63426 results about "Operations research" patented technology

The invention provides a dynamic path planning method and system in an intelligent traffic system, and relates to the technical field of intelligent traffic. Constructing a dynamic road network traffic capacity model based on a space-time diagram convolutional network; generating a multi-branch trajectory prediction result with a confidence score, adaptively starting an encryption parameter synchronization mechanism in combination with vehicle density, and generating a candidate path set through multi-agent collaborative game optimization; performing multi-dimensional deviation degree evaluation based on actual driving data and planning expectation; synchronously generating a multi-mode cooperative guidance signal; the system comprises a multi-source data fusion unit, a space-time modeling engine, a hierarchical decision-making system, a path verification and execution module, a closed-loop feedback controller, an event response system and a multi-mode man-machine interface. According to the method, the global resource utilization efficiency is improved through cooperation of data-driven modeling and hierarchical game decision, and the real-time performance, the safety and the system adaptability of path planning are improved through a closed-loop feedback and event response mechanism.

The invention discloses a multi-modal fusion AGV dynamic path planning and cluster scheduling system, and relates to the technical field of multi-modal perception and data fusion, and the system comprises a multi-modal perception module which generates a dynamic obstacle confidence map through multi-source data fusion in combination with a hardware-level time synchronization and Transform feature fusion network; the dynamic path planning module adopts an improved rolling window algorithm, integrates an LSTM space-time conflict prediction model and an adaptive weight cost function, and realizes dynamic obstacle trajectory prediction and non-oscillation global path generation; the cluster scheduling control module is used for optimizing multi-AGV task allocation and conflict resolution in combination with a dynamic priority preemption mechanism and digital twinborn simulation rehearsal based on a distributed contract network protocol of edge computing; and the data conflict resolution module is used for triggering a multi-modal re-calibration process through confidence weighting and sliding window time sequence verification. According to the system, in logistics storage and intelligent manufacturing scenes, the dynamic obstacle avoidance success rate and the robustness and operation efficiency of an AGV cluster are improved.

The invention discloses a multi-element sales planning agent system and method, and aims to improve the intelligence and precision of sales planning. The system comprises a collection module, an analysis module, an optimization module, a creation module and a generation module. The collection module is used for receiving multi-modal data such as marketing targets and extracting key marketing elements. The analysis module is used for generating a target user portrait and extracting marketing strategy analysis data. And the optimization module is used for calculating a medium putting weight by utilizing reinforcement learning and generating a medium strategy scheme. And the creation module generates a propagation theme and marketing content by adopting a generative artificial intelligence technology. And the generation module predicts a delivery effect by using a machine learning model and dynamically optimizes a medium strategy and a content scheme. Through multi-modal data fusion, intelligent analysis and optimization, closed-loop processing from data acquisition to marketing execution is realized, the marketing decision-making efficiency is improved, and brand promotion accuracy and market adaptability are enhanced.

The invention discloses an intelligent power grid optimal scheduling method and system based on multivariate energy storage cooperative scheduling, and relates to the technical field of power grid optimal scheduling, and the method comprises the following steps: building a prediction model based on first data, generating prediction data, coupling energy storage characteristic parameters of different types of energy storage equipment with the prediction data, and obtaining a prediction model; establishing a multi-energy collaborative scheduling model; dynamically screening the energy storage scheduling strategy set based on a preset real-time performance evaluation index to generate an optimal strategy subset; according to the optimal strategy subset, performing differentiated charging and discharging control instructions on the energy storage equipment cluster; and collecting second data in the charge and discharge control process, calculating a deviation value between the second data and the prediction data, converting the deviation value into a feature vector, inputting the feature vector into a preset incremental learning algorithm, and optimizing parameters of the multi-energy collaborative scheduling model. Layered screening is implemented in combination with real-time performance evaluation indexes, and it is ensured that the optimal scheduling scheme can be rapidly selected in different time periods and under the uncertain disturbance condition.

A method is provided for estimating the number of distinct tokens in a text stream using a modified text-to-text variational autoencoder (T5VQVAE) model. The method includes receiving a continuous input of a text stream; dynamically maintaining a buffer that stores a probabilistic subset of tokens from the text stream; calculating a sampling probability for each token based on a condition related to the current state of the buffer; updating the buffer based on the sampling probability to include or exclude tokens; encoding the buffered tokens into a latent space using the T5VQVAE model; and estimating the number of distinct tokens in the text stream based on the tokens in the buffer and the corresponding sampling probabilities.

The invention relates to the field of bidding risk assessment, and discloses an AI technology-based intelligent bidding data analysis method and system and a storage medium, and the method comprises the following steps: obtaining bidding data, constructing a multi-dimensional space-time tensor, and extracting low-dimensional features through dynamic tensor decomposition; establishing a dynamic enterprise relationship graph, modeling an enterprise cooperation relationship by using a graph neural network, and capturing a bidding behavior mode between enterprises; a multi-target joint optimization model is constructed, and market trend prediction, enterprise credit assessment and risk early warning are synchronously realized; performing adversarial training by adopting a generative adversarial network, and dynamically adjusting task weights in combination with Pareto optimization to balance multi-task conflicts; and outputting a market prediction report, an enterprise credit portrait and a risk early warning list, and providing policy and regulation change influence analysis and a risk evidence chain. According to the method, bidding and tendering data are deeply mined through the AI technology, the market insight and the abnormal bidding behavior recognition capability are improved, and the method has wide application value.

The invention provides a supply chain multi-dimensional fluctuation prediction and inventory optimization method based on artificial intelligence, and relates to the technical field of supply chain management, and the method comprises the steps: obtaining multi-dimensional data, carrying out the preprocessing of the multi-dimensional data, generating a standardized time series data set and a knowledge graph, carrying out the multi-scale decomposition through wavelet transformation, and extracting fluctuation features; and constructing a state matrix by combining a graph attention network and a multi-head self-attention mechanism, predicting a risk conduction path based on historical fluctuation pattern matching and a graph convolutional network, and generating a risk prevention and control scheme. According to the invention, the fluctuation characteristics and the risk conduction path in the supply chain system can be accurately identified, and the early warning accuracy and the response timeliness are improved.

The invention provides a network security protection method and system applied to regional digital and intelligent asset businesses, and the method comprises the steps: collecting asset data flows of a plurality of asset business nodes in a target region, carrying out the threat feature extraction of the asset data flows based on a preset threat knowledge graph, and obtaining a threat feature extraction result; generating a dynamic threat feature vector corresponding to the asset service node; inputting the dynamic threat feature vector into a pre-trained dynamic protection model, and outputting a real-time protection strategy adaptive to the asset service node through a multi-layer decision network in the dynamic protection model; performing strategy execution on the network flow of the asset service node based on the real-time protection strategy, generating a strategy execution result and feeding back the strategy execution result to the dynamic protection model; and performing adaptive optimization on decision parameters of the dynamic protection model according to a strategy execution result, and generating an updated dynamic protection model for a protection decision of a next round of asset business nodes.

The embodiment of the invention provides an industrial real-time data transmission guarantee method and system based on a TSN. The method is applied to the field of industrial communication and intelligent scheduling, and comprises the steps of obtaining industrial field network node information, completing TSN topology deployment and time synchronization, extracting communication characteristics and historical load data of various task flows, constructing a unified time sequence, and inputting a prediction model to generate a load prediction result. And in combination with task parameters, a scheduling strategy list and a prediction result, inputting a TSN digital twin network model to carry out analogue simulation, evaluating performance indexes under multiple strategies, selecting an optimal scheduling strategy and issuing the optimal scheduling strategy to TSN switching equipment, thereby realizing accurate scheduling control configuration of various task flows. According to the scheme, intelligent evaluation and automatic optimization issuing of the scheduling strategy are realized, the real-time performance and the deterministic communication capability of an industrial network are remarkably improved, the resource utilization efficiency and the adaptability of the scheduling decision are enhanced, and the key task flow is ensured to be reliably transmitted on time.

The invention relates to the technical field of artificial intelligence and enterprise decision making, and discloses an intelligent decision making system and method based on an enterprise life index large model, and the system comprises a multi-source heterogeneous data collection module which is used for obtaining and associating various data in real time, and generating target enterprise multi-source heterogeneous data; the dynamic index system generation module is used for sequencing feature importance through a graph neural network and generating a dynamic evaluation index set containing survival, risk and growth indexes; the enterprise life index large model building module is used for fusing index sets, capturing enterprise state evolution, embedding risk factors and outputting multi-dimensional enterprise life indexes; the intelligent decision dynamic generation module is used for inputting the indexes into the decision strategy tree model and matching a strategy library to generate a decision instruction set; and the closed-loop feedback optimization module is used for realizing closed-loop optimization by acquiring execution data and combining Bayesian optimization dynamic correction parameters. According to the invention, the accuracy and timeliness of enterprise intelligent decision making can be improved.

The invention discloses a supply chain warehouse management system and method based on AI digital intelligence, and relates to the technical field of intelligent warehousing, and the method comprises the steps that a data processing unit collects supply chain system data through a data processing module, and constructs an associated data network containing goods information, shipping space information and order information based on the supply chain system data; the goods allocation unit executes a goods allocation algorithm according to the associated data network, extracts an association score based on goods information, analyzes a collaborative ex-warehouse frequency, and calculates an affinity matrix; the storage scheme unit generates a storage position scheme based on the affinity matrix; the job scheduling unit constructs a scheduling model, performs task optimization and outputs a task allocation sequence; the order picking navigation unit is provided with an order picking path planning module, presents a path and guidance, collects execution data, and optimizes the system through analysis. The problems that in traditional warehouse management, the goods allocation efficiency is low, task scheduling is not optimized, and the goods picking path is unreasonable are solved.

The invention discloses a logistics robot path planning method based on multi-modal perception, and relates to the technical field of robot path planning. Laser radar, visual camera and IMU data are fused, and environment state feature vectors are generated through multi-modal data synchronization and space-time alignment; the method comprises the following steps: analyzing environmental semantics by using models such as PointPill and YOLOv8, extracting dynamic characteristics, and identifying obstacles; constructing a space-time risk field, searching a path by a space-time algorithm, converting path points into a continuous trajectory, and optimizing the continuous trajectory; deviation is evaluated in real time, dynamic re-planning is triggered, and multi-robot cooperation and environment semantic understanding are included. Through multi-mode perception fusion, hierarchical planning, multi-target optimization and a cooperation mechanism, the obstacle detection accuracy and the obstacle avoidance success rate are improved, the path planning time is shortened, the energy consumption is reduced, the multi-robot conflict is reduced, the task efficiency is improved, the environment semantic understanding and task adaptive ability is enhanced, and the method is suitable for scenes such as intelligent storage and the like and has wide application prospects. The automation level is improved.

The invention discloses a park energy router multi-agent coordination control method based on a dynamic game model, and relates to the technical field of energy internet optimization control. The method comprises the following steps: according to real-time meteorological data and historical load fluctuation characteristics, identifying multi-energy supply and demand time sequence conflicts, constructing a multi-subject game framework, quantifying dynamic coupling strength of meteorological parameters and load fluctuation, and generating a dynamic constraint boundary; then, combining dynamic constraints and operation states, dynamically adjusting economic, reliability and low-carbon index weights, superposing a risk penalty term of a future supply and demand difference value, and constructing a coordination revenue function; when the photovoltaic output and the thermal load change rate exceed threshold values, an emergency game mode is triggered, Nash equilibrium is solved through a distributed optimization algorithm, and a coordination control instruction is output; and finally, collecting execution data, calculating a supply and demand deviation correction rate and an income balance degree, adjusting a coupling weight and a constraint boundary based on feedback, and realizing multi-subject benefit dynamic balance and stable and efficient operation of the park energy system.

The invention discloses a territorial space layout adjustment decision method based on planning conflict identification, and belongs to the technical field of territorial space planning and management. Comprising the following steps: constructing a space-time conflict feature vector library containing multi-dimensional attributes; generating a semantic knowledge graph containing a causal relationship, a sequential relationship, a spatial relationship and an affiliation relationship; accurate prediction of influence intensity and duration of the conflict state along with time lapse is realized; trend analysis and risk level evaluation of a cross-scale conflict propagation path are realized; automatically identifying dominant conflicts in the current layout and potential conflicts based on propagation prediction, and intelligently classifying composite conflicts by using fuzzy clustering; a staged differentiation conflict intervention strategy recommendation scheme is automatically generated; solving a territorial space layout adjustment scheme; judging whether the layout adjustment scheme meets a preset conflict relieving standard or not;

The invention discloses a computer task scheduling method based on artificial intelligence, and the method comprises the following steps: 1, data collection: employing a double-flow feature fusion mechanism, and generating global feature representation containing long-term dependence and an instantaneous state; step 2, generating a global optimization scheduling strategy: constructing a hierarchical federal reinforcement learning system, dividing a cluster into a plurality of super nodes through an enhanced spectral clustering algorithm, independently training a Dueling DQN network by each super node, performing global strategy cooperation by adopting Shapley value weighted aggregation and differential privacy protection, and generating a scheduling strategy of global optimization; distilling a global strategy into a lightweight decision tree through a strategy distillation technology, and deploying the lightweight decision tree to a physical node; 3, task priority control and elastic resource allocation are carried out, wherein elastic control over resource allocation is carried out through a dynamic time slice bank mechanism; and 4, self-adaptive evolution: establishing a closed-loop optimization system, and carrying out strategy self-evolution by adopting a double-layer optimization architecture.

The invention provides a multi-level attribution and recommendation method and system for medical management decisions, and relates to the technical field of medical management decisions, and the method comprises the steps: extracting graph node evolution characteristics through a multi-level medical data knowledge graph and a decision knowledge base by employing a time sequence diagram convolutional network, constructing a decision influence propagation graph through a cross-layer interactive attention network, and carrying out the analysis of the decision influence propagation graph; calculating the contribution degree of each decision node to the medical effect, generating an attribution analysis result, determining a decision influence hierarchical system according to the attribution result, and generating and evaluating candidate decision paths in combination with a multi-objective optimization network, a hierarchical reinforcement learning network and a Monte Carlo search tree network. In conclusion, accurate attribution analysis and multi-objective optimization of the management decision can be realized, the hospital resource allocation efficiency is effectively improved, the operation cost is reduced, scientific decision support is provided for a hospital management layer, and the hospital fine management level and the overall operation efficiency are remarkably enhanced.

The invention relates to the technical field of computer network security and data transmission, and discloses a computer network security data transmission method and device, and the method comprises the steps: obtaining real-time threat intelligence data, a real-time packet loss rate and a real-time transmission performance parameter, carrying out the encryption, and obtaining encrypted fragmented data; inputting the real-time transmission performance parameters into a path performance evaluation model to obtain a path performance score; sorting according to the path performance score to obtain a transmission path set; according to the transmission path set, matching to obtain a sequence corresponding relation; generating complete transmission data according to the sequence corresponding relation; performing anti-attack optimization according to the real-time transmission performance parameters to obtain a link connection strategy; analyzing the link quality according to the real-time packet loss rate to obtain a transmission link quality score; and performing dynamic adjustment according to the transmission link quality score, the link connection strategy and the complete transmission data to obtain real-time transmission data. According to the method, real-time and complete computer network security data transmission can be realized.

The invention discloses a collaborative sharing method, system and device on an energy data chain fusing multi-party security calculation and zero knowledge proof and a storage medium, and belongs to the field of energy data security sharing and block chain privacy protection, and the method comprises the steps: executing desensitization processing on original data according to types, and generating a cryptographic commitment value and a data hash identifier; encrypting and fragmenting the desensitized data, registering a task structure body through an intelligent contract, and uploading the task structure body; and each node decrypts the ciphertext, executes calculation and generates a zero-knowledge proof, and submits the zero-knowledge proof to the block chain to be verified by the smart contract. According to the method, data privacy protection and result verifiability unification are realized, cross-mechanism cooperation efficiency is improved, an auditing system on a whole process chain is constructed, and safe sharing of energy data is guaranteed.

The invention provides a construction method of a double-path collaborative decision network for multi-agent collaborative path optimization, and the method comprises the steps: obtaining multi-source heterogeneous real-time business data and historical business data, and constructing a standardized data set; converting the standardized data set into a dynamic environment input sequence; at least two domain agents perform full-process business logical reasoning based on the dynamic environment input sequence to generate a decision chain structure covering the full life cycle of the business; implementing full-flow path planning simulation based on the decision chain structure, and generating an initial path scheme including multi-resource collaborative scheduling; the environment perception intelligent agent puts the initial distribution path scheme into an actual logistics distribution scene for execution, and loads real-time environment change information to determine a logistics decision chain weight; and the logistics strategy agent optimizes the initial distribution path scheme based on the logistics decision chain weight to obtain a logistics business knowledge graph so as to construct a logistics double-path collaborative decision network.

The invention discloses a cross-industry data security sharing method and system based on data desensitization and a medium, and relates to the related field of privacy computing, and the method comprises the following steps: carrying out dynamic desensitization processing on original data, generating a shared data set for real-time recording, constructing an access behavior characteristic spectrum to configure a dynamic access strategy, and establishing a third-party security data sharing platform, storing the shared data set in a trusted execution environment in a fragmented manner, obtaining fragmented encrypted data, executing a dynamic access strategy, performing conjoint analysis on the fragmented encrypted data through a security multi-party computing protocol, and generating a data security sharing suggestion. The technical problems that in cross-industry data sharing, the privacy leakage risk is high, the static desensitization rule is rigid, and the multi-party calculation efficiency is low are solved, and the technical effects of breaking data islands and constructing safe and mutual-trust data circulation ecology through fusion of dynamic hierarchical desensitization and a trusted execution environment are achieved.

The invention discloses a government affair data sharing system based on a data security law risk control mode, and relates to the technical field of data management. According to the method, full-process closed-loop management from data classification and grading, differential desensitization, law and regulation compliance review, access authority precise control to behavior trajectory real-time analysis and risk trend prediction is realized, and the risk trend prediction is realized by introducing key technologies such as a context sensitive desensitization algorithm, legal knowledge graph reasoning, behavior portraits, semantic trajectory similarity scoring and risk rise rate monitoring. The method effectively improves the recognition and response capability of the hidden data re-recognition risk, and achieves the safe, controllable, compliant and ordered intelligent risk control management of government affair data in the sharing process.

The invention relates to the technical field of smart power grids, and discloses a smart power grid power dispatching optimization method, which comprises the following steps of: firstly, acquiring power grid operation data, and processing data missing and noise problems by utilizing federal learning; and constructing a load prediction model through a dynamic time warping algorithm and a specific network. A multi-energy coupling scheduling model and a demand response game model are constructed, and a multi-time scale rolling optimization framework is established. And carrying out sensitivity analysis on scheduling parameters, designing a hierarchical collaborative optimization mechanism, and constructing a robust optimization model to cope with the power flow uncertainty. And integrating a scheduling instruction verification module, and deploying an online incremental learning mechanism. The method can effectively process data, accurately predict load, optimize multi-energy scheduling, guide demand response, deal with uncertainty, verify scheduling instructions and update the model in real time, improves the safety, reliability and economy of smart grid power scheduling, and realizes optimal configuration of power resources.

The invention discloses a perception intelligent driving complex traffic scene dynamic risk prediction method, and relates to the technical field of risk prediction, and the method comprises the steps: obtaining the multi-source traffic dynamic data of a target region in real time; performing space-time alignment processing on the multi-source traffic dynamic data; inputting the space-time coupling feature matrix into a pre-trained depth space-time prediction model to generate a risk thermodynamic map; calculating a dynamic risk index of each traffic sub-region, and generating a risk level distribution sequence; and triggering a self-adaptive early warning response mechanism according to the risk level distribution sequence, and dynamically adjusting operation parameters of the variable information sign and the traffic signal controller. The technical problems of frequent traffic congestion and high accident risk caused by inaccurate traffic risk prediction and difficulty in dynamic adjustment according to the real-time traffic condition in the prior art are solved, and the technical effects of accurately predicting the traffic risk and improving the safety and traffic efficiency in a complex traffic scene are achieved.

The invention provides a supply chain multistage warehousing intelligent scheduling and collaboration method and system, and relates to the technical field of intelligent warehousing, and the method comprises the steps: carrying out the hierarchical modeling prediction of the short-term and long-term demands of each stage of warehouse through employing a long-short-term memory network based on historical order data, and obtaining the inventory demand; setting each warehouse node as an independent intelligent agent, and generating an inventory allocation strategy through strategy iteration optimization among the intelligent agents; converting the inventory allocation strategy into a scheduling instruction containing an allocation object, an allocation quantity and allocation time based on a rule knowledge base; and in the edge computing unit of each warehouse node, receiving a scheduling instruction and performing sorting execution, when inventory abnormity or resource conflict is detected, initiating an emergency cooperation request to an adjacent warehouse node, returning response information by the warehouse node receiving the emergency cooperation request according to the own resource state, and performing scheduling according to the response information. And determining an emergency processing scheme through local negotiation between the nodes.

Disclosed in the present invention is an optimized dispatching method for a virtual power plant, which method is applied to a virtual power plant dispatching control center in a virtual power plant dispatching system. The method comprises: predicting next-day wind power and photovoltaic output values and power demand conditions of electric vehicles in a load aggregator on the day ahead; acquiring a next-day output scheme reported by each output unit in a virtual power plant dispatching system, wherein the next-day output scheme is determined by each output unit on the basis of a day-ahead electricity selling price, electric-vehicle charging and discharging strategies and the output characteristics and costs of other output units; and performing coordinated optimization on the basis of the next-day output schemes reported by the output units and a virtual power plant optimized dispatching model which takes demand responses and environmental costs into account, so as to obtain a final next-day output plan, and issuing the final next-day output plan to each output unit, such that each output unit performs execution on the basis of the final next-day output plan. The present invention makes load dispatching in a virtual power plant more flexible.

The invention relates to the technical field of cross-border logistics matching, in particular to a cross-border e-commerce logistics dynamic matching optimization method and system based on big data driving. The method comprises the following steps: obtaining a real-time cross-border e-commerce order information flow, calculating a logistics timeliness demand of a user, and carrying out freight duration period calculation to obtain an optimal freight timeliness window; carrying out transfer node matching degree calculation one by one based on a cross-border e-commerce order flow, and carrying out intelligent adaptation selection so as to extract a predicted planning transfer node; a historical logistics execution log is obtained, dynamic path planning and logistics transportation mode intelligent selection are carried out according to the predicted planning transfer node, and an intelligent path planning strategy is constructed; collecting meteorological data flow, shipping notice information and shipping abnormal events of each transfer node, and constructing a multi-modal disturbance factor model; according to the method, the optimal logistics path is dynamically adjusted through real-time logistics environment change perception, and efficient, accurate and intelligent logistics requirements in a cross-border logistics scene are met.

The invention relates to the technical field of intelligent data fusion deduction, in particular to a task planning method and system based on intelligent deduction, and the method comprises the steps: constructing a rule knowledge graph with weight annotation through integrating a real-time battlefield situation and a preset rule library, and carrying out the task planning through the real-time battlefield situation; a large-model intention understanding engine is used for analyzing a commander instruction to generate a five-dimensional intention vector, an initial scheme is generated by combining a double-engine mechanism of a rule hard constraint engine and a large-model soft constraint engine, and graph weights are dynamically adjusted through reinforcement learning to optimize a rule path. The system monitors node conflict frequency in real time, adopts a near-end strategy optimization algorithm to re-distribute weights for high-frequency conflicts, activates a new rule injection mechanism when the intention conformity is insufficient, and finally outputs a three-level structured scheme including a main scheme, an alternative scheme set and a risk early warning report, thereby realizing an intelligent closed loop from situation awareness to scheme generation. And the command decision-making efficiency and the combat collaboration are obviously improved.

The invention belongs to the technical field of multi-target global dynamic production scheduling, and discloses a manufacturing resource dynamic production scheduling data comprehensive processing method, which comprises the following steps: acquiring multi-source heterogeneous data, constructing a field mapping table, constructing an equipment fingerprint database in combination with historical equipment performance data, dynamically adjusting confidence coefficient weight, and further judging an equipment state. Based on an equipment state judgment result, integrating operator report data and multi-source heterogeneous data through a preset rule base trigger event, and generating an abnormal event flow with a context tag; based on the abnormal event type and the equipment state judgment result, a recommendation scheme is constructed through a production scheduling knowledge graph, and a production scheduling instruction set is generated; according to the production scheduling instruction set, generating a local optimization scheme and a global optimization strategy, and forming a final execution scheme; calculating a resource utilization rate and a bottleneck process index through the 3D virtual workshop model, and generating a difference analysis report; according to the method, global resource scheduling and closed-loop iteration are realized, and the production efficiency and the system robustness are improved.

The invention discloses a freight logistics information intelligent tracking management method and system, and belongs to the technical field of freight management, and the method comprises the steps: obtaining unique identification information of a freight unit, building the association with order content, carrier information and a preset transportation path, constructing a freight entity relation graph, and carrying out the management of the freight logistics information; performing preprocessing and modal fusion on multi-source real-time data in a transportation process, and extracting a structured cargo state feature set; based on a dynamic space-time modeling mechanism, the current freight state is intelligently judged, and a risk prediction result set is generated; predictive reconstruction is carried out on the transportation path in combination with a risk result; automatically executing path reconstruction, vehicle scheduling or user notification after the abnormal event is identified; synchronizing the evolution state and the risk data to a cloud platform for subsequent task optimization; according to the invention, full-flow intelligent sensing, real-time scheduling and abnormal closed-loop response in the logistics process are realized, and the transparency, robustness and aging control capability of a transportation system are remarkably improved.

The invention discloses an artificial intelligence-based engineering resource allocation optimization method and system, and relates to the technical field of artificial intelligence and resource management crossing. The problems of resource configuration dynamic change and resource conflict coordination are solved. And processing the multi-modal heterogeneous data in the target engineering scene through space-time alignment and semantic coding to generate a dynamic data stream. A task, resource and environment node relationship is constructed based on a dynamic heterogeneous graph network, and a node dependency weight is updated through an event-driven mechanism. Two-stage collaborative optimization is executed, in the first stage, a baseline scheme is generated through Monte Carlo sampling, and in the second stage, resource conflicts are eliminated through back propagation negotiation. The resource dynamic entropy is monitored in real time, a rebalance algorithm is triggered during local overload, and a distribution scheme is adjusted in combination with security constraints. And finally, outputting the optimization scheme to an engineering management system for execution, dynamically returning updated graph network parameters, forming a data acquisition, optimization decision and feedback closed loop, and improving the intelligence and robustness of resource allocation.