430 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 relates to the technical field of data processing, and discloses an intelligent bridge reinforcement decision generation method, device and equipment based on damage identification. The method comprises the following steps: carrying out bridge damage identification and evaluation on a bridge state data set to obtain a bridge damage characteristic spectrum; performing intelligent decision analysis on the bridge damage characteristic spectrum, generating a preliminary reinforcement scheme, and obtaining a candidate reinforcement strategy set; performing parametric modeling and multi-scale simulation on the candidate reinforcement strategy set, and performing coupling calculation evaluation on a reinforcement effect to obtain an optimized reinforcement scheme; virtual construction planning is conducted on the optimized reinforcement scheme, a construction process model is generated through an intelligent scheduling algorithm, and a target construction scheme is obtained; and monitoring and feeding back the target construction scheme in real time, and dynamically optimizing the construction process through an adaptive control algorithm to obtain a target intelligent reinforcement implementation scheme. The intelligent bridge reinforcement decision generation efficiency and accuracy based on damage identification are improved.

Described is a system for performing a simulated vehicle control task based on adversarial decision analysis. Empirical game theoretic analyses are performed between an evolving and an adversary population of neural network strategies. Each empirical game theoretic analysis includes using a neuroevolution procedure to perform a fitness-based selection of a strategy in the evolving population that out-performs the adversary population. Using an empirical game theory procedure, the neuroevolution procedure is iteratively run and the selected strategy is added to the adversary population with each iteration, resulting in monotonic strategy improvement with each iteration. Following the empirical game theoretic analyses, a final strategy is selected for the evolving population and the adversary population using a tournament selection procedure. The final strategy is used to train a neural network which is used to perform a simulated vehicle control task.

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 discloses an urban blue-green space ecological benefit key influence factor identification method. Firstly, an urban blue-green space ecological benefit evaluation index set is constructed; secondly, various index data are calculated and standardized, and an urban blue-green space ecological benefit standardized data matrix is established; thirdly, determining subjective weights of the indexes by adopting an analytic hierarchy process, determining objective weights by adopting an entropy weight method, and obtaining weights of the indexes through combined weighting; and then, constructing an entropy weight-TOPSIS model to calculate a weighted urban blue-green space ecological benefit standardized data matrix, and completing urban blue-green space ecological benefit evaluation. Then, establishing an urban blue-green space ecological benefit influence factor set; and finally, based on an urban blue-green space ecological benefit evaluation result, identifying key influence factors by adopting a GWR-XGBoost model. According to the method, a multi-criterion decision analysis method, a geographical weighted regression model and a machine learning technology are combined, so that the key influence factors of the urban blue-green space ecological benefits can be quickly and accurately obtained.

The invention discloses an operation and maintenance decision driving method and system based on a multi-modal data knowledge graph, and relates to the field of intelligent decision support, and the method comprises the steps: obtaining the multi-source and multi-modal data of a power distribution network, and carrying out the preprocessing, and obtaining a knowledge graph database; constructing a knowledge graph based on the knowledge graph database; constructing a fault decision mode based on the knowledge graph; performing decision analysis and output based on the fault decision mode; according to the method, the utilization efficiency of data is improved, the data quality and relevance are improved, the fault type and the influence range thereof can be quickly identified, fault early warning is realized, personalized decision suggestions are provided, operation and maintenance personnel are helped to deal with power grid faults in time, and the fault processing efficiency and accuracy are improved; risks can be prevented in advance, resource allocation is optimized, and stable operation of a power grid is guaranteed; the automatic execution of the decision is realized, and the overall operation and maintenance efficiency is improved.

The invention belongs to the technical field of data management, and discloses a decision management method and system based on machine learning. The method comprises the following steps: constructing an engineering construction decision analysis model and an engineering construction decision optimization model based on a cloud data center; acquiring real-time engineering construction decision data, and performing engineering construction decision analysis by using the engineering construction decision analysis model; performing engineering construction decision optimization on the real-time engineering construction decision data by using an engineering construction decision optimization model according to a real-time engineering construction decision analysis result; according to the optimized real-time engineering construction decision data, an engineering construction decision analysis model is used to carry out analysis after an engineering construction decision; and performing uplink storage on the optimized real-time engineering construction decision data and the optimized real-time engineering construction decision analysis result by using a preset decision storage block chain system. According to the method, the problems of low data security, low analysis accuracy and low practicability in the prior art are solved.

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 provides a machine learning-based multi-dimensional auxiliary decision analysis method and system for power supply of a distribution network. The method comprises the following steps of: S1, acquiring historical data and historical electrical load data of the distribution network; s2, obtaining a power supply demand prediction model through the long and short term memory network and the historical data of the distribution network; s3, obtaining a load change model through a time sequence prediction algorithm and historical electrical load data; s4, identifying the real-time operation data of the distribution network based on the output of the steps S2 and S3 to obtain a power supply capacity change trend and a load margin; s5, establishing a comprehensive objective function based on the output in the step S4, performing optimization solution on the objective function through a genetic algorithm, and outputting an optimal power supply decision scheme; and S6, executing the output of the step S5, monitoring the power supply operation parameters of the distribution network in real time, and dynamically adjusting the optimal power supply decision scheme based on the power supply operation parameters of the distribution network. According to the invention, the reliability of distribution network power supply is improved, and the dynamic adjustment and optimization of the distribution network power supply decision scheme are realized.

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 airport digital twinning, in particular to an airport digital twinning application method based on a multi-agent model, and the method comprises the steps: defining a real agent set and a virtual agent set based on a digital twinning platform; performing action definition on the real agent set and the virtual agent set, and connecting the actions defined by the real agent set and the actions defined by the virtual agent set in series based on a large language model to form a collaborative autonomous agent set; the method comprises the following steps: acquiring airport operation data, performing forward evaluation on the airport operation data based on a collaborative autonomous agent set, constructing an optimization strategy through a large language model in combination with a forward evaluation result, reversely inputting the optimization strategy into a digital twin platform for verification, and performing real-time interaction with the airport operation data. And completing the construction of the multi-agent data twin closed-loop framework of the airport. According to the invention, the operation efficiency and decision analysis accuracy of the airport are effectively improved.

The invention discloses a coal power multi-path transformation and new energy combination decision-making method under low-carbon transformation. The method comprises the following steps: firstly, constructing a system comprising first-level effect indexes such as environmental influence, economic benefit, energy safety and technical innovation and corresponding second-level indexes; then, alternative schemes of various coal power transformation technology and new energy project type combinations are generated; then quantitatively evaluating the effect index of each scheme, and calculating a comprehensive utility value by using multi-criterion decision analysis; identifying potential risk factors, and performing uncertainty analysis through Monte Carlo simulation; and finally, according to the low-carbon transformation target and policy evaluation scheme conformity, selecting a scheme which most conforms to the policy target, and continuously monitoring and adjusting. The coal-electricity multi-path transformation and new energy combination decision-making method under low-carbon transformation is beneficial to promoting energy low-carbon transformation, optimizing resource allocation, improving economic benefits, promoting technical innovation, enhancing risk management capability and ensuring that a project is consistent with policy orientation.

The invention belongs to the field of chip power consumption detection, relates to a data analysis technology, is used for solving the problem that optimization decision analysis cannot be carried out according to test data when the power consumption of a chip is abnormal in the prior art, and particularly relates to a chip power consumption evaluation method and system based on artificial intelligence and a storage medium. Comprising a static test module, a static analysis module, a dynamic test module, a dynamic analysis module and a database, the static test module is used for performing static test on the chip power consumption; the static analysis module is used for analyzing a static test result of the chip and marking a static test object i as a static normal object or a static abnormal object; according to the method, the basic power consumption performance of the chip in the static operation environment can be tested, then the static test result is subjected to data processing to obtain the static coefficient, then the static test object is subjected to differential marking according to the static coefficient, and data support is provided for the performance evaluation and analysis process.

The invention discloses an intelligent auxiliary decision-making method and system for a bridge construction progress based on a multi-modal visual language model, belongs to the technical field of bridge engineering management, and solves the problems that when an existing multi-modal model is used for integrating static scene data and dynamic real-time data of a bridge construction site, the fusion degree is low, and the integration efficiency is high. And therefore, the problems of information incompleteness and cognition deficiency can be solved. The method comprises the following steps: acquiring bridge construction progress data, preprocessing the data, and training a constructed multi-modal bridge construction decision analysis model based on image-text information fusion; preprocessing bridge construction progress data acquired in real time, inputting the preprocessed data into the trained image-text information fused multi-modal bridge construction decision analysis model for analysis, and constructing a unified fusion expression model for feeding back the construction progress and quality in real time according to an instruction normalized visual model required by multi-level user tasks; and obtaining a bridge construction progress scene three-dimensional model. The method is used for intelligent auxiliary decision making of the bridge construction progress.

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 discloses an intelligent judgment method for centralized purchase suppliers, and the method comprises the following steps: S1, obtaining historical cooperative supplier data, and constructing a supplier library; s2, constructing a comprehensive evaluation index standard, and scoring suppliers in the supplier library obtained in the step S1 to obtain a supplier score database; s3, constructing a decision analysis model; s4, according to the supplier score database obtained in the step S2, using the decision analysis model obtained in the step S3 to obtain a supplier comprehensive score; and S5, according to the comprehensive scores of the suppliers obtained in the step S4, completing judgment of the suppliers. The invention also discloses a system for realizing the intelligent judgment method for the centralized purchase suppliers. According to the method, by means of a systematized evaluation strategy and an intelligent analysis process, supplier performance is perspective in all directions, the comprehensive capacity of the supplier is accurately evaluated, the manual auditing burden is remarkably relieved, and the cost benefit is improved.

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 provides a big data intelligent decision analysis method and system based on machine learning. The method comprises the following steps: receiving real-time data streams of a plurality of sources, the real-time data streams comprising structured data and unstructured data; analyzing the unstructured data by applying semantic understanding and context sensing technologies so as to extract themes and emotional tendencies in the unstructured data; the emphasis of data analysis is dynamically adjusted, and an association network between data is constructed by using a graph technology; evaluating mutual influence among different data entities, and predicting future state change to obtain a prediction result; based on the mode, the trend and the prediction result, generating a strategy suggestion oriented to a specific application scene; and the strategy suggestions are converted into executable service rules to support intelligent decision making. According to the technical scheme provided by the invention, different types of data can be comprehensively analyzed to identify key trends, understand emotions of consumers and predict market trends, so that the data analysis efficiency is effectively improved.

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.