489 results about "Decision taking" patented technology

The invention relates to the technical field of enterprise management, in particular to an intelligent decision support system and method based on cognitive logic and scenarized semanteme, and the method comprises the steps: obtaining enterprise decision data through multi-source data monitoring, carrying out the preprocessing, and generating cross-modal enterprise scene cognitive information; analyzing cross-modal association among the data, fusing a cognitive logical reasoning rule and a semantic understanding model, and constructing scenarized semantic decision fusion features; training an enterprise decision-making quality evaluation model based on historical cases, performing quality perception on a current decision-making scene, and outputting decision-making quality information; and an execution effect is judged in combination with an expected target, an optimization mechanism is started if the target is deviated, candidate schemes are generated by using a historical knowledge base and a multi-target optimization algorithm, an optimal solution is screened, and intelligent adjustment of a decision scheme is realized. According to the method, multi-source heterogeneous data and cognitive logic are fused, semantic understanding, dynamic evaluation and adaptive optimization capabilities of a decision system are enhanced, and scientificity and real-time performance of enterprise decision are improved.

The invention discloses an agent inference system based on multiple maps, and relates to the technical field of artificial intelligence, and the system comprises the steps: based on industry report, academic literature and business manual multi-source data, extracting entity-relationship-attribute, and constructing a knowledge map; dynamically capturing cooperation, report and task allocation relationships among entities, and constructing a production relationship graph; constructing a decision graph based on a field expert heuristic rule; based on the thinking engineering theory, human thinking modes and emotional states are analyzed, and a thinking map is constructed; integrating a knowledge graph, a production relation graph, a decision graph and a thinking graph, performing entity and mode alignment, mapping multi-source nodes and edges into the same vector space, introducing conflict resolution, and constructing a unified multi-mode heterogeneous knowledge graph; and guiding a large language model to generate a reasoning direction through path cue word injection, querying a constraint reasoning boundary, outputting an optimal reasoning result, and realizing agent reasoning of multiple maps. The method has the beneficial effect that the reasoning accuracy is improved.

The invention relates to the technical field of material management, and discloses a dynamic rule generation and self-adaptive auditing system and method for material management, and the system comprises a rule intelligent extraction module, a rule management knowledge base, an enhanced auditing engine, a man-machine cooperation calibration module and a self-adaptive execution module. The method comprises the steps of automatic rule extraction, rule storage and management, enhanced auditing and reasoning, man-machine collaborative calibration, knowledge base real-time optimization and adaptive routing execution. According to the method, the rule is automatically extracted from the unstructured document, the problem that a traditional system rule depends on manpower and is lagged in updating is solved, dynamic optimization of the rule and confidence is achieved by introducing a man-machine collaborative feedback closed loop, the accuracy and transparency of an audit decision are improved by enhancing reasoning and explainable decision technologies, and the audit efficiency is improved. And the optimal balance between auditing efficiency and risk control is realized through self-adaptive routing execution based on credibility.

The invention discloses a multi-agent collaborative decision-making system and method based on knowledge Token, and the method comprises the following steps: S1, constructing a knowledge base architecture, executing the content Hash calculation, and generating an original fingerprint in an original fidelity layer; s2, extracting content, packaging the content into knowledge Token, and writing identification, semantics, source, authority, value, contract, association, traceability and health information; s3, activating a knowledge layer, constructing a vector index and mapping knowledge graph nodes; s4, unifying an index layer, matching task requirements, and screening and verifying candidate Tokens to obtain an authorization set; s5, the multiple agents generate local decisions and fuse values and semantics to form a collaborative result; and S6, binding a Token identifier after consistency verification, generating a collaborative decision and recording data consanguinity information. According to the method, multi-agent collaborative decision-making is realized through the knowledge Token and the knowledge graph, and the decision-making accuracy and traceability are improved.

The invention discloses a large language model auxiliary optimization strategic decision-making system and a large language model auxiliary optimization strategic decision-making method. The system comprises six core modules. The dynamic knowledge fusion module constructs a three-layer distributed knowledge network, constructs an entity association weight matrix through a bidirectional Transform model based on an attention mechanism, and realizes knowledge dynamic association in combination with a time attenuation factor and a hybrid coding technology. The large language model module performs field fine tuning by adopting incremental pre-training and low-rank adaptation technologies, and introduces an exclusive word segmentation list to improve professional analysis precision. The full-process intelligent writing module covers submodules for report generation, revision and the like, and supports full-life-cycle management of reports. The strategic decision intelligent deduction module integrates scene impact factors, and realizes multi-scene deduction through reinforcement learning and Monte Carlo tree search. The interaction display module provides a visual interface, and the multi-mode interaction module realizes full task chain management. According to the invention, real-time knowledge support and intelligent deduction capability are provided for strategic decision making, and decision making efficiency and accuracy are improved.

The invention discloses a semantic judicial contract compliance automatic generation and examination system based on a DIKWP artificial consciousness model. The system is constructed according to five layers of data, information, knowledge, wisdom and intention, wherein the data layer integrates multi-source information; the information layer enables terms to be semantized and disambiguated; the knowledge layer performs rule matching and compliance judgment by using a legal knowledge graph; the wisdom layer automatically corrects the risk terms based on a fair principle and performs scene simulation; and the intention layer is embedded into targets of all parties to ensure that the contract is consistent with the compliance intention. The system process covers the whole process from intention analysis, knowledge reasoning to intelligent generation and intention negotiation, and finally a compliance contract and an audit report are output. The system is provided with a multi-language semantic adaptation and intelligent negotiation interface, and convenient access can be realized through an API (Application Program Interface). The system realizes integration of contract generation and review, intention driving and decision interpretability, can effectively reduce compliance cost and dispute risks, and is suitable for multiple scenes such as finance, e-commerce, government purchase and the like.

The invention provides a layered multi-agent-based full-process intelligent decision-making method and system for mineral development. The method comprises the following steps: constructing a three-level intelligent decision-making main body-based full-process layered collaborative decision-making architecture for mineral development; designing a communication protocol; and establishing a quantitative coupling relationship between the cross-link coupling constraint and the process parameters. And constructing a state space and an action space of the three-level intelligent decision-making main body. Designing a global objective function, a link objective function and a key parameter collaborative optimization model; and establishing an adaptive weight optimization mechanism. And designing a distributed collaborative optimization mechanism based on an alternating direction multiplier method. And implementing an agent collaborative decision-making process of the evolutionary game. The selected decision scheme is converted into a specific production instruction to be issued and executed, the execution effect and feedback data are monitored in real time, and the strategy is dynamically adjusted and optimized. According to the invention, the multi-objective collaborative optimization of the full life cycle of mineral development is realized, and the intelligent, green and sustainable development of mineral resource development is promoted.

The invention relates to the technical field of artificial intelligence knowledge maps, and discloses a customs emergency decision-making method and system based on a multi-agent large language model, and the method comprises the steps: constructing a knowledge map from an unstructured customs emergency procedure document through a multi-agent cooperation framework; the multi-agent cooperation framework at least comprises a regulation analysis agent, a key point screening agent, an emergency element extraction agent and a logic construction agent; and providing support for customs emergency decision-making problems of the user and giving customs emergency decision-making answers through a map navigation agent driven by a large language model on the basis of the constructed knowledge map. According to the application, high-fidelity automatic construction of the customs emergency knowledge graph can be realized, and the accuracy and integrity of knowledge are ensured; a static knowledge graph can be converted into an interactive dynamic decision support tool, and a full link from an unstructured regulation document to intelligent decision support is really opened.

The invention discloses a sewage plant multi-process-section collaborative intelligent decision fusion system, which comprises a sensing and rule fusion layer used for collecting inlet and outlet water quality parameters, process control parameters and operation state parameters of multiple process sections in real time, preprocessing data and fusing an expert rule base; the mechanism and data driving joint modeling layer is used for establishing a mechanism model and a data driving model under the constraint condition of the expert rule base and predicting control quantities respectively; and the collaborative optimization and fusion decision-making layer is used for executing cross-process-section multi-target collaborative optimization and fusion decision-making based on an output result of the mechanism and data driving joint modeling layer under the constraint condition of an expert rule base, calculating a dynamic fusion weight, generating a final control quantity, and issuing the final control quantity to execution equipment. And in combination with real-time feedback self-adaptive adjustment, closed-loop optimization is realized. According to the system, the sewage treatment stability, decision precision and resource utilization efficiency are improved, and the environmental risk is reduced.

The invention provides an order change-oriented AIAgent multi-task collaborative decision-making method and system, and the method comprises the steps: obtaining an order change basic data set containing historical order data and real-time change information, and constructing an order change association knowledge graph based on the order change basic data set; calling a multi-task collaborative decision AIAgent to perform multi-task collaborative analysis processing on the order change associated knowledge graph, and generating a collaborative decision result including task priority ranking, a resource allocation scheme and a risk coping strategy; and according to the collaborative decision result, generating an order change coping decision instruction including an execution node time planning and resource scheduling instruction, and sending the order change coping decision instruction to an order management system to trigger a dynamic adjustment operation. According to the invention, the comprehensiveness, coordination and operability of order change coping are effectively improved.

The invention relates to the technical field of artificial intelligence and decision support, and discloses a large model agent-based strategic decision generation method, which comprises the following steps of: performing semantic analysis and multi-target game balance on a strategic instruction by a main control agent, and decomposing to generate subtasks; a corresponding domain agent calls an RAG enhancement module to retrieve related multi-modal data, and a preliminary strategy of embedded compliance verification is generated based on the data; and finally, dynamically arranging and coordinating the preliminary strategy by a hierarchical workflow engine, and generating a comprehensive decision report after receiving strategy correction information of a commander. According to the invention, through multi-agent cooperation and multi-target game balance, the decision-making globality and strategic depth are realized; by fusing dynamic multi-modal data, the adaptive capacity of the decision to the external environment is improved; through workflow arrangement and man-machine cooperation, logic self-consistency, execution feasibility and safety of a final scheme are ensured.

Transactional Neural Reasoning AI (TNRAI) is a novel class of artificial intelligence designed to simulate human-like reasoning during live, multimodal user sessions. TNRAI departs from traditional static inference models by integrating a five-pillar architecture: (1) delta-path modeling for real-time outcome deviation detection, (2) skew-based adversarial recognition, (3) vector memory recall for behavioral context, (4) ambient reasoning overlays to incorporate situational data, and (5) a multi-logic arbitration engine that fuses rule-based, statistical, situational, and historical reasoning. The system evolves continuously via a CI / CD feedback loop, adjusting its logic and thresholds based on live outcomes. TNRAI supports overlays such as time or location constraints to influence reasoning and can activate conditional triggers based on logic patterns or confidence thresholds. This architecture enables adaptive, deliberative decision-making in real time, extending the utility of AI across domains such as automation, compliance enforcement, customer engagement, and transaction-based system control.

The invention discloses a flood control decision-making method and system based on multi-agent cooperation, and relates to the technical field of artificial intelligence and water conservancy information.The flood control decision-making method comprises the steps that a decision-making thinking chain template guided by domain business logic is constructed, and the flood control decision-making process originally depending on artificial experience is converted into an interpretable and traceable structured reasoning process; modularized disassembly is carried out on complex tasks in flood control business to form a standardized decision sequence, and a business framework is provided for the reasoning process of a large language model, so that each step of reasoning must be aligned with a preset decision target and call a specified knowledge demand; therefore, the common logic jump and constraint omission problems of a general large model in a professional scene are completely eradicated from the working principle, when a system processes user query, automatic matching is carried out, step-by-step advancing is carried out along the thinking chain template, and an intermediate result highly consistent with business operation is generated; effective alignment of artificial intelligence reasoning and domain expert thinking modes is realized.

The invention provides a large model collaborative reasoning and dynamic optimization method for oral clinical decision, and belongs to the field of artificial intelligence. A structured thinking chain auditing reasoning mechanism is constructed, and multi-source evidence fusion and traceable output are realized through a planner, an actuator and a verifier; designing a dynamic expert routing and risk gating mechanism, packaging a large language model, a knowledge graph, image analysis and the like into pluggable experts, dynamically selecting and fusing according to context, and supporting security degradation when evidence is insufficient; a continuous optimization closed loop driven by multi-source feedback is established, doctor adoption and editing behaviors and patient follow-up results are converted into multi-dimensional rewards, a model is updated in combination with reinforcement learning and preference alignment, and meanwhile, elastic weight consolidation and knowledge distillation are introduced to prevent catastrophic forgetting. The method effectively solves the problems of uninterpretability, uncredibility, static solidification and single capability of the model, significantly improves the transparency, robustness and safety of decision making, and is suitable for orthodontics, implantation, maxillofacial surgery and other high-risk scenes.

The invention relates to the field of data processing and information technology operation and maintenance, and discloses an intelligent operation and maintenance decision-making method and system based on multivariate heterogeneous data fusion. Comprising the following steps: constructing a dynamic causal graph reflecting a system state and an environmental factor causal relationship based on a causal structure learning algorithm; constructing a multi-agent game decision model based on the dynamic causal diagram, and respectively setting a global operation and maintenance target and a component local demand as a leader strategy and a follower strategy of the game; and solving game equilibrium by using multi-agent reinforcement learning, and outputting an optimal joint operation and maintenance decision instruction. The system is composed of a data acquisition preprocessing module, a multi-modal feature fusion module, a causal structure learning module, a game decision solving module and an execution monitoring module. According to the method, the causal inference and the game theory are fused, so that the causal logic can be accurately extracted, the decision robustness and interpretability are improved, and the global optimal collaborative configuration is realized.

The invention discloses a construction supervision decision-making method and system based on game optimization and multi-agent reinforcement learning, and relates to the field of construction supervision decision-making. According to the method, a digital twinborn environment based on a Stackelberg master-slave game architecture is constructed, feature extraction is performed on a dynamic space-time interaction graph of a construction site in combination with a graph attention network, and continuous differentiable hard security constraints are applied to a game process by using a security risk potential energy field based on a physical field theory; and a multi-agent reinforcement learning algorithm based on Lagrangian dual optimization is adopted to solve a Nash equilibrium strategy, so that a supervision agent and a construction / resource agent can realize automatic balance of risk avoidance-progress optimization in a complex dynamic game, and intelligent collaborative decision considering safety compliance and construction efficiency is realized.

The invention discloses a distributed multi-agent collaborative decision-making system based on a block chain trust mechanism, and relates to the technical field of agent collaboration, and the system comprises the steps: deploying a local decision-making client on each agent to achieve local perception, capability vector reporting, and local decision-making and federated learning clients; acquiring local execution data, resource occupation data and environment disturbance data of each agent in a historical auditing rotation period through a measurement and perception device, and extracting a capability vector, local observation characteristics and historical compensation factors by a local decision client; calculating a matching degree and an execution reliability index in each historical auditing rotation period to form a historical compensation sequence; and identifying a key process set and a task candidate scheme. According to the method, the bottleneck problems that efficiency and fairness cannot be considered at the same time in a traditional unified scheduling mechanism, and the overall construction period is blocked due to key process delay in a complex production process are solved, so that the robustness and scheduling precision of the system are improved.

The invention relates to a multi-agent decision conflict resolution method and device, network equipment, a storage medium and a program product. When communication intention information sent by the terminal equipment is received, task decomposition is performed on the communication intention information to obtain a plurality of sub-tasks, and the sub-tasks are allocated to a plurality of intelligent agents for analysis to obtain analysis results of the intelligent agents; when it is determined that decision conflicts exist among the multiple agents according to the analysis results of all the agents, the type of the current conflict among the multiple agents is determined, and a solution of historical conflicts corresponding to the type of the current conflict is obtained; and carrying out resolution processing on the current conflict according to the solution to obtain a processing result. According to the technical scheme, the communication intention is decomposed to generate the multiple sub-tasks, the sub-tasks are distributed to the multiple agents, cooperative operation of the agents is achieved, when decision conflicts exist among the multiple agents, the current conflicts are processed based on the historical conflict solution, and the accuracy of multi-agent conflict resolution is improved.

The invention relates to an artificial intelligence and multi-agent system technology, in particular to a multi-agent collaborative decision-making method and system based on a large language model, and the system comprises a perception analysis agent, a collaborative decision-making agent, a resource scheduling agent, an execution monitoring agent and a learning optimization agent. The system carries out linkage evaluation through three quantitative indexes of global reward value variance, accuracy improvement rate and cooperation strategy consistency, sets a differentiation threshold value and yellow, orange and red three-level early warning, accurately recognizes training states such as strategy oscillation, local optimum and strategy instability, triggers a progressive response from enhanced monitoring to immediate intervention, and improves the early warning efficiency. The system failure risk caused by abnormal training is obviously reduced; after the early warning is triggered, the system starts a structured four-step analysis process including anomaly positioning, causal verification, root cause determination and report generation, and outputs an executable report including an evidence chain and optimization suggestions by calling a large language model and automatically executing verification codes, so that the operation and maintenance transparency and the decision-making efficiency are improved.

The invention relates to the technical field of intelligent futures decision, discloses an intelligent futures decision agent method based on NLP and multi-modal data fusion, and aims to solve the problems of representation decoupling, causal chain deficiency, decision response lag and the like caused by separation and shallow fusion of multi-modal data processing in the prior art. According to the method, a multi-modal data acquisition and preprocessing module, a dynamic heterogeneous causal atlas construction module, a causal conduction space-time diagram neural network reasoning module and a futures decision signal generation and interpretation module are adopted, so that a system for uniformly representing dynamic endogenesis of macroscopic events, industrial logic and microcosmic prices is constructed, and cross-modal is realized.

The invention belongs to the technical field of nuclear power station maintenance management, and particularly relates to a nuclear power maintenance decision-making system and method based on multi-Agent cooperation. The system comprises an input layer, a multi-Agent cooperation layer, a knowledge support layer, a decision processing layer, an output and interaction layer and a feedback learning layer. The input layer receives initial work order information including equipment basic information and fault description; the multi-Agent collaboration layer allocates sub-tasks to predefined six types of professional Agents according to work order information and completes information interaction; the knowledge support layer comprises a nuclear power professional knowledge base, a historical maintenance database and a rule and regulation library; the decision processing layer performs conflict detection, negotiation and decision fusion on Agent output; the output and interaction layer provides a decision result display and man-machine interaction interface; and the feedback learning layer realizes comprehensive improvement of nuclear power maintenance decision-making efficiency and safety. The method has the beneficial effects that a multi-professional Agent collaborative network technical means is adopted, and the technical effects of maintenance decision cross-professional information instant sharing and efficient collaboration are realized.

The invention discloses a large model auxiliary task planning decision-making method based on field fine tuning. The method comprises the following steps: establishing a theme database with a knowledge graph based on original data; performing multi-modal data fusion on metadata in the theme database with the knowledge graph to obtain graph fusion data; establishing a reinforcement learning large model, and performing field fine tuning training on the reinforcement learning large model by adopting a cue word fine tuning technology; and establishing a decision-making platform, and processing the mapping fusion data by the decision-making platform through a reinforcement learning large model to obtain a task planning decision. According to the method provided by the invention, the decision-making platform can be efficiently and accurately assisted to generate an executable high-quality decision-making scheme.

The invention provides a DIKWP semantic modeling method. Complex problems of an enterprise are converted into a five-layer linkage semantic map. The method comprises the following steps: 1, cleaning multi-source heterogeneous data to form nodes by a data layer; (2) information layer construction domain ontology clarification concepts and constraints; (3) the knowledge layer aligns with the knowledge base to infer and complement the relationship to generate a knowledge graph; (4) the wisdom layer outputs scheme elements, benefits and risks based on decision rules; and (5) modeling each intention layer refining strategic target constraint layer. Full-link semantic mapping from data to intention is achieved, consistency and accuracy of problem definition are improved, cross-department implicit causality is mined, it is ensured that a scheme is accurately aligned with a strategic target, an interpretable semantic basis is provided for automatic scheme generation, scene simulation and optimization decision, and the method has remarkable commercial application value.

The invention belongs to the field of project group decision making, and particularly relates to a dynamic strategic target-oriented project group reconstruction method and system, and the method comprises the steps: obtaining a project adjustment decision scheme according to a constraint condition; inputting the strategic targets, the project attributes and the decision-making schemes into a large language model to obtain preference scores of the decision-making schemes, and then sorting the decision-making schemes to obtain a sorting result; setting a state; the strategy network generates a corresponding weight vector according to the sorting result and selects an execution action to obtain a selection strategy; according to the initial state and the execution action, state conversion is carried out to obtain a next state, and the extraction weight of each training sample is calculated; updating the strategy network according to the extraction weight and the training sample to obtain an optimized strategy network; and obtaining a project group adjustment result according to the sorting result and the optimization strategy network. The method has the effects that the project is objectively decided, and the project combination is accurately changed according to the changing dynamic strategy target.

The invention provides a multi-agent task planning and executing method and a decision making system, and relates to the technical field of artificial intelligence and automatic control. According to the method, a complex multi-agent decision problem is decomposed into an upper-layer semantic planning subsystem and a lower-layer action control subsystem which are relatively independent and collaborative optimization; the upper-layer vision-language model planner generates sub-target sequences and track priori conforming to tactical intentions from a global perspective by utilizing the powerful vision understanding and language reasoning capabilities of the upper-layer vision-language model planner; and the reinforcement learning strategy controller at the lower layer focuses on executing specific control actions under the guidance of the sub-targets. Meanwhile, the system can optimize the final task performance and the process execution quality at the same time by introducing an evaluation mechanism and a combined reward signal based on the completion degree of the sub-targets. Environmental native rewards ensure that the system is optimized towards a final objective.

The invention discloses a decision strategy rule evaluation method, system and device and a medium, and the method comprises the steps: obtaining a to-be-verified decision strategy rule; executing a decision on the decision strategy rule through at least two different verification modes to obtain a respective decision result set in each verification mode, wherein the at least two different verification modes comprise an offline simulation verification mode, an online shadow verification mode and a real-time small-flow experiment verification mode; performing associated storage and cross comparison analysis on decision results generated by different verification modes based on the unified identifier of the same business case to obtain a cross verification conclusion; and according to the cross validation conclusion and the actual business effect index in the at least one validation mode, evaluating the capability and effect of the decision strategy rule. According to the method, the comprehensiveness, the accuracy and the credibility of decision strategy rule capability and effect evaluation are improved, and the possibility that normal users are accidentally injured or risky users are released after the rules are completely online is effectively reduced.

The invention provides a credible manuscript review decision-making method, system and equipment and a readable storage medium. The method comprises the following steps: receiving a manuscript to be reviewed, analyzing a document structure of the manuscript to be reviewed, and identifying a paper type; based on the identified paper type, calling a data processing agent, extracting a performance index corresponding to the paper type from at least one of a text, a table and a chart of the to-be-reviewed document, and converting the performance index into standardized structure data; traversing a preset database, comparing the data in the reference database with the structured performance indexes, and determining the similarity and the relative sequence of the performance indexes of the to-be-reviewed manuscript and the data in the database; and based on the similarity and the ranking percentage, outputting a quantitative score of the to-be-reviewed manuscript and the confidence of the quantitative score. Compared with the prior art, the method has the advantages that the professionality, objectivity and credibility of review are remarkably improved; and interference caused by training data prejudice and text subjective description is effectively reduced.

The embodiment of the invention provides a multi-agent decision deduction collaboration method and system based on a large language model. The method comprises the following steps: S1, receiving an input decision demand text, and determining and initializing a plurality of agents in different fields; s2, driving a plurality of agents to perform initial viewpoint expression and at least one round of interactive discussion; wherein in each round of interaction, S21, a natural language viewpoint of the round is generated based on a semantic fusion frame formed by an initial viewpoint or a historical interaction round; s22, fusing the natural language viewpoints generated by the intelligent agents at the message level, and converting the natural language viewpoints into structured message viewpoints; s23, the structured message viewpoint is transmitted among the agents, and the multiple agents are driven to interact, respond and debate to generate interaction viewpoints; and S24, carrying out secondary fusion on the interaction viewpoints formed after the current round of interaction. And S3, performing global level fusion on semantic fusion frames generated by multiple rounds of interaction, and generating a structured deduction result containing a consensus field and a divergence field.

The invention relates to the technical field of multi-modal data processing and intelligent decision making, in particular to a complex-task-oriented multi-modal data alignment and cross-modal reasoning method and system for an agent with a body, and the method comprises the following steps: multi-skill task and resource information collection, original data preprocessing, three-level multi-modal data alignment, cross-modal reasoning, multi-skill task and resource information collection, three-level multi-modal reasoning and cross-modal reasoning. Cross-modal reasoning and decision generation, reasoning result verification and iterative optimization; the method has the beneficial effects that through a three-stage framework of'space-time calibration-semantic mapping-constraint adaptation ', the problems of space-time asynchronization and semantic segmentation of multi-modal data are effectively solved, the alignment accuracy is greatly improved compared with that of a traditional method, and a high-quality data basis is provided for subsequent reasoning.

The invention provides an artificial intelligence-based collection decision method and system and a storage medium, and the method comprises the steps: obtaining project approval information, sending the project approval information to an AI model to obtain a reference file template, and determining a target file template based on the reference file template; sending the target file template to an AI model to obtain a reference qualification requirement, and determining a target qualification requirement based on the reference qualification requirement; sending the target qualification requirement to an AI model to obtain a reference bid evaluation method, and determining a target bid evaluation method based on the reference bid evaluation method; sending the target bid evaluation method to the AI model to obtain reference related elements, and determining target related elements based on the reference related elements; sending the target related elements to an AI model to obtain a reference bid invitation file, and determining a target bid invitation file based on the reference bid invitation file; and sending the target bid invitation file to the AI model to obtain an audit result of the target bid invitation file, and obtaining a bid invitation decision based on the audit result. According to the invention, the bid procurement decision can be determined efficiently and accurately.