81 results about "Forward looking" patented technology

The invention discloses a public opinion risk dynamic early-warning method and system based on a large language model, and the method and system carry out the semantic recognition, event extraction and situational reasoning of unstructured public opinion data through introducing an advanced large language model, and carry out the dynamic early-warning of the public opinion risk in combination with structured emotion indexes and market dynamics. Potential risk factors in social public opinions can be automatically identified, and risk quantitative evaluation is realized in combination with semantic tags, market behavior data and emotion intensity. The system has strong prospective perception ability and event evolution reasoning ability, can provide rapid and accurate early warning information, and further can generate personalized suggestions and decisions in combination with user risk preferences. The method can be widely applied to a plurality of public opinion sensitive fields such as enterprise brand management, government supervision, financial institutions and media transmission control, and the automation and precision capability of dealing with sudden public opinion risks is effectively improved.

The invention relates to a resource recommendation method and device based on a small enterprise dynamic portrait, equipment and a medium. According to the method, key events are extracted from enterprise multi-dimensional data and standardized to form an initial event sequence, importance scores are calculated based on frequency and weight, and a core event set is generated through self-adaptive filtering; according to the timestamps and the semantic features, calculating dependency strength among events, and constructing a causal event relation graph; learning event embedding and time sequence characteristics by using a graph neural network and a gating loop unit, and generating a dynamic portrait vector; analyzing a historical event influence weight through an attention mechanism to predict future demand distribution; the resource urgency degree and the matching degree are optimized in combination with enterprise capability constraints, and a prospective recommendation list is generated; and a closed loop is formed by predicting deviation to trigger incremental updating of the atlas and retraining the model. Finally, the dynamic capture of the development trajectory and the accurate pre-judgment of the resource demand are realized, and the recommendation timeliness and the service suitability are remarkably improved.

The invention provides an inference service instance expansion and contraction method, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence, and the method comprises the steps: predicting a future business load based on historical operation data of a target inference service to generate an active expansion and contraction instance decision; evaluating the current operation state based on the real-time operation data of the target inference service to generate a passive scaling instance decision; and performing collaborative decision-making on the active expansion and contraction instance decision and the passive expansion and contraction instance decision to determine a final expansion and contraction instance instruction, and adjusting the instance number of the target inference service according to the final expansion and contraction instance instruction. According to the method, a double-engine cooperation mechanism combining active prediction and passive response is established, while prospective capacity expansion and contraction are realized by using historical data to reduce time delay, bottom correction is carried out by using real-time data to cope with burst load, the problem of response lag or resource waste of a single capacity expansion and contraction mode is effectively solved, and the method is suitable for large-scale popularization and application. And the resource utilization rate and the service quality stability of the inference service are obviously improved.

The invention discloses a financial digital intelligent management system, and relates to the technical field of computers, and the system comprises an atomic power unit ACU, a side effect metadata management module which is used for decomposing a financial process into ACU and defining the structured side effect metadata of the ACU, and the structured side effect metadata comprises an influence object, a type, a condition, a range, a preset risk level and the importance of an associated data object; the dynamic process arrangement engine module is used for generating candidate execution paths according to the process definition and the ACU side effect metadata, and selecting a final execution plan by taking the score minimization as an optimization target; and the execution control and risk relief module selectively performs influence domain sandbox rehearsal before actual execution of the ACU according to a risk assessment result, and executes conditional rollback or compensation when execution fails or unacceptable side effects are generated. According to the invention, through prospective side effect management and intelligent risk avoidance and control, the stability, reliability, auditing performance and combinable innovation capability of a financial automation process are significantly improved.

The invention relates to the field of building energy management, in particular to a multi-source data fusion air conditioning system low-carbon operation optimization method and system, and the method comprises the steps: constructing an energy data asset library to form a commercial operation data flow; injecting the commercial operation data flow into an air conditioner asset evaluation model to generate an operation performance evaluation report; meanwhile, the business prediction engine generates a prospective insight report by using historical and market data; constructing a linear programming problem through the two reports, and outputting an operation strategy combination according to an operation target; selecting one item in the combination, analyzing the item into a scheduling plan, and issuing and executing the scheduling plan; and the execution performance is audited, and generated data is fed back to the evaluation model and the prediction engine for calibration, so that a closed-loop low-carbon operation optimization system is formed. And the system generates a specific production instruction through the operation performance and the insight report, and feeds back the model for calibration when a deviation occurs, so that rapid and accurate response to resource scheduling is realized, and the response efficiency of resource scheduling and the reliability of the system are improved.

A computer-implemented system (100) for real-time detection of supply chain disruptions and forecasting of the financial impact in a computer-driven ERP environment, wherein the system (100) comprises: a data acquisition interface (1) configured to continuously capture structured and unstructured operational data from one or more ERP modules, supply chain execution systems, and enterprise event streams; a data management and harmonization engine (2) configured to validate, normalize, deduplicate and semantically map the captured data into a unified, time-aligned canonical enterprise schema; a disturbance signal fusion and anomaly detection engine (3) configured to fuse signals from multiple sources and detect disturbance events using one or more machine learning models to output a disturbance value, event class and a list of affected nodes; a supply chain dependency graph and a digital twin builder (4) configured to create and update a dynamic dependency graph representing suppliers, facilities, transportation routes, SKUs, orders, contracts and lead times, and propagate disruption effects across the graph; a financial impact forecasting engine (5) configured to estimate the real-time and forward-looking effects of the disruption on one or more financial measures, including sales, margin, working capital, cash flow, service level penalties and inventory holding costs, and furthermore the effects on the cost of goods sold (COGS) and / or the landed costs using a Kl-based forecast with a range of uncertainty; a module for coordinating corrective actions and automating workflows (6) configured to generate ranked corrective actions and initiate ERP workflow steps, including reordering, reassignment, alternative sources of supply, production rescheduling and logistics diversions, wherein the ranked corrective actions include recommendations to resolve the disruption by using available stock, reassigning low-priority shipments or diverting shipments that can be delivered later within a lead time window; an explainability, audit trail and compliance logging module (7) configured to record model inputs, feature assignments, decision justifications, scenario assumptions and action results as an immutable audit trail; and a visualization, alerting and collaboration interface (8) configured to output real-time alerts, dashboards and scenario comparisons to authorized users and downstream systems via APIs and role-based access controls, wherein the interface (8) generates a quick report view that displays early disruption signals and impact on manufacturing costs, delivers notifications to mobile devices, triggers a special urgent notification if the disruption affects a high-priority trading partner, and accepts user responses on mobile devices that automatically and without delay trigger corrective actions or updates in the ERP system.

The invention discloses a storage goods allocation dynamic optimization method based on AI, and belongs to the technical field of intelligent storage and logistics management.The method comprises the steps that firstly, multi-dimensional data of goods, storage, business and the like are obtained; then, calculating a time-sensitive goods location-goods matching degree score integrating future delivery frequency of goods, association degree with adjacent goods, physical distance and a dynamic time urgency factor changing along with approaching of expected sorting time; a reinforcement learning model is used, the matching degree score is used as a key decision basis, a composite reward function considering overall layout gain, physical handling cost and high-urgency-degree task reward is used for guidance, and a dynamic goods allocation optimization scheme with globality and perspectiveness is output; according to the method, continuous self-adaptive adjustment of the storage layout can be realized, and the sorting efficiency and the response capability of the warehouse are remarkably improved.

The invention relates to the field of safety monitoring, and particularly discloses a calcium formate production process safety monitoring method and system, which utilize a distributed control system to collect original production data, and ensure time consistency of all monitoring variables through data synchronization processing, thereby laying a foundation for subsequent analysis. Thirdly, performing feature engineering processing on the synchronized data, extracting features such as short-term and medium-term change rates, moving average values and standard deviations of temperature, pressure and stirrer current, constructing state features of the reaction kettle, and capturing dynamic operation information of the system; in order to predict potential risks, a trend prediction model based on deep learning is adopted, a historical temperature sequence is converted into a future temperature trajectory, and prospective pre-judgment on the temperature trend is provided; meanwhile, the current system health condition is evaluated in combination with a state recognition model, the risk level is determined through a decision tree model according to the abnormal score and the predicted temperature maximum value, and graded early warning is achieved.

The invention relates to the technical field of network store evaluation, in particular to a big data network store evaluation method driven by a regional operation strategy map, which comprises the following steps of: respectively calculating a store flow value index and a real-time value index based on flow data and operation profit data of a target network store; constructing a linear algebraic model to generate a comprehensive value index of the store; evaluating the operation condition of the store based on the comprehensive value index of the store, and generating a regional operation strategy map in combination with a geographic information system technology; and predicting a future operation condition and outputting an operation risk index by using a distributed deep learning model so as to optimize a regional operation strategy map and generate an intelligent operation strategy. According to the method, dynamic monitoring and prospective analysis of the operation state of the network store can be realized, the intelligent level of evaluation and the accuracy of decision making are improved, and the problems of response lag and lack of strategy output in an existing evaluation method are solved.

The invention discloses an industrial Internet of Things prospective security defense method and system based on reverse deduction modeling, terminal equipment and a medium, and relates to the technical field of industrial Internet of Things security defense. The method comprises the following steps: constructing a predefined future security threat scene set of the industrial Internet of Things; backtracking an attack path through a reverse deduction algorithm based on the set, and determining a potential vulnerability association topology; carrying out vulnerability quantitative modeling on the equipment layer, the protocol layer and the authority layer to obtain a quantitative association topology; constructing a cognitive pressure test environment to verify the availability of vulnerabilities, and outputting attack behavior feature data; and dynamically generating a defense strategy adaptive to the business demand based on the data. According to the invention, future threat prospective pre-judgment is realized through reverse deduction, zero-day vulnerabilities of an industrial control protocol are covered, industrial resource constraints are adapted, security defense and service real-time performance are balanced, and the accuracy and prospective performance of security protection of the industrial Internet of Things are improved.

The invention belongs to the technical field of intelligent building and heating ventilation air conditioner control, and particularly discloses an air conditioner system look-ahead optimization control method based on rolling prediction and global collaboration, which comprises the following steps: performing preprocessing and feature construction on air conditioner system data to obtain a historical data window; inputting the window into a Transform prediction model to obtain a future prediction sequence; outputting a group of global optimal set values through an optimization strategy generator, calculating an initial set value by the optimization strategy generator by adopting an empirical formula group on the basis of a prediction sequence, and carrying out iterative solution by taking the initial set value as a starting point and based on a system total energy consumption model of a physical coupling relationship between fusion equipment, so as to obtain the global optimal set value enabling the total energy consumption of the system to be the lowest; and issuing the global optimal set value to an equipment controller for execution, and updating the Transform prediction model according to the actual operation data. The problem of large inertia lag of the air conditioning system can be solved, and accurate and reliable prospective energy efficiency optimization is achieved.

The invention discloses a supply chain e-commerce system and method. The system comprises a commodity production management module, a logistics management module, a commodity traceability module and a terminal market management module. And the terminal market management module is used for measuring and calculating future market demands through a demand prediction model based on the reservation information containing the user levels and the purchase information, and generating technical instructions for guiding upstream supply chain operation in combination with real-time inventory and production speed, such as inventory early warning or storage scheduling plans. The system also has a self-optimization closed-loop feedback mechanism, takes subsequent actual sales data as new input, and corrects and optimizes the demand prediction model. By constructing a data-driven and self-optimized closed-loop management system, the technical problems that an existing e-commerce platform lacks effective prediction of future market demands and cannot realize prospective and dynamic supply chain management are solved, the precision of production planning can be remarkably improved, and cost reduction and benefit improvement are realized.

The invention relates to the technical field of fan control, and discloses a fan control method and system based on a laser radar, and the method comprises the steps: carrying out the hierarchical scanning of a wind field in front of a fan through the laser radar, obtaining multi-dimensional wind condition data, carrying out the preprocessing of the collected data, and extracting the wind condition characteristic parameters, the characteristic parameters are compared with a historical database to generate a yaw correction path, a pre-control instruction is generated based on future wind regime prediction, a safety instruction is triggered through wind regime risk judgment to cover the pre-instruction, and after execution is completed, the database is updated to form closed-loop optimization. According to the invention, the fusion of prospective accurate wind facing and intelligent safety protection is realized through the laser radar, the yaw precision and the power generation efficiency are improved, and meanwhile, the operation reliability of the unit under the complex wind condition is enhanced.

The invention discloses an enterprise energy consumption management method based on data fluctuation load analysis. The method comprises the following steps: collecting multi-dimensional real-time operation data of a power enterprise production system; in a sliding time window, extracting fluctuation characteristics such as standard deviation, kurtosis and spectral entropy from the time sequence data of the key parameters, and converting the fluctuation characteristics into fluctuation characteristic vectors; constructing a data fluctuation load model, and learning a non-linear relationship between fluctuation feature vectors and system energy efficiency and carbon emission indexes; on the basis of a model prediction result, a fluctuation load index (FLBI) is calculated and used for quantitatively evaluating the health bearing capacity of the system for operation fluctuation; and finally, according to the FLBI value and the change trend thereof, proactive sub-health early warning, root cause diagnosis and accurate optimization control suggestions are generated. According to the invention, the information is used as core information for analyzing the dynamic health degree of the system, and the transformation from post-event alarm to pre-event early warning is realized.

The application relates to the fields of data processing and intelligent prediction, in particular to a quality prediction system and method based on multi-dimensional feature coupling driving, which comprises a business logic solidification module, a real-time working condition comparison module, a risk trend extrapolation module and a final risk grading module; the business logic solidification module is used for refining and solidifying business influence logic and generating static risk factors; the real-time working condition comparison module is used for comparing real-time operation data and generating a dynamic deviation file; the risk trend extrapolation module is used for processing the time sequence of the dynamic deviation file and outputting a forward-looking risk track; and the final risk grading module is used for integrating the static risk factors and the forward-looking risk track, grading and outputting a final quality risk evaluation value. The application solves the problem that traditional methods are difficult to integrate multi-dimensional information by deeply coupling the static inherent risk of a business process with the dynamic evolution trend of real-time data, quantifies the global correlation effect of the current deviation in the business process, and improves the accuracy and interpretability of quality risk prediction.

The invention relates to the technical field of information management systems, and discloses a marketing data processing method and system, and the method comprises the steps: outputting a corresponding customer role according to the commodity identification information in each piece of sales order data; generating a multi-dimensional monomer characteristic value for each sales order data code; all sales orders collected in the same time granularity and the same geographic partition are processed into an analysis unit; order feature extraction is carried out on all sales orders in each analysis unit according to the individual feature values to generate unit features; and according to the unit characteristics of each analysis unit, constructing a space-time correlation topology network to obtain a dynamic evolution rule between the analysis units, and outputting a group consumption behavior mode under the common influence of the time sequence correlation and the space correlation. According to the method, the fluctuation trend of the sales data on the space-time level can be obtained, and support is provided for prospective marketing decisions.

The embodiment of the invention provides a recommendation method and device based on real-time track data and a computer readable medium. According to the scheme, a historical track sequence of a user and potential dynamic information in corresponding context information are effectively utilized; the actual demand and intention of the user are perceived in advance through the time sequence dependency relationship among the historical track points reflected by the historical track sequence and the context information, so that the predicted path is more accurate, and on this basis, the appropriate recommended merchant is pushed to the user, so that the merchant recommendation with higher perspectiveness is completed, and the user experience is improved. The recommendation accuracy is improved, and the actual requirements of the user are better met.

The present application relates to a resource recommendation method, device, equipment and medium based on dynamic portraits of small enterprises. The method extracts key events from multi-dimensional data of enterprises and standardizes them to form an initial event sequence, calculates importance scores based on frequency and weight, and adaptively filters to generate a core event set; calculates the dependency strength between events based on timestamps and semantic features, and constructs a causal event relationship map; uses graph neural networks and gated recurrent units to learn event embedding and time series features to generate dynamic portrait vectors; analyzes the impact weights of historical events through the attention mechanism to predict future demand distribution; optimizes resource urgency and matching in combination with enterprise capacity constraints to generate a forward-looking recommendation list; triggers incremental map updates and model retraining through prediction deviations to form a closed loop. Ultimately, dynamic capture of development trajectories and accurate prediction of resource needs are achieved, significantly improving recommendation timeliness and business adaptability.

This invention belongs to the field of financial investment decision-making technology, specifically a financial investment decision-making assistance method and system based on game theory and multi-source big data. It collects and processes multi-source financial data, constructs a unified data foundation, and uses machine learning to generate multi-dimensional market prediction signals based on this foundation. These prediction signals are then input into a game theory model to simulate the behavior of market participants, solve for equilibrium strategies, and generate investment or risk control suggestions based on the game results, which are then visualized. This invention overcomes the limitations of single data sources by integrating multi-source big data and generating multimodal prediction signals. By combining game theory to model the complex interactive behaviors of market participants, it can deduce market evolution trends from a global perspective, thereby assisting in the formulation of more forward-looking investment or risk control strategies. It can simulate information asymmetry, strategy interaction, and dynamic evolution processes in the market, thus providing a better understanding of market fluctuations and the impact of sudden events.

A control method and system for tart crust processing equipment based on big data modeling, belonging to the field of food industry equipment control technology, includes the following steps: obtaining dough kneading and proofing process data based on batch identifiers; obtaining pre-processing state data of the stamping equipment for the target dough stamping process, and combining the kneading and proofing process data with a trained stamping quality prediction model to obtain a predicted quality value of the finished tart crust after stamping; comparing the predicted quality value with a preset quality target to determine whether the preset stamping process parameters need adjustment; if so, adjusting the preset stamping process parameters to obtain the execution stamping process parameters; and controlling the stamping equipment to perform the stamping operation on the target dough according to the execution stamping process parameters. This approach achieves forward-looking prediction of the finished tart crust quality and enables timely intervention and adjustment, ultimately improving the stability and consistency of product quality.

The invention relates to the technical field of cloud data processing, and discloses a cloud financial data analysis and early warning device which comprises a cross-domain event aggregation module, a dynamic graph construction module, a causal flow inference module, a pattern convergence analysis module and an interactive presentation module. The method comprises the following steps: aggregating business events from a plurality of business systems, and constructing a dynamic business process map; then, identifying an abnormal disturbance event in the map, and generating one or more abnormal causal chains connected to the quantitative financial influence based on the event tracing so as to generate tactical early warning; the device performs pattern clustering and root cause analysis on the plurality of abnormal causal chains to generate strategic insights. According to the method, the dynamic graph is constructed, and causal inference and mode convergence are executed, so that the problems that traditional financial analysis is lagged and a deep causal relationship between business and finance is difficult to reveal are solved, and prospective and penetrating analysis and early warning from a single business event to a systematic risk root cause are realized.

The application discloses an artificial intelligence driven intelligent prediction and optimization decision system.In the application, the inside of the creative thinking module is internally provided with a knowledge fusion submodule, an association discovery submodule, a pattern recognition submodule and a creative generation submodule; the knowledge fusion submodule ensures that the decision is based on a comprehensive and diversified knowledge base, avoiding the limitations of a single information source; the association discovery submodule further excavates potential connections among data, providing unexpected insights for decision-making; the pattern recognition submodule further refines valuable information and trends from complex data, enhancing the reliability of prediction; and the creative generation submodule injects creative thinking into the core decision-making process, not only providing traditional solutions, but also generating novel and unique strategies.The synergistic effect of these modules enables the system to make more accurate and forward-looking decisions in complex and changing environments, effectively improving the competitiveness and response capability of enterprises.

The invention relates to the technical field of data assets, and discloses a multi-source information fusion enterprise data asset value reduction sign dynamic identification method, which comprises the following steps: carrying out data feature extraction and standardization processing on an enterprise internal data asset value reduction sign by adopting an event-driven time sequence coding method; based on an early-stage multi-input LSTM model and a multi-head self-attention mechanism fusion method, data feature extraction and standardization processing of data asset value reduction signs based on stock market information are carried out; feature dimension alignment is carried out, feature-level fusion is realized by using a multi-head attention mechanism, and decision-level weighted integration is carried out on prediction results; the influence amplitude of enterprise internal and market factors is quantified through a risk intensity calculation method, and the risk direction is identified in combination with a trend judgment method. By using the scheme of the invention, accurate capture and prospective early warning of the data asset value reduction sign are realized, and the problems of information splitting and single evaluation of a traditional method are solved.

The application provides an industrial equipment fault prediction and diagnosis method and system based on a multi-modal large model. First, multi-modal time series data of an industrial rotating machine during operation is collected. Second, the data is input into a pre-trained multi-modal large model to extract frequency domain features, hot spot area temperature change trend features, and abnormal sound component features. Then, a cross-modal attention mechanism is used to fuse the three extracted features to generate a unified multi-modal equipment state embedding representation. Then, a comprehensive health index is calculated, and a sequence prediction method is used to deduce the future change trajectory of the comprehensive health index. Finally, when the future change trajectory indicates that the comprehensive health index will cross the preset warning boundary value, a diagnosis conclusion and maintenance decision are output. The technical solution provided by the application not only accurately predicts the early stage of industrial equipment failure, diagnoses the type, and provides forward-looking maintenance decision support, but also improves the intelligent level of equipment health management and operation and maintenance efficiency.

The invention relates to the technical field of computer data processing and project management, and discloses an industrial-grade business and financial fusion method and system based on project management and a storage medium, and the method comprises the following steps: S1, obtaining multi-source heterogeneous data in a project, and mapping the data into business and financial entities; s2, abstracting the state of the business and financial entity into a system state vector, and establishing a state transition matrix for defining a time evolution rule of the system state vector; s3, performing prediction or risk analysis on future financial indexes of the project based on evolution of the system state vector driven by the state transition matrix; and S4, according to the prediction or risk analysis result, generating a decision scheme for intervening the project execution, the real-time business operation of the project and the financial state change are deeply fused, and through establishing the dynamic state space evolution model, the prospective and quantitative prediction of future financial indexes of the project is realized.

The present invention discloses a method for optimizing and updating user portraits in combination with big data, which relates to the field of data processing technology. The method comprises: performing big data association retrieval based on target user attribute information to determine an associated user group; reading the target user's behavioral feature sequence in a predetermined time zone, analyzing and generating a first predicted activity coefficient sequence for the future time zone; reading the behavioral feature sequence set of the associated user group to generate a second predicted activity coefficient sequence for the future time zone; analyzing the scenario factors of the future time zone in combination with big data to determine the scenario impact coefficient; optimizing the user portrait update frequency based on the first predicted activity coefficient sequence, the second predicted activity coefficient sequence, and the scenario impact coefficient, and executing the update within the future time zone. This achieves the technical effect of enhancing association analysis and forward-looking analysis capabilities and improving update adaptability.

The invention relates to the technical field of commodity sales data, and discloses a commodity sales data analysis method based on deep learning, which comprises the steps of collecting transaction information, user behavior information and market environment information, extracting multi-dimensional feature indexes, constructing a dynamic adaptation system to obtain key parameter indexes, and analyzing the key parameter indexes. And generating optimization strategy parameters through a deep learning model, and adjusting a transaction mode, inventory management and marketing activities according to the optimization strategy parameters. According to the method, multi-level and multi-dimensional accurate analysis and regulation can be realized in combination with dynamic characteristics of the text travel industry and diversified influence factors, the prediction accuracy and the operation efficiency are improved, resource waste is avoided, and the method has relatively high practicability and perspectiveness.

A carbon neutralization-oriented production line carbon emission real-time accounting and optimization method relates to the technical field of industrial low-carbon manufacturing, and comprises the following steps: obtaining operation data of each production sub-sequence, constructing a local carbon emission estimation model of each production sub-sequence, and outputting an estimation threshold interval of a carbon emission value of each production sub-sequence; performing prospective accounting on each production sub-sequence, and dividing the production sub-sequence into a normal state or an abnormal early warning state; when the production subsequences are in the abnormal early warning state, acquiring a feature network of the production subsequences, performing carbon emission quantitative analysis on the feature network, and judging whether to execute local pre-adjustment operation or not according to a carbon emission quantitative analysis result; a data sharing platform and an overall carbon emission prediction model are constructed, data sharing is carried out on operation data of a production subsequence and a local pre-adjustment operation result, whether overall carbon emission optimization operation is triggered or not is judged according to a data sharing result, and the efficiency and accuracy of real-time accounting and optimization of production line carbon emission are remarkably improved.

The invention discloses an engineering project dynamic cost management and control system, which relates to the technical field of engineering project management, and comprises the following steps of: in a data acquisition layer, acquiring multi-dimensional data such as energy consumption of construction equipment, water and electricity consumption, design budget, purchase contract, construction progress and the like in real time by virtue of butt joint between Internet of Things equipment and a multi-service system AP I; the collected data is cleaned and standardized, real-time cost data is dynamically compared with budget and historical data, an analysis report is generated by calculating a cost deviation coefficient (CDC), abnormal early warning is triggered according to a multi-level threshold value, a cost prediction model is trained based on a machine learning algorithm, and the real-time cost prediction is realized. The cost fluctuation trend and risk level evaluation of each link are output, prospective anticipation of risks is achieved, data barriers of departments such as design, purchase, construction and finance are broken through, multi-terminal real-time access and sharing are supported, all departments can collaboratively formulate and execute processing schemes online, whole-process closed-loop management is achieved, and economic benefits are improved.

The invention discloses a financial condition pressure testing method based on generative artificial intelligence. The method comprises the steps of integrating multi-source heterogeneous data and constructing an asset combination relation graph; defining a layered Bayesian parameterized structure; designing a graph generative adversarial network taking the macroscopic impact scene and the relation graph as conditions, wherein the network comprises a generator and a discriminator for simulating risk conduction; performing adversarial training and Bayesian posteriori inference to train the network; and utilizing the trained generator to generate joint probability distribution of future financial performance of the asset combination under a given pressure scene, calculating a risk index, and generating a probabilistic analysis report. According to the method, the hierarchical Bayesian, the graph generative adversarial network and the dynamic graph updating mechanism are fused, so that the problem of data sparsity is effectively solved, a nonlinear risk conduction mechanism of dynamic change can be accurately described, transformation from deterministic single-point assessment to probabilistic analysis is realized, and the objectivity and foresight of risk assessment are improved.