309 results about "Decision making analysis" patented technology

The invention discloses a BIM-based building electromechanical full life cycle management platform and an optimization strategy thereof, relates to the technical field of building informatization management, and solves the defects of the existing BIM technology in the aspects of processing efficiency, model precision, data synchronization and change management. According to the invention, a data layer is used for data cleaning and standardization through a machine learning model, and distributed storage is realized through a cloud management system; the model layer constructs and updates a high-precision BIM model; the synchronization collaboration layer ensures the consistency and traceability of data by using a real-time synchronization mechanism and a block chain technology; the decision analysis layer performs data analysis and prediction through a deep learning algorithm; the full life cycle management layer integrates historical data and current data and automatically triggers a management process; according to the method, the data processing efficiency and the model precision are greatly improved, and the seamless sharing capability of information and the transparency of data synchronization and change management are remarkably improved, so that the overall collaborative efficiency and the management level of a project are enhanced.

The invention discloses a marketing decision analysis system and method based on artificial intelligence, and aims to improve the intelligent level of marketing decision, optimize resource allocation and improve the rate of return on investment. The system comprises a data analysis module, a prediction evaluation module, an AI decision engine and a knowledge management module. The data analysis module obtains marketing-related data from a plurality of data sources and generates a market insight result. And the prediction evaluation module predicts the potential effect of the marketing scheme by adopting a statistical analysis method based on the market insight result and the historical marketing data, and obtains a marketing prediction result. And the AI decision engine receives the market insight result and the marketing prediction result, and determines an optimal marketing decision scheme based on multi-round dialogue management, knowledge graph construction and decision reasoning technologies. According to the marketing decision analysis system and method, accurate, efficient and reusable marketing decisions are realized in a data driving mode, and the scientificity and the performability of enterprise marketing strategies are improved.

The invention belongs to the field of network security, and particularly discloses a network defense agent system based on a large language model. Through the design of the sensing layer, the decision analysis layer and the action execution layer, comprehensive protection of network threats is realized. The sensing layer is responsible for collecting original information from multiple channels and converting the original information into standardized data; the decision analysis layer performs modeling and threat reasoning on attack behaviors, evaluates a risk level and predicts subsequent actions; and the action execution layer specifically executes defense operation according to the defense strategy scheme output by the decision analysis layer. In addition, the application also constructs a data set oriented to attack and defense confrontation, records a complete attack sequence, defense response and effect evaluation thereof, and provides a reliable basis for continuous learning of defense agents. Experimental results show that the framework provided by the invention is superior to the traditional method in the aspects of attack detection accuracy, attack chain identification and defense strategy generation, and has stronger adaptability and real-time response capability.

The invention provides an implementation method, system and equipment of a commercial intelligent system based on a large model ReAct, and belongs to the technical field of artificial intelligence. The method is applied to a ChatBI platform. Comprising the steps that a main agent receives query request information from a user terminal and guides an LLM to conduct cyclic reasoning on the query request information according to a preset ReAct normal form through a preset Prompt project, and the cyclic reasoning process comprises the steps that 1, input information from an upstream task is analyzed through the LLM, and a to-be-executed action corresponding to a downstream task is determined; step 2, according to each action task corresponding to the action to be executed and the action task type thereof, executing a reasoning action; 3, receiving an inference action feedback result, updating a loop inference context, and determining whether to trigger a next round of loop inference or not until a preset loop termination condition is met; and based on each decision analysis result obtained by loop reasoning, generating a query result and sending the query result to the user terminal.

The invention provides a big data intelligent decision analysis method and system. The method comprises the following steps: acquiring vibration data and mechanical node stress distribution data in power equipment transportation, and extracting high-frequency harmonic distortion characteristics aligned with a vibration data timestamp; performing cross-domain correlation analysis on stress data fluctuation and harmonic characteristic dynamic change, and identifying a symbiotic mode of mechanical stress abnormality and electrical harmonic distortion by using a machine learning model; based on logistics path planning and equipment constraints, a cooperative control instruction for adjusting the transportation path and the power supply loop parameters is generated through a multi-target cooperative optimization algorithm; and constructing a joint risk assessment matrix, and dynamically adjusting the cooperative relationship between the transportation path and the harmonic suppression parameter according to the quantitative analysis result and the cooperative control instruction to realize the closed-loop decision of the power equipment. According to the technical scheme provided by the invention, the power equipment transportation data processing efficiency is remarkably improved.

The invention relates to the technical field of intelligent decision making of production equipment, and discloses a decision making method and system based on a knowledge graph, and the decision making method based on the knowledge graph comprises the following steps: processing input data through a multi-granularity knowledge graph construction system, and obtaining a multi-scale knowledge graph; processing the real-time sensor data and the multi-scale knowledge graph through a time sequence knowledge dual representation learning framework to obtain a dynamic representation model; processing the dynamic representation model and the multi-scale knowledge graph through a time-varying causal propagation network decision system to obtain a decision analysis result; processing the decision analysis result and the multi-scale knowledge graph through a multi-hypothesis reasoning algorithm to obtain a root cause analysis report; a multi-scale knowledge graph is constructed by fusing multi-source heterogeneous data, and intelligent decision with high accuracy and strong interpretation is realized by combining time sequence knowledge dual representation learning and time-varying causal propagation network analysis.

The invention belongs to the technical field of electric energy dispatching, and particularly relates to a virtual power plant electric energy dispatching scheme optimization method based on intelligent decision analysis, which comprises the following steps: acquiring multi-source related parameter data; constructing a cascade prediction model based on the equipment basic data and the external environment data to obtain prediction results of all levels; constructing a risk assessment model based on prediction results of all levels in combination with equipment basic data and external environment data, generating a risk map in combination with a network vulnerability analysis method, and forming a schedulable resource pool; market risk data are acquired, a market risk comprehensive score is obtained, and a corresponding safety alternative scheme is triggered; and constructing a decision tree, determining a decision path in combination with the risk map, and generating a final optimization strategy. According to the method, through multi-dimensional means such as cascade prediction, risk quantitative evaluation, resource optimization screening and market risk response, high precision, high adaptability and intelligentization of virtual power plant electric energy scheduling are realized.

The invention relates to the field of digital twinning, in particular to an intelligent decision analysis method and system for a digital twinning-based manufacturing system, and aims to improve the generalization ability and adaptability of a digital twinning model in an emerging market small sample scene through deep fusion of meta-learning and adversarial training. According to the method, the problem of prediction distortion caused by data scarcity is relieved, high-quality training samples are expanded in combination with a generative enhancement technology, a multi-target elastic scheduling mechanism is synchronously constructed, dynamic optimization and feasibility verification of a production strategy are realized in a digital twin environment, and finally a closed-loop optimized intelligent decision system is formed. According to the method, through deep fusion of meta-learning and adversarial training, by combining a generative enhancement technology to expand high-quality training samples and a synchronously constructed multi-target elastic scheduling mechanism, dynamic optimization and feasibility verification of a production strategy are realized in a digital twin environment, and finally a closed-loop optimized intelligent decision system is formed.

The invention relates to the technical field of intelligent manufacturing decisions, in particular to a digital twinning-based manufacturing system decision analysis method and system, and the method comprises the steps: deploying a plurality of Internet of Things sensors on a physical manufacturing system, and collecting a physical real-time data stream of equipment in real time; the method comprises the following steps: establishing a virtual data acquisition channel aligned with a physical manufacturing system clock, injecting a physical real-time data stream into a digital twinning creation model, and performing data preprocessing based on distributed edge calculation to delay and compress original data acquisition of the physical real-time data stream to 10ms level, the time sequence database and the NTP / GPS clock are synchronized to ensure the state alignment error lt of the physical-virtual system; compared with the prior art, the deep space-time prediction network is fused with a CNN-LSTM-attention mechanism, the accuracy of multivariable coupled KPI prediction is improved, in addition, model failure is recognized in real time through Page-Hinkley inspection, and a prediction error reaches a relatively stable state through an adaptive retraining mechanism.

The invention discloses a cold region tunnel freeze injury diagnosis, risk grading and control system and method, and relates to the technical field of tunnel engineering. The system comprises a basic data layer for storing disease, index and measure databases; the core analysis layer is used for diagnosing a frost heaving mechanism, identifying single-factor, double-factor and three-factor frost heaving types, calculating freezing depth and frost heaving force and analyzing sensitivity; the risk level evaluation layer is used for dividing a disease zone, a frost heaving level, a freezing injury risk and a freezing injury risk response level; and the intelligent decision-making layer constructs an intelligent comprehensive decision-making analysis platform to realize risk-measure accurate matching and closed-loop management and control. The method comprises the four steps of data acquisition and storage, frost heaving mechanism diagnosis and quantification, risk grade evaluation and intelligent decision measure matching, through multi-source data fusion, multi-factor coupling analysis and zoning and grading prevention and control, the whole-process accurate management and control of the cold region tunnel frost damage is realized, and the frost damage treatment efficiency and the tunnel operation safety are improved.

The invention is suitable for the field of atomic layer deposition, and discloses an atomic layer deposition process management method and system based on digital twinning, and the method comprises the steps: building a digital twinning model of an atomic layer deposition production system based on a multi-scale physical information neural network model; acquiring real-time reaction chamber process parameters and real-time state variables, and inputting the real-time reaction chamber process parameters and the real-time state variables into the digital twin model to obtain physical field distribution in the reaction chamber and a predicted growth state of a film on the surface of the substrate; based on the physical field distribution in the reaction cavity and the substrate surface film prediction growth state, decision analysis is carried out through a pre-trained reinforcement learning agent, and correction parameters used for optimizing the film growth quality are obtained; and generating a correction instruction based on the correction parameter, and updating the process parameter of the reaction cavity based on the correction instruction, so that the film on the surface of the substrate in the reaction cavity reaches the target film thickness and uniformity, and thus real-time intelligent closed-loop control of perception-prediction-decision-execution can be formed.

The invention relates to the technical field of data processing, and discloses a digital intelligent water affair global data operation decision analysis comprehensive service system. The system comprises an acquisition module for acquiring standardized processing data; the construction module generates a water plant three-dimensional model and virtual representation; the storage module executes hierarchical storage to obtain quality enhancement data; the identification module constructs an index system to diagnose the operation state; the evaluation module uses a Bayesian network to check asset cost; and the generation module provides decision support and an early warning scheme. According to the invention, by constructing the global data operation decision analysis comprehensive service method, the data barrier is broken, the whole-process data integration, analysis and intelligent decision support of the water affair system from the source to the terminal are realized, and the operation efficiency and management level of the water affair system are improved.

The invention discloses a land comprehensive renovation planning method based on three-dimensional modeling and auxiliary decision analysis, and relates to the technical field of land comprehensive renovation planning, and the method specifically comprises the following steps: based on a generated three-dimensional terrain model, recognizing a region with a continuous slope surface and a slope toe having a height difference fracture, and calibrating the region as a sliding structure interference sensitive region; the sliding structure is uniformly divided into a plurality of sliding structure sub-regions; obtaining slope sliding interference configuration information of each sliding configuration sub-region, analyzing the slope sliding interference configuration information, evaluating the sliding risk potential degree of each sliding configuration sub-region, and dividing each sliding configuration sub-region according to an evaluation result; and on the basis of the division result of each sliding configuration sub-region, corresponding slope reinforcement measures are executed, and dynamic regulation and control are carried out on the sliding configuration sub-regions. The problem of risk misjudgment caused by unclear recognition of the slope toe fracture is solved, and accurate recognition and dynamic reinforcement response of the sliding slope are achieved.

The invention relates to the technical field of damage management, in particular to a combined combat large-scale damage effect evaluation and treatment system based on a large model. The system comprises a combat target data processing module, a target damage feature merging module, a target damage effect evaluation module and a multi-dimensional damage treatment and analysis module. The method comprises the following steps: acquiring a combat target battlefield situation image sequence and combat target unit text data; performing data cleaning and standardization on the battlefield situation image sequence of the combat target and the text data of the combat target unit, and performing feature analysis and damage feature vector merging to generate a combat target damage feature vector; and acquiring combat geographic coordinate data, performing coupling construction of a damage large model, performing damage effect evaluation analysis and multi-dimensional damage treatment decision analysis, and generating a combat target multi-dimensional damage effect treatment decision so as to execute combat target damage effect treatment strategy work. According to the invention, accurate damage assessment can be provided for combat command.

The invention discloses an electric power AI security detection model optimization method and system fusing attribution quantification and adversarial correction. The optimization method comprises the following steps: step 1, carrying out structured semantic representation on heterogeneous security alarms of an electric power network; 2, performing model decision logic analysis based on hybrid attribution quantization; step 3, automatically diagnosing decision prejudice based on domain knowledge masks; step 4, constructing an adversarial sample generated based on an anti-fact text; and 5, performing closed-loop fine adjustment and optimization on the attribution regularization model. According to the method, the interpretable ability of large model decision analysis, the root cause positioning ability of misinformation and the autonomous repair optimization ability are improved, the transparency and credibility of model decision are improved, the model misinformation caused by environmental influence is reduced, and the efficiency of model autonomous correction is improved.

The invention discloses an artificial intelligence interaction and decision analysis system based on a multi-modal technology. The invention discloses the artificial intelligence interaction and decision analysis system based on the multi-modal technology. The data acquisition module is used for acquiring a plurality of multi-modal feedback suggestions and suggestion parameters published by a plurality of participants for different subject discussions; the analysis module traverses each subject discussion, determines the emotional polarity and emotional intensity of the multi-modal feedback opinions by using an emotional analysis model, determines the evaluation quality scores of the multi-modal feedback opinions according to opinion parameters, and determines the popularity of the subject discussion in combination with the analysis result; the decision-making module clusters a plurality of multi-modal feedback suggestions under a plurality of topic discussions, and determines the processing emergency degree of each cluster according to the popularity of the topic discussions to which each multi-modal feedback suggestion in each cluster belongs; and supervising the multi-modal feedback suggestions in the plurality of clusters according to the processing emergency degree. The technical problems that the processing efficiency is low and the user experience feeling is poor when supervision is carried out in sequence according to the publishing sequence of the user feedback opinions under the subject discussion in the related technology are solved.

The invention discloses a distributed collaborative decision-making system based on multi-modal data driving and an implementation method thereof, and relates to the technical field of group intelligence and distributed decision-making, and the system comprises a user end interaction module which provides a multi-modal interaction and decision-making scheme visual interface; the distributed node management module comprises a main node and an edge node, and the main node manages node registration, state monitoring and task distribution; the information fusion and preprocessing module is used for processing multi-source heterogeneous data; the decision analysis module is used for carrying out clustering analysis on the opinions and generating candidate schemes in combination with domain knowledge; the domain knowledge graph module is used for constructing a domain entity relationship network; the consensus mechanism and credit evaluation module determines multiple rounds of interaction rules, calculates a user credit value and influences an opinion weight; and the decision result output and feedback module is used for collecting user feedback for system optimization. According to the method, the stability, the response speed and the load balancing capacity are improved, the multi-source information processing and opinion aggregation quality is optimized, and efficient and reliable support is provided for distributed collaborative decision making.

The invention discloses an enterprise operation data analysis method and system based on a knowledge graph, and relates to the technical field of data processing, and the method comprises the steps: collecting an enterprise multi-source heterogeneous operation data set, and carrying out the standardization processing and multi-dimensional entity extraction; performing knowledge graph modeling and dynamic updating optimization based on the enterprise operation multi-dimensional entity set, and constructing an enterprise operation knowledge graph; constructing an operation data analysis task list and performing prediction driving on the enterprise operation knowledge graph; and obtaining enterprise operation period data, performing risk prediction reasoning and operation decision analysis based on the operation data analysis task engine, determining a target enterprise operation decision scheme, and performing operation decision control through the target enterprise operation decision scheme. The technical problems of enterprise operation data islanding, weak knowledge analysis and reasoning ability and decision lag in the prior art are solved, and the technical effects of improving operation data analysis and reasoning intelligence and providing prospective risk prediction and accurate operation decision support are achieved.

A safe and efficient improved PBFT Internet of Vehicles block chain consensus method, system, device and medium, and the method comprises the steps: carrying out the pre-screening according to a vehicle comprehensive trust value, and obtaining an initial set of entrusting consensus nodes; performing multi-criterion decision analysis according to the trust value of the vehicle, the communication channel capacity, the data collection time and the difference value of the existing timestamp, obtaining a score ranking, and selecting the first K nodes as entrusting consensus nodes; selecting a leader node by adopting a VRF verifiable random function; entrusting the consensus node to carry out consensus, and carrying out aggregation signature in a consensus preparation stage and a consensus submission stage by adopting a BLS-based signature technology; based on a D3QN deep reinforcement learning algorithm, optimizing the throughput of the block chain consensus; consensus is completed, trust value updating is carried out based on consensus performance of the nodes, and data is stored in a block chain account book; the system, the equipment and the medium are used for implementing the method. The method can reduce the consensus delay and increase the consensus throughput, and has the advantages of safety, high efficiency and the like.

The invention provides a cooperative control method and system based on a port automatic driving mixed driving scene, and relates to the technical field of port traffic management, and the method comprises the steps: firstly obtaining a dynamic interaction data set containing information of multiple aspects such as an automatic driving vehicle and manual driving equipment in a port operation region; and then carrying out association feature extraction on the dynamic interaction data set, generating dynamic association representation data containing traffic participant association relationships and other features, executing collaborative decision analysis based on the dynamic association representation data, and determining a right-of-way priority sequence and a collaborative path planning scheme. And a cooperative control instruction set including speed cooperative parameters and the like is generated according to a cooperative decision analysis result, and finally the cooperative control instruction set is distributed to a corresponding control system, so that dynamic cooperative driving control in a port mixed driving scene is realized, and the port operation efficiency and safety are improved.

Disclosed is a procurement optimization approach that integrates real-time reinforcement learning and blockchain-based compliance checks for dynamic, transparent decision-making. Initial procurement specifications and weightings are automatically generated using AI analysis of historical procurement data, followed by supplier evaluations using multiple criteria decision analysis with buyer-defined weightings, while a reinforcement learning module continuously refines these weightings based on observed procurement outcomes or feedback. Each updated weighting is automatically verified against predefined procurement rules encoded on a distributed ledger, ensuring that no unauthorized criteria change bypass regulatory thresholds. If found compliant, the system immutably stores the refined weighting and resulting supplier evaluation as a ledger transaction, creating an unalterable audit trail. Optional modules handle anomaly detection, predictive risk analytics, duplication detection across departments, automated tender generation, quantum-resistant cryptography, and ERP integration. This modular design addresses persistent procurement challenges: static weighting, delayed or incomplete compliance checks, and limited traceability. By unifying adaptive AI logic with an immutable ledger, the disclosed system offers robust, real-time improvements in cost efficiency and governance.

An urban drainage, regulation and storage facility operation step-by-step decision analysis method based on risk assessment. When information of each decision standard preference for urban drainage, regulation and storage facility operation is completely missing or is unclear, a method combining game theory with feasible weight space is used to aggregate urban drainage, regulation and storage facility operation weights; stochastic multi-criteria decision analysis algorithm and fuzzy optimization models are used to construct a stochastic multi-criteria decision analysis-fuzzy optimization model for the urban drainage, regulation and storage facility operation; an urban drainage, regulation and storage facility operation decision risk assessment model is constructed to quantitatively assess the risk of urban drainage, regulation and storage facility operation decision errors; and multi-stage multi-criteria decision analysis is used to determine step by step information of the weight of each standard of the urban drainage, regulation and storage facility operation, thereby achieving an optimal decision for the urban drainage, regulation and storage facility operation.

The invention discloses a multi-agent-based industrial equipment abnormal event management method and device and a medium, and relates to the technical field of multi-agents, and the method comprises the steps: carrying out the equipment abnormal event recognition of industrial Internet of Things platform data, so as to determine an equipment abnormal event; based on the original knowledge base, performing multi-dimensional exception decision analysis on the equipment exception event to obtain a task execution instruction; based on the equipment automatic execution task instruction, first work order data is obtained through equipment work order completion processing; according to an operation and maintenance personnel execution task instruction, performing operation and maintenance dialogue completion processing to obtain second work order data; and performing knowledge classification distinguishing knowledge precipitation on the first work order data and the second work order data to determine updated knowledge, and uploading the updated knowledge to the original knowledge base to obtain an updated knowledge base. According to the method, the technical problem that the relevance of the abnormal event management process of the industrial equipment is poor is solved.

The invention provides an enterprise informatization management integration platform based on big data, which is based on Hadoop distributed computing framework construction, integrates technologies of MapReduce parallel processing, Spark real-time computing, HDFS distributed storage and the like, realizes data parallel processing through a swarm intelligence algorithm and a pheromone transmission mechanism, performs heterogeneous data mapping by utilizing a hierarchical self-encoding network, and realizes enterprise informatization management. Dynamic resource scheduling is realized in combination with a biological cell division mechanism and an entropy balance mechanism, storage management is optimized by adopting an adaptive grid partition and data temperature layering technology, and multi-scale feature fusion and knowledge reasoning support decision analysis are constructed based on Spark MLlib. According to the platform, the problems of low data processing efficiency, limited system expansion, difficulty in data sharing and the like in traditional enterprise informatization management are solved, and reliable technical support is provided for enterprise digital transformation.

The invention relates to the technical field of power system operation and control, in particular to a risk quantification and optimization decision method and system for power grid operation management. Comprising the following steps: S1, collecting historical operation data, real-time sensing data and power grid static topology data of a power distribution network to construct a basic data set for management decision analysis; s2, calculating a conventional risk probability, and calculating a comprehensive physical vulnerability index; s3, determining a coupling risk index of each key element in combination with the conventional risk probability and the comprehensive physical vulnerability index; s4, in response to the condition that the coupling risk index of any element exceeds an early warning threshold value, identifying the element as a potential initial fault point; s5, deducing a chain failure path based on the potential initial fault point; and S6, aiming at the chain failure path, generating an optimal strategy recommendation for supporting an operator to carry out active defense decision making. According to the method, the one-sidedness of a single-dimension evaluation method is overcome, and the comprehensiveness and accuracy of risk quantitative evaluation are remarkably improved.

The invention discloses a marketing decision analysis method based on artificial intelligence, and the method comprises the steps: S1, obtaining the marketing data of a plurality of channels, carrying out the preprocessing of the marketing data, and carrying out the classification of the preprocessed marketing data according to the data type; s2, obtaining a marketing scene demand, obtaining a corresponding analysis model according to the marketing scene demand, and generating a marketing decision-making scene through the intelligent decision-making model based on the analysis model; s3, acquiring user behavior data, then collecting behavior feedback of the user on the marketing decision scene, merging user behavior characteristics into user portrait data, and analyzing the user portrait data; and S4, extracting feature information in the marketing decision scene, constructing a marketing effect evaluation index according to the extracted feature information, and predicting a marketing effect suitable for the marketing decision scene from the historical marketing data based on the marketing effect evaluation index. According to the method, the problem of data islands when the multi-source heterogeneous marketing data is processed by a traditional method is solved.

The invention provides a mining area ecological restoration intelligent decision-making system based on multivariate data integration, which comprises a mining area data acquisition module, a data fusion modeling module, an intelligent decision-making analysis module and a scheme simulation evaluation module, the data fusion modeling module is used for fusing collected multi-source data and establishing an ecological model, the intelligent decision analysis module is used for analyzing the ecological model and generating a restoration scheme, and the scheme simulation evaluation module is used for simulating and operating the restoration scheme and performing evaluation based on an operation result; according to the system, a new-generation digital solution is provided for mine ecological restoration through a technical closed loop of data intelligent fusion, scheme dynamic optimization and effect accurate pre-judgment.

The invention relates to the technical field of energy management, in particular to a fuzzy logic-based multi-objective decision analysis method, which comprises the following steps of: firstly, dividing an evaluation criterion into quantitative and qualitative index sets; constructing an interval type-2 fuzzy set capable of describing data uncertainty according to quantitative indexes such as a full-life investment payback period; for qualitative indexes such as technology maturity, a spherical fuzzy set is introduced, and an intelligent operator for perceiving hesitance is used for aggregation; calculating a group utility value, an individual regret value and a comprehensive sorting index of each energy configuration scheme by applying a hybrid fuzzy VIKOR algorithm, outputting an optimal compromise sorting result, and identifying a reference optimal strategy and a risk avoidance strategy; according to the method, the explainable enhanced fuzzy rule is extracted, the robustness of the decision method is improved by analyzing different types of fuzzy logic, and the transparency, the credibility and the compliance of the energy configuration decision process are remarkably enhanced by the finally generated explainable decision basis, so that a feasible configuration decision reasoning model is provided for energy planning.

The invention discloses a large-scale equipment shock resistance optimization design method and device based on multi-objective integration, belongs to the technical field of large-scale equipment shock resistance design and optimization, and particularly relates to a method which comprehensively considers multiple design objectives and carries out optimization design on response of large-scale equipment in a shock environment based on a multi-objective integration method. The problem that an existing impact resistance design method only optimizes a single target and cannot deal with a complex multi-target optimization problem is solved. The method comprises the following steps: scoring the importance of each objective function to form a judgment matrix; calculating the weight of each objective function by using an analytic hierarchy process based on the judgment matrix; and S5, carrying out multi-criterion decision analysis on the optimal solution set by utilizing the weight of the objective function, and selecting an optimal design scheme. The large-scale equipment impact resistance optimization design method and device based on multi-objective integration are suitable for large-scale equipment impact resistance optimization design.