422 results about "Decision maker" patented technology

The invention relates to the technical field of computing power scheduling, and discloses a heterogeneous resource computing power intelligent scheduling method and system. According to the method, real-time state monitoring is conducted on heterogeneous computing resources, and resource state parameters such as the computing unit utilization rate and the memory occupancy rate are obtained; task attributes and user request parameters of the task queue are collected, historical task data are processed based on the genetic algorithm optimization model to execute task demand prediction, and predicted demand parameters are generated. A dependency graph containing resource unit nodes and communication link roadsides is constructed through a resource topology analysis tool, predicted demand parameters are input into a scheduling priority classifier trained by a graph neural network, and an actual scheduling priority is identified. And executing resource conflict prediction based on the priority, inputting task feature vectors into a conflict resolution module of a fuzzy logic decision maker, outputting actual conflict resolution parameters, and finally integrating to generate a scheduling scheme containing a resource allocation sequence and an execution time table.

The invention provides a deep learning training and reasoning task dynamic cooperation system based on GPU space-time resource sharing, and the system comprises a GPU resource state perceptron which is used for monitoring a GPU kernel function call sequence and a video memory distribution state of a distributed training task in real time, dynamically capturing a calculation gap and video memory fragments generated by the training task, and transmitting the calculation gap and the video memory fragments to the GPU resource state perceptron; generating a two-dimensional resource spatial-temporal characteristic spectrum, and predicting a GPU calculation idle period caused by communication synchronization based on an LSTM model; the kernel function dynamic scheduling decision maker is used for performing priority division and dynamic resource quota allocation on an online reasoning task and an offline reasoning task by adopting a self-adaptive allocation strategy on the basis of a resource spatial-temporal characteristic spectrum so as to realize spatial-temporal resource decoupling of the training task and the reasoning task; and the kernel function execution arbiter is used for dynamically controlling submission and blockage of the reasoning task kernel function according to the scheduling decision through video memory space multiplexing and a calculation instruction arbitration mechanism. According to the method, the GPU resource utilization rate is remarkably improved, on the premise that stable training task performance is guaranteed, fragmented resources are effectively utilized to support parallel execution of multiple types of reasoning tasks, and efficient resource collaboration of a deep learning task cluster is achieved.

The invention relates to a multi-source data fusion-based urban physical examination evaluation index calculation method and system. The method comprises the steps of extracting a multi-source data sequence; identifying a data source of the urban physical examination index set, and extracting an independent time sequence data sequence; calculating the information entropy of the independent time sequence data sequence, and distributing a basic fusion weight; calculating a dynamic state evaluation value of the independent time sequence data sequence, and performing weighted fusion on the basic fusion weight and the dynamic state evaluation value to obtain a comprehensive state evaluation value; obtaining a distribution variance of the basic fusion weight, inputting the distribution variance into the uncertainty quantification model, and obtaining an index calculation result containing uncertainty measurement; the real-time performance of the evaluation result is enhanced through an aging attenuation mechanism, and the latest state of the city system is accurately reflected; the output uncertainty measurement index provides a quantitative basis of result credibility for a decision maker, and the decision risk caused by a data fusion error is reduced.

The invention relates to the technical field of ecological protection, and discloses a method for evaluating the stability of an ecological system in an arid region, which comprises the following steps: S1, constructing an index system, S2, performing multi-source remote sensing fusion, S3, performing response modeling on the ecological system, and S4, constructing a stability evaluation matrix in a sensitivity dimension; the method comprises the following steps: S1, constructing four-quadrant stability classification to carry out index weighting, S5, intelligently collecting ground sample points, and then combining an ecological change hot area graph, S6, carrying out model dynamic feedback optimization, and S7, outputting a result and carrying out zoning application, namely dividing a key protection and restoration area and supporting regional ecological safety pattern construction and continuous management. Through fusion of remote sensing data and intelligent acquisition and analysis of ground samples, dynamic updating and real-time monitoring of stability evaluation of the ecological system in the arid region are realized, and the effects of accurately reflecting ecological changes and providing efficient and timely data support for decision makers are obtained.

The invention discloses a building facade optimization method and system based on sustainable analysis, and relates to the technical field of data processing. According to the method, parametric modeling is carried out on the external facade of a target building, and a design space is defined; training a machine learning model integrating energy consumption, carbon emission, cost and lighting prediction functions by using historical data; building an optimization problem by taking external facade parameters as decision variables and taking minimization of energy consumption, carbon emission and cost and maximization of lighting as targets, and automatically generating an optimal scheme set by adopting a multi-target optimization algorithm; a decision maker is assisted to select a final scheme through a visual interface, and a corresponding BIM model is output; the method effectively solves the problems that a traditional design method is difficult to coordinate multi-target conflicts, depends on experience and is low in efficiency, and can automatically and intelligently generate an optimal design scheme which is energy-saving, low-carbon, low-cost and good in lighting performance.

The embodiment of the invention provides a heterogeneous aircraft low-altitude management method and device, and the method achieves the dynamic generation of a global avoidance decision through the innovative building of an aircraft dynamics model library, and the combination of a distributed cooperative avoidance network and a multi-objective optimization algorithm. And designing a flight rule identification model, fusing visual, instrument and digital flight rules, and realizing dynamic grouping and airspace distribution based on task similarity through an airspace management decision maker. And a task priority evaluator is constructed, a conflict resolution optimization model is trained based on historical data features, and avoidance instruction optimization under multi-target constraint is realized. According to the method, the defects of the traditional technology in the aspects of cooperative avoidance, rule recognition, conflict resolution and the like are effectively overcome, and the safety and efficiency of heterogeneous aircraft low-altitude management are remarkably improved.

The invention discloses a Token level cache matching method and system of a large language model and a storage medium. The method comprises the following steps: constructing a local context fragment; generating a context embedding vector of the current Token, and calculating a context entropy value, a semantic consistency index and a semantic change gradient; for the target Token, determining a semantic category of the target Token; inputting the semantic category to which the target Token belongs into a Hash decision maker to obtain a Hash granularity level; if the Hash is the first-level Hash, executing fixed-length Hash and mapping the Hash to a semantic cache bucket based on a semantic theme or a context range of the first-level Hash; if the first-level hash is the second-level hash, dynamically adjusting the hash length according to the semantic similarity with the adjacent Token; if the Hash granularity level of the target Token is a third-level Hash, constructing a high-dimension context representation and executing a fine Hash operation; and matching the hash result with the KV in the pre-stored cache, and executing corresponding operation according to the matching result.

The invention relates to the technical field of digital twinning, in particular to a digital twinning task scheduling system and a digital twinning task scheduling method oriented to multi-machine cooperation, which realize intelligent mapping between resources and tasks by introducing a topological resource space model. Comprising a topological resource space builder, a task analysis and modeling device, a task resource mapping decision-making device, a global monitoring and optimizing unit and a data fusion processor, the topological resource space builder builds a topological space model of a resource and task mapping relation, and the task analysis and modeling device extracts feature information and a dependency relation of a digital twin task; a task resource mapping decision maker generates a task scheduling decision based on a topological space model and feature information, a global monitoring and optimizing unit dynamically adjusts connectivity parameters of the topological space model according to execution information, a scheduling strategy is optimized, the resource utilization rate is increased, and a data fusion processor merges execution result data and improves the task scheduling efficiency. And through multi-dimensional task feature analysis and intelligent decomposition combination, complex tasks are flexibly dealt with, and the execution efficiency is improved.

The embodiment of the invention discloses a human and AI collaborative large-scale group decision-making method and equipment based on a trusted network. The method comprises the following steps: constructing a decision-making problem; performing initial evaluation on the alternative scheme through a human decision maker set and an AI decision maker set to obtain initial evaluation information; constructing a human and AI trusted network; establishing a continuously learned human and AI consensus reaching algorithm, and dynamically updating the human and AI trust network and the initial evaluation information through the human and AI consensus reaching algorithm to obtain final evaluation information; calculating the weight of each attribute in the alternative scheme and the weight of each decision maker according to the final evaluation information; the final score of each alternative scheme is calculated, and a final decision is generated; the decision-making response speed, precision and decision-making consistency can be effectively improved, and the method is suitable for the complex decision-making field.

The invention discloses a multi-objective optimization-based multi-commodity order fulfillment method and device, equipment and a medium, and relates to the technical field of supply chain management. The multi-commodity order fulfillment method comprises the following steps: acquiring order and inventory information including a current order set, a commodity set and a warehouse inventory set; and modeling an order fulfillment problem based on an actual demand of fulfillment of a multi-commodity order in the order and inventory information. The established model is a multi-objective optimization problem containing three objectives. Generating an initial population by adopting a heuristic method based on priority and inventory feasibility; and carrying out iterative optimization on the initial population by adopting a rapid non-dominated sorting genetic algorithm with elitism based on a double-layer elitism strategy to obtain a Pareto non-dominated solution set. And selecting a final order fulfillment scheme from the Pareto non-dominated solution set according to the preference of a decision maker. According to the method, three core operation indexes are synchronously improved under the condition of meeting feasibility constraints such as inventory, processing capability and demand.

The invention discloses a multi-objective optimization water resource allocation scheduling method, which combines an LSTM-Prophet hybrid prediction model with an attention mechanism, synchronously captures climate periodicity, policy regulation sensitivity and industrial development tendency characteristics in water demand prediction, and improves water demand prediction precision through a genetic algorithm. Through an improved NSGA-III algorithm and a PSO collaborative optimization mechanism, the convergence problem of a high-dimensional target space is effectively solved in combination with a chaotic mapping technology, and the multi-target optimization efficiency is improved by 40% in cooperation with a dynamic weight adjustment strategy of deep reinforcement learning. And multi-scenario simulation verification of a scheduling scheme is realized by utilizing deep coupling of a digital twin system and a three-dimensional GIS, and a real-time feedback correction mechanism of a Markov decision process is realized. A finally constructed entropy weight-TOPSIS multi-dimensional evaluation system is combined with AR visualization and intelligent contract traceability technologies, and a Pareto optimal solution giving consideration to social fair, economic benefits and ecological integrity is provided for decision makers.

The invention provides a bridge group multi-target maintenance decision-making method fusing an evolutionary algorithm and artificial intelligence, and relates to the technical field of civil engineering and artificial intelligence crossing. The method comprises the steps of defining bridge group maintenance cost and structure failure risks, representing preferences of decision makers for different decision targets by weight combinations, and constructing a multi-target maintenance decision optimization model; encoding the weight combination into an individual of a multi-objective evolutionary algorithm, and randomly generating an initial population; aiming at each generation of weight combination, constructing a bridge group Markov decision-making environment; learning an optimal maintenance strategy by adopting an A2C training reinforcement learning agent; the optimal maintenance strategy is evaluated, and an evaluation result is used as individual fitness to be fed back to the multi-objective evolutionary algorithm; using a multi-objective evolutionary algorithm to perform evolutionary search on the multi-objective weight combination; and through a closed-loop feedback mechanism, outputting a Pareto optimal solution set containing an optimal maintenance strategy under various weight combinations, thereby realizing collaborative optimization of weight optimization and strategy learning.

The invention provides an intelligent recommendation method, system and device for MySQL dynamic parameters and a medium, and belongs to the technical field of databases. The method comprises the following steps: collecting structured performance indexes in real time, extracting unstructured text knowledge, and storing the unstructured text knowledge into a data lake; performing cleaning, fusion and feature extraction processing on the acquired data, constructing a time sequence data set, and generating a multi-modal feature vector; constructing a parameter optimization model fusing the text encoder, the parameter dependency graph encoder and the reinforcement learning decision maker, and training the parameter optimization model; according to the real-time system state vector and the user optimization target, using the parameter optimization model to output parameter adjustment suggestions, generating a parameter adjustment suggestion list, and predicting potential risks; according to the parameter adjustment suggestion, a progressive adjustment strategy is adopted to execute parameter adjustment operation, and the adjusted performance index is monitored in real time; and feeding back a parameter adjustment implementation result to a parameter optimization model training process, updating a model weight, generating a parameter adjustment case and storing the parameter adjustment case in a knowledge base.

The invention provides an emergency decision-making auxiliary system based on a knowledge graph. Comprising a data acquisition and processing module, a knowledge graph construction and reasoning module, an information retrieval and generation module, a multi-level reasoning and thinking chain prompting module, a multi-agent cooperation and game optimization module, an incremental learning and dynamic knowledge updating module and a block chain trust guarantee module. The emergency decision-making efficiency, accuracy, cooperation capability and safety are broken through, dynamic and complex emergency scenes can be effectively dealt with, and efficient, reliable and intelligent technical support is provided for decision makers.

The invention discloses a multi-agent collaborative visual navigation reasoning enhancement method and system, and the method comprises the steps: an inference agent analyzes input data, and generates structured output containing a reasoning process and an initial answer; independently analyzing the input data by the evaluator agent, generating an evaluation answer and reviewing the output of the inference agent; comparing whether the initial answer is consistent with the evaluation answer, and if yes, outputting a final answer; if not, calling a bifurcation feedback agent, and generating a core bifurcation point abstract and a visual evidence verification plan; and calling a final decision maker agent to directionally retrieve the video evidence according to the visual evidence verification plan, and outputting a final answer. Four roles of an inference person, an evaluator, a divergence feedback person and a final decision maker are introduced, a complete'generation-evaluation-feedback-optimization 'decision link is constructed, and the cognition and inference accuracy in a complex scene is remarkably improved.

The invention belongs to the technical field of robot task planning, and particularly relates to a robot closed-loop task planning method and system based on a double-track re-planning strategy. Comprises: using a large language model to generate an action sequence based on a to-be-executed task; before the action of each step is actually executed, the action executability of the action of the current step is judged; after each step of action is actually executed, action execution success judgment is carried out on the current step of action; when it is judged that the action cannot be executed or execution of the current-step action fails, a replanning decision maker based on a large language model is triggered, selection is conducted between two replanning strategies of plan reconstruction and task replacement, a plan reconstruction or task replacement result is executed, and robot closed-loop task planning is achieved. According to the method, planning flexibility and environment constraint perception are considered, invalid attempts can be reduced, and the success rate and robustness of long-time tasks in a complex scene are improved.

The invention relates to the technical field of hydraulic engineering big data processing, in particular to a flood control scheduling method and system based on GraphRAG. Comprising a data acquisition and processing module, a knowledge graph construction module, a knowledge base construction module and a workflow arrangement module. Multi-source heterogeneous data related to flood control dispatching are collected through the data collecting and processing module, the knowledge graph construction module constructs a flood control dispatching knowledge graph by applying semantic comprehension and generation capacity of a large model, the knowledge base construction module enhances a knowledge base of GraphRAG on the basis of the knowledge graph, and the flood control dispatching knowledge graph is constructed. And the workflow arrangement module arranges a large language model workflow based on a business process, realizes knowledge retrieval and reasoning, provides scheduling scheme adjustment suggestions for decision makers in combination with real-time data feedback and visual display, and improves timeliness and flexibility of flood control scheduling.

The invention relates to the technical field of computers, and discloses an intelligent scheduling system and method for a high-efficiency charging platform, and the method comprises the steps: constructing a four-dimensional constraint model integrating the power grid load, the user demand, the equipment health degree and electricity price prediction, and employing a depth deterministic strategy gradient algorithm to drive a multi-target dynamic scheduling decision maker, the global optimization distribution of the charging resources in the space-time power dimension is realized, and strategy reconstruction is completed within 10s when a power grid emergency instruction or a device fault occurs. The system comprises a power grid sensing module, a user acquisition module, a health assessment module, an electricity price response module, a scheduling decision module, an instruction execution module and an emergency reconstruction module, and supports millisecond-level adaptive evolution. According to the method, the weighted reward function is constructed by quantifying the four indexes of power grid stability, user satisfaction, equipment loss and platform income, and the instruction verification and steady-state adaptive mechanism is combined, so that the user experience is synchronously improved, the service life of the equipment is prolonged, and the operation energy consumption is reduced on the premise of ensuring the safety.

The invention discloses a target threat assessment method based on a large language model, and the method comprises the following steps: carrying out the modeling of a confrontation environment, integrating multi-dimensional information, and determining a plurality of threat assessment targets; generating an evaluation index through the large language model; evaluating the generated evaluation indexes in combination with expert knowledge, if the evaluation indexes do not meet the requirements, further generating the evaluation indexes by the large language model, and if the evaluation indexes meet the requirements, generating an evaluation matrix by the large language model; performing evaluation by combining an evaluation matrix symmetrically formed by expert knowledge, if the evaluation matrix does not meet requirements, further generating the evaluation matrix by the large language model, and if the evaluation matrix meets the requirements, calculating the distance between each target and the positive and negative ideal solutions; and performing threat level sorting on the target according to the distance. By optimizing the decision process, more efficient threat assessment in a complex confrontation environment is realized, and decision support with higher reference value is provided for decision makers.

The invention relates to a drip irrigation pipe network zoning irrigation control method and system based on multi-objective optimization, and belongs to the technical field of artificial intelligence. The method comprises the following steps of collecting multi-source heterogeneous data and sampling point space coordinates, after abnormal value elimination and normalization processing, splicing features and normalized coordinates to form a joint feature vector, and obtaining irrigation partitions with continuous space and similar features through K-means clustering; constructing a multi-objective optimization model taking the zoning irrigation duration and the inlet target flow as decision variables; solving by adopting a hybrid strategy multi-objective evolutionary algorithm of chaotic mapping initialization and adaptive hybrid variation to obtain a Pareto optimal solution set; and finally, combining an entropy weight method and decision maker preference, determining an optimal irrigation parameter through a superior and inferior solution distance method, and introducing a real-time soil moisture monitoring feedback correction parameter. Partitioned precise irrigation of the drip irrigation pipe network can be achieved, multiple optimization objectives are balanced, the irrigation effect and the resource utilization efficiency are improved, and system energy consumption is reduced.

The invention relates to the technical field of cigarette supervision, in particular to a cigarette retail store supervision route planning method, which solves the problems of data missing and heterogeneity through data cleaning and standardization processing, and provides high-quality basic data for subsequent analysis. Due to the application of the analytic hierarchy process, scientific quantification of multi-dimensional indexes is realized, and the reliability of an evaluation result is improved; the improved ant colony algorithm is combined with pheromone concentration parameter adjustment, so that the routing inspection path planning efficiency and pertinence are optimized; key problems can be quickly positioned by introducing an anomaly detection algorithm, and the timeliness and accuracy of supervision are enhanced; a clustering analysis algorithm reveals a potential rule of a regional problem by grouping abnormal stores and calculating spatial distribution density; the thermodynamic diagram visualization technology presents the distribution of supervision key areas in a visual mode, and provides clear information support for decision makers; dynamic supervision and trend analysis are achieved through interactive function design, and scientificity and flexibility of supervision decision making are further improved.

The invention provides a neural architecture search-based multi-modal automatic modeling and fusion method and device, and relates to the technical field of computer science. The method comprises the following steps: acquiring multi-modal data and a task type; automatically generating a corresponding optimal unit architecture for each modal data based on neural architecture search, and performing feature extraction on each modal data; according to a high-dimensional feature of each modal data, analyzing a dependency relationship between modals of a feature level to generate a dynamically updated correlation thermodynamic diagram, obtaining a specific fusion strategy according to a fusion strategy decision maker, and constructing an optimal fusion network architecture; and an output layer and a loss function are automatically adjusted according to task types, a network meeting task requirements is obtained, and different downstream tasks are completed. According to the method, end-to-end joint optimization of single-mode feature extraction and a multi-mode fusion strategy can be realized, the manual intervention cost and GPU resource consumption are remarkably reduced, and meanwhile, the task performance and the model generalization ability in a complex scene are improved.

The invention provides an anti-fraud method and system based on multi-modal perception and cross-time feature modeling. The method comprises the following steps: receiving a multi-modal financial transaction graph sent by an edge computing node; inputting the multi-modal financial transaction graph into a risk control model for risk analysis to obtain a node risk score of each node, an edge risk score of each edge and a gang risk score of each sub-graph; the risk control model is a multi-scale ST-Transform-GCN framework, and the risk control model is a multi-scale ST-Transform-GCN framework; processing the node risk score, the edge risk score and the gang risk score through a gating fusion mechanism to obtain a comprehensive risk score; based on the node risk score, the edge risk score and the gang risk score, extracting a suspicious transaction sub-graph from the multi-modal financial transaction graph; and inputting the comprehensive risk score and the suspicious transaction sub-graph into a preset reinforcement learning decision maker to obtain an anti-fraud strategy, and executing the anti-fraud strategy to achieve the purpose of accurately identifying the complex collaborative fraud behavior.

The invention discloses an intelligent decision-making method and system based on deep learning, and relates to the field of data processing. The method comprises the steps of obtaining place data and corresponding attribute data of a to-be-decided project; establishing a fuzzy relation matrix between the places and the attributes; generating a network model reflecting trust relationship strength among users through trust propagation operation of the graph neural network; identifying a community structure containing a community overlapping degree through a community discovery algorithm; and according to the trust relationship strength between the users and the community overlapping degree, calculating an influence weight of a decision maker, forming a group consensus through a robust optimization method, and generating a decision result of the project to be decided. Aiming at low network relation modeling precision caused by multi-source heterogeneous data in bus station layout decision making in the prior art, the method and the device have the advantages that the network relation modeling precision is low through accurate modeling of an information propagation path in a complex trusted network, effective dimension reduction representation of a high-dimensional feature space and robust optimization solution in an uncertain environment; therefore, the calculation precision and robustness of the bus station layout intelligent decision-making system are improved.

The invention provides a power transmission and transformation project full-life-cycle carbon emission design system based on multi-objective optimization, and the system comprises a full-life-cycle carbon emission database module which is used for storing carbon emission factors and technological parameters of a power transmission and transformation project in each stage; the engineering scheme modeling module is used for generating an engineering scheme including a standardized bill of materials and a construction list based on the carbon emission factors and the process parameters in combination with engineering parameters and design data; the carbon emission evaluation module is used for evaluating the full-life-cycle carbon emission of the engineering scheme; the multi-objective optimization module is used for realizing synchronous optimization of the carbon emission and various constraint conditions to obtain a plurality of engineering scheme solution sets; and the decision support and visualization module is used for visually displaying the target optimization solution set and the carbon emission of the whole life cycle, automatically generating an engineering scheme for comparative analysis, and assisting a decision maker to quickly select an optimal scheme. And through a multi-objective collaborative optimization method, synchronous optimization of full-life-cycle carbon emission and economic objectives is realized.

An equipment system optimization method driven by deep reinforcement learning comprises the following steps: sequentially constructing an equipment system architecture model and a combat scene simulation model based on a DoDAF framework, then carrying out parameter space sampling in the equipment system architecture model by using a uniform design method, and according to capability items and equipment elements defined by the equipment system architecture model, carrying out parameter space sampling on the equipment system architecture model; constructing a combat effectiveness evaluation model and a system cost evaluation model for evaluating the system; constructing a parameter-modulated deep reinforcement learning (PM-DRL) model to explicitly embed system parameters into an agent state space, and performing data collection and evaluation through the trained PM-DRL model to obtain lt; system parameter-evaluation result gt; a data set; and finally, constructing an optimization model by taking the agent model as a target function, and determining an optimal solution, namely system parameter configuration, by combining the solved Pareto frontier with the preference of a decision maker. According to the method, under the same combat effectiveness requirement, system parameter configuration with lower construction cost can be obtained through optimization.

The invention provides a power transaction agent system based on AI auxiliary decision making, the system can provide intelligent power transaction service based on an intelligent contract, the system is responsible for processing massive, high-frequency and multi-dimensional data, providing deep insight and accurate prediction, liberating traders from heavy data work, and improving the working efficiency of the traders. The method focuses on strategy formulation and risk control of transactions. The system can provide questioning and answering services for power transactions for users, and can perform power transaction prediction and visualization in each mode based on collected power transaction big data, so that power market intelligent analysis, retrieval and other services of traders, analysts and decision makers can be deeply endowed, multiple functions are integrated, and the user experience is improved. The system assists traders, users, managers and the like, realizes intelligent service and lightweight service of power transaction, reduces transaction cost, and improves market circulation efficiency and benefits of power transaction.

The invention belongs to the field of intelligent workshop scheduling, and particularly discloses a large model enabled workshop scheduling end-to-end self-decision engine, method and device. According to the method, the large model is positioned as a core decision maker instead of a traditional auxiliary tool, so that the powerful capabilities of the large model in the aspects of natural language understanding, context learning, complex logical reasoning and content generation are fully utilized; according to workshop real-time state information, a dynamically changing workpiece pool, a scheduling target input by a user through a natural language and emergency description, an autonomous and end-to-end scheduling decision can be directly carried out, so that the rapid response capability and intelligent processing level of a scheduling system to dynamic events are improved, and the scheduling efficiency is improved. And the application threshold is reduced through natural language interaction, the dependence on explicit programming and rule customization is reduced, a low-code development paradigm is developed, and the method has a far-sighted decision potential exceeding a traditional heuristic rule.

The invention provides a remote sensing image interpretation method based on large model reasoning and tool enhancement, which comprises the following steps: generating an initial reasoning node of a reasoning tree by using a large language model as an active decision maker based on system cue words and input remote sensing image features; based on the current inference tree state, the active decision maker executes inference and generates a new inference node, so that the inference tree is expanded; based on node scores, adopting a pruning strategy to eliminate reasoning paths with low scores; repeating the steps until a reasoning termination condition is met, and selecting a path with the highest accumulated score from the reasoning tree as an optimal reasoning path; and key information is extracted and organized into a structured intelligence report to be output. According to the method, a complete interpretation chain of'thinking-tool-observation 'is explicitly modeled through a tree reasoning mechanism, so that the generation logic of each intelligence conclusion is clear and visible, and the problems of trust crisis and insufficient interpretability caused by'black box' decision of a traditional deep learning model are solved.

Systems and methods for third-party evaluation of workspace orchestration services are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution by the processor, cause the IHS to: evaluate a workspace orchestration service based, at least in part, upon (i) anonymized orchestration data received from the workspace orchestration service, and (ii) synthetic user data produced based, at least in part, upon user data and notify an Information Technology Decision Maker (ITDM) of the evaluation.