irregular trends. Data is collected in real time to ensure temporal integrity in the invention. Extraction filters and isolates useful data. Data cleansing removes noise and extraneous data after preliminary analysis identifies patterns and abnormalities. Feature engineering organizes cleansed data for machine learning algorithms. Primary storage stores this data for fast retrieval and extensive trend analysis. Holidays and weekends provide unique patterns in trend analysis. These trends are used to cluster data and create hierarchical predictive models, starting with a first-order model for general trends and increasing in order to refine residuals. Serializing these models improves storage and retrieval. Trend clusters are created from new data points, and algorithms detect pattern deviations. Statistical tests and machine learning classifiers identify anomalies and create alerts and remedial measures. The system monitors and analyzes incoming data to detect anomalies.

The invention relates to a battery internal short circuit early warning method and system based on a physical information neural network, and belongs to the technical field of battery fault detection, and the method comprises the steps: constructing a first-stage PINN model through a linearized Butler-Volmer equation, and carrying out the training through a multi-source signal of a battery; taking the output of the first-stage PINN model as prior input, and constructing a second-stage PINN model by using a nonlinear electrochemical-thermal-mechanical coupling equation, and training the second-stage PINN model; using the trained first-stage PINN model and the trained second-stage PINN model to output prediction results of the voltage and the expansive force of the target battery; comparing the difference between the prediction result of the model and the measured value of the sensor to generate a voltage residual error and an expansive force residual error; and realizing identification and alarm of the short circuit risk in the target battery based on the voltage residual error and the expansive force residual error. According to the method, a multi-fidelity PINN hierarchical modeling mechanism is introduced, so that high-precision characterization and prediction of the internal state of the battery can be realized by using experimental data and mechanism model information at the same time under different precision and complexity hierarchies.

The invention discloses a multi-scene gait monitoring and motion function assessment method and system, a multi-camera cooperation system is deployed in a life scene to accurately identify an identity and extract multi-scene motion features, and an accurate old people gait function assessment scheme is provided through key motion hierarchical modeling, multi-scene data fusion and a risk suppression mechanism. Specifically, 2D human body key points are estimated based on an OpenPose algorithm, and a PoseLifter model is utilized to lift the 2D key points to 3D, so that a three-dimensional motion track of a target is estimated; dynamically adjusting the weights of the face features and the skeleton features during identity recognition according to ambient light; designing an independent long-short-term memory network for different actions to evaluate the motion function score and the fall risk rating of the actions; and designing a risk sensitivity mechanism and fusing multi-scene data to carry out comprehensive scoring. The gait function abnormity of the old people can be found in time, and a basis is provided for health management.

The invention discloses an AI system for generating customized contents based on user preferences, which belongs to the technical field of artificial intelligence, and comprises a user preference modeling module for constructing and continuously updating a user feature vector library and realizing personalized demand modeling and classification; the interactive input and early warning module is used for receiving user input, implementing risk control and providing a visual interactive correction tool; an intelligent parameter conversion module; a video preprocessing and enhancing module; a cross-modal semantic generation module; the dynamic training and optimizing module is used for controlling a model training process and balancing repair quality and style migration; the multi-version generation and evaluation module is used for outputting a differentiated repair result and quantitatively evaluating the performance; the feedback learning and iteration module is used for collecting user preference data and driving the system to continuously optimize; and a distributed task scheduling module. According to the method, the real-time performance and accuracy of the user portrait are ensured through the layered modeling mode, accurate matching of personalized content generation is supported, and user experience and content recommendation are effectively improved.

The invention discloses a marine meteorological observation data millisecond query system based on time sequence block indexing. The marine meteorological observation data millisecond query system comprises a data input module, a data preprocessing module, a data hierarchical modeling module, a data storage cluster, a data analysis engine, a data query engine and a data operation interface module. Based on data modeling, data thinning and data partitioning technologies, data preprocessing is carried out for different meteorological ocean space weather data, and distributed storage and access of mass data are achieved; the method comprises the following steps: inputting ocean actual observation data, forecast data or historical data, carrying out model construction and index design according to meteorological ocean space weather data characteristics, completing distributed storage, and applying a data query engine and a data analysis engine to realize rapid aggregation query of the meteorological ocean space weather data; storage and query of lattice point data are realized by using a distributed database, and the problems of scale and query performance of meteorological ocean space weather data are effectively solved.

The invention relates to a few-sample image classification method based on hyperbolic space image-text local feature alignment, and belongs to the technical field of image recognition and artificial intelligence, and the method comprises the steps: generating word-level attribute description for a support set image through employing a multi-mode large language model; encoding the image and the text by adopting a vision-language model; constructing a hyperbolic local feature alignment module in a hyperbolic space, screening most relevant image local features for text local features by calculating hyperbolic cosine similarity, and fusing by using hyperbolic weighted average; designing a hyperbolic cross attention module, and aggregating key information from the multi-modal local features of the support set to construct a category prototype by taking query image aggregation features as guidance; and finally performing classification based on the hyperbolic geodesic distance. According to the method, the hierarchical modeling capability of the hyperbolic space and the semantic priori knowledge of the large language model are fully utilized, fine-grained multi-modal feature alignment is realized, and the small sample image classification performance is improved.

The invention discloses an unmanned surface vehicle gridding control method and system based on reinforcement learning, and relates to the technical field of unmanned surface vehicle intelligent control, and the method comprises the steps: dividing the control task of an unmanned surface vehicle into three levels of global navigation, local obstacle avoidance and energy management, and each level is optimized through an independent reinforcement learning model. Decisions among the layers are coordinated through meta-learning, and control strategies of the layers are dynamically adjusted according to task requirements. And after each level of control strategy is optimized, dividing the navigation area of the unmanned ship into a plurality of grid units, and optimizing the control decision in each grid unit according to the real-time environment data. According to the method, through a three-step control architecture design of hierarchical modeling-task coordination-space autonomy, significant progress is made in the aspects of task decomposition, control precision and energy consumption optimization, and an evolvable control foundation framework is provided for autonomous operation of the unmanned ship in a complex and changeable marine environment.

The invention provides an engineering machinery working environment layered modeling and planning method and system, and the method comprises the steps: carrying out the modeling based on the scanning point cloud data of an engineering machinery working region, the geometric point cloud of an object in the working region, and the aerial photographing image and aerial photographing point cloud data of the working region; a bottom geometric perception layer, a middle semantic planning layer and an upper topological navigation layer are obtained, and a corresponding model is reconstructed when an object moves, so that the contradiction between high-precision calculation power waste and low-precision modeling insufficiency caused by a current single-precision map is solved, the modeling calculation efficiency is improved, and the method can be expanded to different complex scenes.

The invention discloses a vulnerability detection method based on local and global message hierarchical modeling and fusion, and belongs to the field of network security. According to the method, source codes are converted into AST, CFG and DDG graph structures, a hierarchical feature extraction network is constructed, a piecewise function is adopted for local message modeling, a message matrix is generated based on node distances and feature differences, an exponential attenuation function is used for strengthening interaction of adjacent nodes, and Gaussian attenuation is adopted for noise suppression of far adjacent nodes; feature global sorting difference, a path attenuation factor and a degree balance factor are introduced in global message modeling, and cross-graph long-range dependence is captured; and a local / global message matrix is fused by self-adaptively allocating weights by using the graph diffusion entropy, and a feature extraction strategy is dynamically optimized. Multi-graph features are subjected to weighted fusion through a self-attention mechanism, semantic complementarity is enhanced, and finally a classification layer is input to realize vulnerability detection. According to the method, the problem of insufficient long-range dependence modeling of the traditional graph neural network is effectively solved, and the accuracy and robustness of vulnerability detection are remarkably improved.

The invention belongs to the technical field of power system resource scheduling, and provides a building flexible resource load optimal scheduling method and system based on a game mechanism, and the method comprises the steps: obtaining a distributed architecture of a building flexible resource load; performing department-level and building-level layered modeling on the acquired distributed architecture to obtain a department-level model and a building-level model; carrying out distributed optimization solution on the constructed department-level model and building-level model by adopting an alternating direction multiplier method to obtain a distributed collaborative model; considering an aggregator, and constructing a single-leader-multi-follower game model which takes the transaction center as a dominant and takes the aggregator and a power generator as followers; and solving the constructed game model, and completing optimal scheduling of the building flexible resource load based on the game mechanism.

The invention relates to the technical field of artificial intelligence service development and intelligent operation and maintenance, and discloses a system for developing and designing an AI service, and the system comprises a multi-dimensional target configuration module which defines a business target and a KPI, and generates an initial configuration parameter; the life cycle layered modeling module is used for receiving parameters and constructing a digital twinborn model in a layered (macroscopic, mesoscopic and microscopic) manner; the global prediction optimization module is used for executing multi-objective optimization based on digital twinning and generating a global control strategy; the distributed intelligent execution module is used for executing a global strategy in a microcosmic layer by an intelligent agent and performing local autonomous optimization; the intelligent resource management module is used for dynamically distributing system resources according to strategies and task requirements; and the monitoring module collects execution and resource data and is used for updating the digital twinborn model and adjusting a global strategy. According to the method, AI service accurate modeling, global intelligent optimization, efficient cooperative execution and continuous closed-loop self-adaption are realized.

The invention discloses a power grid material vehicle path planning method, and provides a space-time hierarchical modeling and cloud edge collaborative optimization architecture aiming at the problems of dynamic embargo, channel capacity fluctuation, multi-resource conflict, response lag and the like faced by power grid material dispatching in the prior art. By constructing a space-time coupled channel capacity-path planning hierarchical model, multi-commodity network flow pre-calculation and real-time vehicle path dynamic decoupling are carried out, and material distribution is optimized in combination with a space-time double-coding genetic algorithm; a multi-agent deep game decision-making mechanism is designed, channel priorities of different types of materials are dynamically balanced through reinforcement learning, and distributed conflict resolution is achieved; a block chain-based cross-domain scheduling instruction synchronization system is developed, low-delay path re-planning is realized by adopting a differential updating mechanism, and second-level updating of a scheduling scheme under an emergent road condition is supported; the on-chain evidence storage technology is introduced to guarantee instruction integrity and operation tracing, and the scheduling deadlock risk is reduced in combination with a key channel competition early warning model.

The invention provides a general visual unsupervised defect detection method based on hierarchical reconstruction, and the method comprises the steps: firstly processing an input image through a self-attention mechanism and a hierarchical jump-connection reconstruction module, carrying out the hierarchical modeling and reconstruction of a foreground, a background and a mask of the image through the reconstruction module, and generating the multi-dimensional reconstruction representation of an original image; by means of the design, structural decoupling can be achieved, the model is made to learn accurately, and complex background textures are reconstructed; then, a defect judgment module is used for judging the feature distribution of the original image and the reconstructed image so as to determine whether the input sample has defects or not; according to the method, global semantic information and multi-level structure features are fused, the robustness, accuracy and real-time performance of defect detection are effectively improved, and the method is suitable for complex and changeable industrial visual detection scenes and has good application prospects.

The invention provides a design method and system for efficiently querying a bank data warehouse based on an MPP architecture, and the method comprises the steps: S1, collecting and cleaning data from a plurality of bank business systems, and loading the data to a Greenplum distributed data warehouse; s2, performing hierarchical modeling in the warehouse, constructing a customer center wide table, and storing data in each node in a balanced manner by adopting partition and hash distribution strategies; s3, designing a batch processing task scheduling process, and improving the batch processing efficiency by utilizing parallel execution; s4, optimizing configuration indexes and materialized views according to the queries, and accelerating query response by utilizing parallel computing of the MPP architecture; and S5, monitoring a system load and dynamically and elastically scaling cluster resources to deal with service peak and valley demands. By means of the steps, high-performance batch processing and real-time query support of EB-level bank data are achieved on the GreenplumMPP cluster, the data processing efficiency and the query response speed are remarkably improved, and good expansibility is achieved so as to meet the service development requirement.

The invention belongs to the field of image similarity calculation, and provides an Euclidean space and hyperbolic space fusion-based image similarity calculation method, which comprises the following steps of: introducing exponential mapping on the basis of a deep neural network, embedding Euclidean features into a hyperbolic space, and combining a pseudo-sorting label generation strategy and a hierarchical sorting loss function design to obtain an image similarity calculation result. And hierarchical modeling and optimization of the image similarity are realized. According to the method, the stability of the Euclidean space and the structure expression ability of the hyperbolic space are both considered, the similarity calculation precision is improved, and meanwhile the good training efficiency and application expansibility are achieved.

The invention discloses a method for predicting an RNA (Ribonucleic Acid) methylation site, which is based on a multi-modal feature fusion and semantic vector embedding technology and is used for remarkably improving the recognition precision of an m7G modification site. The method comprises the following steps: firstly, constructing an RNA sequence data set containing positive and negative samples, and dividing the RNA sequence data set into a training set and an independent test set according to a predetermined proportion; then, multi-modal features are extracted through One-hot coding (One-hot), nucleotide chemical property coding (NCP), electron-ion interaction potential coding (EIIP) and local nucleotide composition coding (ENAC), and context semantic information of a nucleotide sequence is obtained in combination with a DNA2Vec model; according to the model, a multi-modal feature fusion path (MRF) and a DNA2Vec embedding path are adopted, after feature dimension compression is carried out through a full connection layer, a Transform encoder is used for capturing a long-range dependency relationship, and a prediction probability is calculated through a Sigmoid activation function. In the optimization process, batch normalization, Dropout and Adam optimizers are adopted, and the binary cross entropy loss function is minimized. Finally, the performance of the model is evaluated through five-fold cross validation, and the generalization ability of the model is verified on an independent test set. According to the method, through multi-source feature fusion and hierarchical modeling, the analysis capability of sequence information is remarkably improved, and an accurate calculation tool is provided for RNA modification prediction.

The invention relates to the technical field of new energy power systems, in particular to a new energy consumption prediction and early warning method, system, equipment and medium, and the method comprises the steps: collecting and preprocessing multi-source data in real time, and constructing a feature vector to support hierarchical collaborative prediction: firstly, training a new energy power generation prediction model based on historical weather and power generation data; secondly, combining load characteristics and influence factors to establish a load prediction model, and finally combining real-time parameters of a power grid to construct a consumption capability prediction model; early warning is triggered by dynamically comparing the generated power with the consumption capability predicted value, and a source-grid-load-storage cooperative control strategy is generated to execute regulation and control; the data coupling relation is deeply mined through the hierarchical modeling architecture, accurate quantification of the consumption potential and risk prospective early warning are achieved, the prediction timeliness and the power grid toughness are remarkably improved, power abandoning is effectively restrained, and the operation economical efficiency is optimized.

The invention relates to the technical field of posture recognition, in particular to a motion posture recognition method and system based on deep learning, and the method comprises the following steps: based on a human body motion image sequence, analyzing a skeleton center space trajectory, adjusting an acquisition visual angle, recognizing staged skeleton nodes, and generating a node time sequence trigger sequence; and optimizing joint point angle feature grouping and expression to obtain a hierarchical feature expression structure. In the invention, through detection for interference dynamic change characteristics, visual angle adjustment and node staged response in a time sequence are combined, compensation reasoning of a space trajectory is enhanced, and the hierarchical expression capability of motion capture is improved; the input features have the advantages of actively screening background disturbance, dynamically adapting the motion direction, separating action core nodes and perfecting node response association and grouping angle time sequence structures, the integrity and identification degree of action feature structure expression and hierarchical modeling are guaranteed, and higher identification accuracy and complex environment adaptability are brought.

The invention discloses a debris flow formation mechanism analysis and risk assessment method based on multi-modal data fusion. The method comprises the steps that S1, debris flow multi-modal data are collected and preprocessed; s2, constructing a condition vector; s3, generating debris flow risk scene data according to the condition vector; s4, evaluating the authenticity probability of the generated data by using a discriminator in the conditional generative adversarial network; s5, performing hierarchical modeling on the generated debris flow risk scene data by adopting a multi-scale image convolution layer to obtain updated node feature representation; s6, calculating a global debris flow risk prediction result, and generating a debris flow risk assessment report; s7, finely adjusting the generator and the discriminator according to the actual debris flow event occurrence condition; and S8, performing real-time evaluation and early warning on the debris flow risk. According to the method, an efficient and scientific optimization scheme can be provided in debris flow formation mechanism analysis and risk assessment, and remarkable technical values and economic benefits are brought to practical application.

The invention discloses a system engineering meta-model generation method based on a scene and a DM2 theory, and relates to the technical field of system engineering, and the method comprises the steps: obtaining scene description information related to a target business scene; performing multi-level analysis on the scene description information to obtain a corresponding service model; performing hierarchical modeling and conversion on the business model based on the hierarchical architecture of the DM2 framework to generate structured model data; and according to the expansion requirement of the system engineering field, performing field expansion and integrated processing on the model entity and the semantic relationship in the structured model data to generate a system engineering meta-model. Through the method, the technical problems of incomplete sorting of the meta-model, separation from actual business and poor ductility in a traditional mode are effectively solved.

The invention relates to the technical field of wind power generation, and discloses a wind power plant clustering method based on transient and steady-state rotating speed data, which comprises the following steps: collecting dynamic data based on fault time sequence key nodes, and extracting five-dimensional feature vectors of transient and steady-state rotating speeds of a doubly-fed wind generator as clustering indexes; noise data is adaptively separated and classified through a density clustering algorithm, and abnormal value interference is eliminated; the homogeneous units are clustered and combined into an equivalent model in combination with a hierarchical modeling strategy, and a detailed model is reserved for heterogeneous units; dynamically fusing multi-dimensional parameters by adopting a capacity weighting method, a wind energy conservation principle and a power weight, calculating equivalent generator, transformer, wind speed and system parameters, and evaluating and optimizing model precision through errors; according to the method, the problems of insufficient dynamic response characterization, abnormal value sensitivity and parameter coupling deficiency in a traditional method are solved, and the simulation precision of a wind power plant equivalent model and the engineering applicability of interaction analysis of a power system are remarkably improved.

The invention discloses a hierarchical OvO-SVM nondestructive testing method for the attack degree of potato blackheart disease, which comprises the following steps: preparing a detection sample, and collecting a VIS / NIR original transmission spectrum; preprocessing the collected original spectrum; adopting a random forest algorithm to screen sensitive wavebands; a two-stage hierarchical classification framework is constructed, health / disease coarse classification-disease degree subdivision is carried out, cross validation type Platt calibration is carried out on decision scores of each group of SVMs, and corresponding probabilities are output; according to the method, a classification threshold value is introduced, the final grade of the morbidity degree is judged through the maximum value of a pseudo probability / threshold value, and through the technical path of spectrum preprocessing, sensitive band screening, two-stage hierarchical modeling, probability calibration and classification threshold value shaping, hierarchical modeling and OvO local boundary learning are utilized to judge the morbidity degree. According to the method, the boundary confusion problem of'health-light 'and'moderate-heavy' is remarkably relieved, the spectrum dimension is reduced by screening sensitive wavebands, and accurate grading of the attack degree of the blackheart disease is achieved.

The invention relates to the technical field of complex equipment data management, in particular to a model-driven complex equipment data relation automatic construction method and system. The method comprises the following steps: constructing a business entity meta-model standard framework comprising a base layer and a domain extension layer through a meta-model technology, and establishing a dual-check system; a hierarchical modeling method is adopted to construct a data model system, and model management is performed through a multi-source heterogeneous data reference mechanism and semantic version control; constructing a data association relationship by adopting a multi-modal fusion method and integrating structural analysis and semantic reasoning; and finally, four-dimensional quality evaluation of integrity, consistency, timeliness and accuracy is carried out on the data association relationship. According to the method, the problems of non-uniform data standards, low association efficiency and incomplete change transmission in complex equipment development are solved, and an automatic closed loop from data association construction to quality control is realized.

The invention relates to the technical field of intelligent traffic simulation and heavy vehicle cooperative control, and discloses an intelligent cooperative control simulation verification system for a tractor or a trailer, which comprises three modules and four modules: a three-dimensional environment modeling module based on multi-source data fusion and hierarchical modeling and combined with a timing and event double-trigger mechanism to update dynamic obstacles; the cooperative motion control module integrates Bezier trajectory planning, a geodesic theoretical dynamics coupling model and triangular collision detection, and realizes vehicle-towing cooperation and risk early warning. The visual simulation interaction module constructs a multi-window view based on OpenGL / Unity 3D, and simulates light and shadow and friction characteristics in combination with material attributes; the result verification module outputs a conclusion based on the quantitative index and the qualitative report. According to the system, the goodness of fit between a simulation environment and a real scene is remarkably improved, the collision early warning response is less than or equal to 100ms, more than 80% of roll-on-roll-off ship scenes can be covered, the entity test cost is reduced by 60%, and a high-fidelity verification tool is provided for vehicle-tow intelligent cooperation.

The present invention provides a supply chain multi-level warehousing intelligent scheduling and collaboration method and system, which relates to the field of intelligent warehousing technology. The method comprises the following steps: based on historical order data, a long short-term memory network is used to perform hierarchical modeling and prediction of the short-term and long-term demands of warehouses at all levels to obtain inventory demand; each warehouse node is set as an independent intelligent agent, and an inventory allocation strategy is generated through iterative optimization of strategies between intelligent agents; the inventory allocation strategy is converted into a scheduling instruction containing the allocation object, allocation quantity and allocation time based on a rule knowledge base; the scheduling instruction is received and sequenced for execution in the edge computing unit of each warehouse node; when an inventory anomaly or resource conflict is detected, an emergency collaboration request is initiated to the adjacent warehouse node; the warehouse node that receives the emergency collaboration request returns response information based on its own resource status, and based on the response information, an emergency treatment plan is determined through local negotiation between nodes.