802 results about "Influence analysis" patented technology

The invention relates to the field of bidding risk assessment, and discloses an AI technology-based intelligent bidding data analysis method and system and a storage medium, and the method comprises the following steps: obtaining bidding data, constructing a multi-dimensional space-time tensor, and extracting low-dimensional features through dynamic tensor decomposition; establishing a dynamic enterprise relationship graph, modeling an enterprise cooperation relationship by using a graph neural network, and capturing a bidding behavior mode between enterprises; a multi-target joint optimization model is constructed, and market trend prediction, enterprise credit assessment and risk early warning are synchronously realized; performing adversarial training by adopting a generative adversarial network, and dynamically adjusting task weights in combination with Pareto optimization to balance multi-task conflicts; and outputting a market prediction report, an enterprise credit portrait and a risk early warning list, and providing policy and regulation change influence analysis and a risk evidence chain. According to the method, bidding and tendering data are deeply mined through the AI technology, the market insight and the abnormal bidding behavior recognition capability are improved, and the method has wide application value.

According to the multi-energy complementary power distribution system data analysis method based on deep learning, firstly, preprocessing and feature extraction are carried out on multi-source heterogeneous sensor data, and key indexes reflecting the health state of equipment are obtained; and obtaining globally consistent equipment state representation through a spatio-temporal data fusion technology. And then a multi-fault mode knowledge base is constructed, and a multi-label classification algorithm of deep learning is adopted to identify fault types. And finally, establishing a mapping relation between an equipment topological structure and a fault propagation path by using an attention mechanism and a graph convolutional network, and reasoning a specific position of equipment damage through fault traceability and influence analysis. The robustness and generalization ability of the model are improved by adopting methods of data enhancement, transfer learning and the like, and real-time and reliable fault location is realized through edge calculation. According to the method, the equipment fault in the multi-energy complementary power distribution system can be accurately positioned, and the reliability and safety of the system are improved.

The invention relates to the technical field of surface quality detection, in particular to a PVC floor surface coating defect detection method and system, and the method comprises the steps: carrying out region division on a to-be-detected coating surface, obtaining a region scanning image, and carrying out image optical compensation on the region scanning image; analyzing high-dimensional image features of each area scanning image, performing difference analysis on the high-dimensional image features and reference features, and selecting a surface defect area; a high-dimensional defect feature analysis model is established, defect positioning information and defect geometric parameters are analyzed and generated, and the defect type of the surface defect area is judged according to the defect positioning information and the defect geometric parameters; and according to the defect positioning information and the defect geometric parameters, performing quantitative evaluation on the sensitivity weight of the surface performance of the to-be-detected coating in combination with defects, and outputting an overall surface performance influence analysis result. According to the invention, the problem that the capability of identifying small-size and complex-shape defects is insufficient is effectively solved.

The invention discloses a geological disaster deformation monitoring method based on remote sensing data, and relates to the technical field of environment monitoring, and the method comprises the following steps: S1, data source selection and preprocessing; s2, region feature extraction; s3, differentiating the multi-temporal remote sensing images; s4, analyzing a time sequence; s5, performing three-dimensional reconstruction on the deformation area; s6, settlement and landslide monitoring; s7, performing fusion analysis on environment and meteorological data; s8, performing data anomaly detection and automatic early warning; and S9, risk assessment and influence analysis. According to the geological disaster deformation monitoring method based on the remote sensing data, SAR, optical images and LiDAR data are combined, the defect of dependence of a single data source in the prior art is overcome, dynamic data fusion and time sequence analysis are used, multi-dimensional real-time monitoring of earth surface deformation is achieved, the monitoring precision and timeliness are improved, and the geological disaster deformation monitoring method based on the remote sensing data has good application prospects. More comprehensive geological disaster early warning can be provided, and the effectiveness of disaster prevention and control is greatly enhanced.

The present invention provides a method for impact analysis of port construction on a coastal ecotone based on remote sensing data, which belongs to the technical field of remote sensing application. The method quantifies landscape data and satellite image data, and then converts the data into intuitive images to observe the spatio-temporal change of the coastal ecotone after port construction. The method includes: step 1: obtaining remote sensing image data of a research region; step 2: preprocessing the remote sensing image data to obtain processed images; step 3: establishing an evaluation system of human disturbance indexes; and step 4: generating a spatio-temporal distribution map of regional disturbance indexes. The present invention provides a method capable of quantifying the impact range and the impact degree of the port construction factor on the coastal ecotone, provides an idea for reducing the impact of port construction on a surrounding environment.

The invention relates to the technical field of data analysis, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data consanguinity map processing method, device, equipment and medium, and the method comprises the steps: collecting metadata change information and data operation event information, and building a mixed consanguinity map model; monitoring a metadata change event and a data operation event, and triggering incremental updating based on an event type; and performing data change influence analysis based on the updated blood relationship map model, generating an analysis result, and outputting the mixed blood relationship map model and the data change influence analysis result. According to the invention, by collecting metadata, incrementally updating the blood relationship map and carrying out data change influence analysis, full-link data tracing and influence analysis are realized, the problems of blood relationship breakage and insufficient real-time data tracing in traditional data blood relationship analysis are solved, and the accuracy of data tracing and the timeliness of business decision are improved.

The invention relates to a multi-mode AIGC cold-chain logistics path optimization decision-making method and a multi-mode AIGC cold-chain logistics path optimization decision-making system. The method comprises the following steps: inputting position data, temperature data, road condition data and weather data in a cold-chain logistics system into an AIGC model of a stack auto-encoder for feature extraction and reconstruction error calculation to obtain a feature index set; performing space-time interval division and similarity matching on the cold chain distribution path to obtain an interval reference path library; inputting a hierarchical control model for stage processing to obtain an initial control parameter; an objective function including temperature constraint, time window constraint and vehicle dynamics constraint is constructed for solving, and a path optimization scheme is obtained; performing road condition random disturbance and temperature fluctuation influence analysis to obtain a path risk scoring index; and performing feedback correction and iterative optimization on the initial control parameter to obtain a target control parameter. According to the invention, the temperature control precision is ensured, and the distribution efficiency and the system reliability are effectively improved.

The invention provides an automatic generation method and system for an intelligent pavement maintenance scheme, and relates to the technical field of road maintenance, and the method comprises the steps: obtaining a pavement image, and recognizing a disease through a convolutional neural network; a multi-scale grid is utilized to adaptively extract geometric features, and dynamic evaluation vector classification is constructed; matching a basic scheme based on the evaluation result; a maintenance scheme is optimized through a cross attention enhanced hierarchical decision network; and space-time influence analysis and dynamic adjustment are carried out. The pavement maintenance efficiency and quality are improved, the resource waste is reduced, and the influence on traffic is reduced.

The invention provides a digital asset-oriented network security management method and system. The method comprises the following steps: acquiring a digital asset list and a business process document, identifying keywords in the document by using a natural language processing technology, constructing an association relationship graph of digital assets and business processes, and discovering potential association; directly connected business process nodes of each digital asset are extracted from the incidence relation graph, an asset business influence range report is generated, and a many-to-many mapping relation between the assets and the businesses is displayed in a matrix form; in the review process, association and quantitative indexes, including a risk thermodynamic diagram, an influence degree scatter diagram and the like, among assets, risks and business processes are dynamically presented through a visualization technology, and a visual information transmission channel is provided for all parties; and after meeting review is consistent, qualitative influence analysis of the business department and quantitative loss estimation of the technical department are integrated into a business influence analysis result by adopting a fuzzy comprehensive evaluation method, and a comprehensive business influence level is obtained.

The invention discloses an unmanned intelligent inspection method and system for an intelligent power plant, and relates to the technical field of power plant inspection, and the method comprises the steps: carrying out the inspection partitioning according to the function partitioning and environment attribute information of a target power plant, and arranging an inspection equipment group and an edge processing node; receiving real-time environment monitoring data through each edge node, adaptively optimizing a conventional routing inspection scheme, and executing intelligent routing inspection to obtain a routing inspection data set; and obtaining a local inspection result through independent anomaly analysis, uploading the local inspection result to a master control center for correlation influence analysis, and correcting the local inspection result. The technical problem that in the prior art, along with the complexity trend of an intelligent power plant, a traditional inspection method cannot adapt to complex and changeable inspection requirements, so that the inspection efficiency and accuracy are low is solved, and real-time monitoring and quick response of the operation state of the power plant are achieved through unmanned intelligent inspection. And the inspection efficiency and the data accuracy are improved.

The invention provides a Gaussian modeling method combined with laser scanning point cloud data processing, and relates to the technical field of three-dimensional modeling, and the method comprises the steps: carrying out the registration of a plurality of point cloud data sets through employing a point cloud fusion algorithm, and obtaining a target point cloud data set; performing data precision, position deviation and noise influence analysis on the plurality of point cloud data sets, determining point cloud data precision, a position deviation coefficient and a noise influence coefficient, and comprehensively evaluating and determining point cloud acquisition precision; setting a data dimension reduction proportion according to the point cloud acquisition precision, and performing covariance matrix dimension reduction on the target point cloud data set; and selecting a predetermined kernel function, performing data fitting and Gaussian process regression modeling on the dimension reduction point cloud data set, and outputting a surface fitting model. According to the method, the technical problems that the requirements for storage and computing resources are too high and efficient three-dimensional modeling cannot be realized due to the fact that a large number of covariance matrixes need to be calculated in a traditional Gaussian modeling method can be solved; the dimensionality of data can be effectively reduced, and the efficiency of three-dimensional modeling is remarkably improved.

Systems and processes are disclosed for a multidimensional, cross-node, hashchain, network-based Meta-Content Enabler (MCE) providing real time anomaly detection and impact analysis. Unique hexadecimal sequence identifiers are generated based on real time indexing and categorization to create a golden virtual metadata copy used by AI engine to determine content score to identify the degree of deviation therefrom, and to identify the hexadecimal nodes in the hashchain. The identified discrepancy is verified across cross-node hashchains to give end to end parallel impact analysis on the anomaly. By leveraging real-time monitoring, multidimensional verification, and blockchain-based storage, the system provides a robust and efficient solution for ensuring the accuracy and integrity of user activities. The system integrates privacy-preserving techniques, such as differential privacy or secure multi-party computation, to protect sensitive metadata. These techniques enable the system to analyze and process the metadata while preserving the privacy of individual users.

The invention provides a temperature regulation and control method in a photovoltaic glass annealing process, relates to the technical field of photovoltaic glass processing, and aims to obtain photovoltaic glass composition structure information, retrieve a frequent annealing curve based on the glass composition structure information and determine an annealing furnace for annealing treatment. Inputting the reference annealing temperature curve into a controller of the annealing furnace for annealing control, monitoring in real time, comparing the multi-point temperature time sequence with the reference annealing temperature curve, identifying a transverse temperature difference gradient and a longitudinal temperature difference gradient, and performing annealing quality influence analysis based on the transverse temperature difference gradient and the longitudinal temperature difference gradient. And regulating and controlling the annealing temperature according to the annealing quality influence index. The technical problems that in the prior art, the photovoltaic glass annealing temperature is lack of flexible regulation and control, so that the temperature accuracy and uniformity are poor, and the annealing quality defect exists are solved. The technical effects of ensuring the accuracy and uniformity of the annealing temperature and improving the annealing quality and performance of the photovoltaic glass are achieved.

The invention provides a cluster reliability test method based on fault simulation, which comprises the following steps: acquiring injection parameter information, and performing fault simulation in a cluster based on the injection parameter information and a fault transfer model to obtain a complex fault scene; key performance index records are obtained in real time based on the complex fault scene; executing a reliability detection step based on a preset reliability detection model and the key performance index record, and obtaining a data analysis report; and obtaining a reliability score, an influence analysis result and a risk prediction result based on a data analysis report, a weighted scoring algorithm, an anomaly detection algorithm and linear regression analysis, and completing reliability detection of the cluster. According to the cluster reliability test method and system based on fault simulation provided by the invention, the test efficiency and the reliability detection level are greatly improved, the fault simulation is carried out in the cluster and the reliability detection model is combined, so that the accurate quantification of the reliability detection result is realized, and the reliability detection efficiency and the detection capability are improved.

The invention relates to the technical field of safety management, in particular to a safety management early warning method and system based on the digital twinning technology, and the method comprises the following steps: collecting the entity state data of a plurality of physical entities in a campus, and constructing a digital twinning model containing the entity state data; defining a current scene risk factor, and analyzing the interaction influence between the scene risk factors by performing simulation interaction influence analysis between the scene risk factors in the digital twin model; based on a simulation interaction influence analysis result, constructing an incidence matrix between risk factors, establishing a dynamic association graph between physical entities, and evaluating the weight of each node and edge through a graph calculation algorithm; and calculating a comprehensive risk score according to the dynamic association graph, and setting multi-level early warning levels. According to the method, real-time dynamic modeling of risk factors is realized, limitation of traditional single entity monitoring is avoided, a dynamic association graph based on data driving is provided, and risk propagation and influence ranges among physical entities are accurately described.

The invention provides an artificial influence weather-based image recognition method and system, and the method comprises the steps: firstly obtaining a meteorological image data set of a target region containing a plurality of original meteorological images in sequential arrangement, carrying out the feature extraction of the original meteorological images, and obtaining a meteorological element distribution feature and a dynamic evolution feature; and then, based on a preset weather influence analysis model, carrying out weather influence analysis on the meteorological element distribution characteristics and the dynamic evolution characteristics, generating weather intervention decision characteristics, finally, according to the weather intervention decision characteristics, generating an operation strategy of artificial influence weather, and sending the operation strategy to operation execution equipment to trigger weather intervention operation. Therefore, meteorological image features can be comprehensively and deeply analyzed, the operation strategy is scientifically and accurately generated, and the effect and efficiency of artificial influence weather operation are improved.

The invention belongs to the technical field of smart campus, and relates to a smart campus safety management early warning method and system based on big data. The method comprises the following steps: collecting registration information, real-time behavior data and state information of an environment of a foreign vehicle; constructing a dynamic space-time diagram, and generating an edge attribute reflecting a relationship between deployment nodes in the dynamic space-time diagram; calculating a reasonable path set of the foreign vehicle, and analyzing trajectory deviation data of the foreign vehicle; abnormal scoring is carried out on the external vehicle; generating a corresponding intervention control strategy; performing causal influence analysis and generating a decision basis; and training the local graph neural network model, and carrying out centralized aggregation and distribution updating on shared model parameters in a training result of the local graph neural network model. According to the method, a dynamic behavior modeling strategy based on a heterogeneous graph neural network is introduced, structural modeling and time sequence behavior evolution expression of campus moving target multi-source data are achieved, the system can capture tiny anomalies in individual trajectory evolution, and the abnormal behavior recognition sensitivity is improved.

The invention provides a control method and system for temperature return of a constant-temperature incubator. Wherein the real-time temperature in the constant-temperature incubator is monitored, external environment data is combined, the real-time temperature and a preset threshold value are compared and analyzed, and a temperature return starting signal is generated; based on the temperature return starting signal, a control algorithm based on self-adaptive dynamic programming is adopted, a temperature gradient equalization technology is applied, and a heating control instruction is generated; based on the heating control instruction, constructing a three-dimensional temperature distribution model by using a temperature field reconstruction algorithm, predicting a temperature change trend through a recurrent neural network, and generating temperature prediction information; and on the basis of the temperature prediction information, after a set temperature is reached, fine temperature control is automatically switched to, environmental influence analysis is carried out, measures are taken to offset adverse factors, and a temperature control result is generated. According to the control method and system for temperature return of the constant-temperature incubator, the temperature control precision and the response speed are remarkably improved, long-term stability is ensured, and a reliable and efficient environment is provided for biological experiments.

The invention discloses a throwing object influence analysis method based on a multi-mode large model in an expressway scene, and belongs to the technical field of intelligent traffic. In order to improve the real-time performance and accuracy of monitoring of the thrown objects, a segmentation module carries out fine-grained segmentation operation on image data output by a video acquisition and management module, and carries out segmentation and preliminary identification on the thrown objects in an image; the instruction generation module is used for integrating data from the instance segmentation module, the memory module and the thrown object detection task description, generating an output instruction and outputting the output instruction to the large model reasoning module for executing a thrown object analysis task; the input of the large model reasoning module comprises an output instruction of the instruction generation module and image data preprocessed by the video acquisition and management module, and the large model reasoning module is used for carrying out deep analysis and reasoning on the input data to generate an influence analysis result of the expressway thrown objects; and the evaluation module is used for comprehensively evaluating the expressway thrown object influence analysis result output by the large model reasoning module.

The invention relates to the field of power systems and intelligent power grids, in particular to an intelligent analysis system and method for the influence of multi-scene new energy power generation on a distribution network, and the system comprises a data collection module, a multi-scene modeling module, an influence analysis module, an intelligent evaluation module and an optimization suggestion generation module. And the data acquisition module, the multi-scene modeling module, the influence analysis module, the intelligent evaluation module and the optimization suggestion generation module are in communication connection with an external data bus. The hybrid digital twinborn model is constructed through the multi-scene modeling module, four typical scenes of new energy output fluctuation, extreme weather, AC / DC hybrid access and power grid topology change are dynamically simulated, and dynamic configuration of scene parameters (such as new energy permeability and load growth rate) is supported. According to the model, through fusion modeling (such as a power flow equation, an LSTM time sequence and a graph neural network GNN) driven by a physical mechanism and data, the running state of the distribution network under the multi-scene coupling effect can be reflected more truly.

The invention discloses a tuna pond culture strategy optimization method and system based on meteorological influence analysis, and the method comprises the steps: collecting regional meteorological data and pond culture monitoring data, and constructing a multi-modal pond ecological feature vector; establishing a weather-water body dynamic coupling model by using a space-time diagram neural network, and generating water body situation response prediction information; a breeding regulation and control strategy is made based on the prediction information, and strategy self-adaptive optimization is achieved through real-time monitoring data; evaluating the anti-disaster capability of the tuna after the meteorological influence is finished, screening high-quality germplasm resources by combining genetic analysis, and generating an anti-disaster breeding scheme. According to the method, full-chain optimization from meteorological early warning to culture regulation and control to genetic breeding is achieved, and the climate toughness and economic benefits of tuna pond culture are comprehensively improved.

The invention relates to an intelligent IT asset management system, and the system comprises a configuration asset management module which is used for automatically collecting IT asset information in a network, building a complete configuration item relation model, and supporting the dependency relation mapping and impact analysis between configuration items; the service desk management module is used for establishing a service catalog, defining a processing flow, response time and a service level protocol, automatically carrying out classification and priority evaluation on the received service requests through an intelligent classification engine, and distributing the service requests to proper processing personnel according to a preset rule; the contract supplier management module is used for full-life-cycle management of suppliers and contracts; the IT service financial module is used for multi-dimensional budget management and fine management of IT cost; and the multi-tenant management module is used for realizing entity separation and authority control. The problems of a traditional IT asset management system in the aspects of asset tracking, service management, cost control and the like are solved, and a set of complete IT service management solution is provided for enterprises.

The invention relates to the technical field of power grid risk prediction, and discloses a power grid risk monitoring method and system based on meteorological early warning, and the method comprises the steps: dividing a power grid monitoring region into a plurality of monitoring regions; defining a power grid risk fault type, obtaining a risk type set of different monitoring region types, and calculating a risk assessment index and a region influence index; generating a risk meteorological feature set, and calculating a meteorological influence index of the risk meteorological feature set on the risk type; and constructing a risk prediction model, performing risk prediction, and issuing meteorological risk early warning information. Through the risk monitoring method of the system, from data collection, risk assessment and meteorological influence analysis to risk prediction and early warning, the level of power grid risk management is comprehensively improved, the risk is monitored and predicted in real time, early warning information is issued in time, the response speed and efficiency for responding to the risk are improved, the occurrence probability and the influence degree of risk events are reduced, and the risk management efficiency is improved. The economic loss is reduced.

The invention discloses a severe clinical health care sensing system and method based on virtual reality, and belongs to the field of virtual reality. Injury information of a patient and the influence of a nursing action selected by a corresponding nurse on an injury position are obtained and imported into an action injury position influence analysis strategy for injury position influence analysis; the method comprises the following steps: acquiring physiological information of a corresponding patient to carry out physiological feature influence analysis, acquiring an injury position influence analysis result of a corresponding nursing process and a physiological feature influence analysis result, and carrying out nursing action matching analysis on the injury position influence analysis result and the physiological feature influence analysis result. The risk caused by the nursing action is accurately analyzed according to the vulnerability degree characteristic of the wound; according to the application, the physical abnormality of the patient is considered while the secondary injury of the wound caused by the nursing action is evaluated, and the danger caused by the nursing action is comprehensively judged.

The invention discloses a metal plate stamping forming performance prediction method based on big data, and the method comprises the steps: collecting test data and related data of a stamping forming metal plate, and carrying out the preprocessing of the test data and influence data; performing clustering analysis on the test data according to the condition similarity to obtain a sub-data set, and performing influence analysis on the related data according to the sub-data set to obtain a change coefficient; performing feature selection on the test data according to the change coefficient to obtain key features, and performing correlation analysis on the key features and the change coefficient to obtain a correlation coefficient; and constructing a metal plate stamping forming performance prediction model according to the correlation coefficient, inputting to-be-predicted data into the metal plate stamping forming performance prediction model, and outputting a prediction result. The method not only can improve the precision of metal sheet stamping forming performance prediction based on big data, but also has good interpretability, and can be directly applied to a big data performance prediction system.

The invention discloses a civil aviation airport ground guarantee resource scheduling method and device and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a real-time flight plan, and constructing a guarantee resource coupling network based on the real-time flight plan and a predefined guarantee flow rule base; performing conflict prediction and conflict influence analysis on the guarantee work of the future flight by traversing the guarantee resource coupling network, and generating a structured conflict early warning report; triggering an on-demand scheduling mechanism of a virtual resource pool for querying and allocating available resources on demand according to the structured conflict early warning report, querying available shared guarantee resources in the virtual resource pool, and generating a conflict resolution scheme which can be directly executed; and executing the conflict resolution scheme, monitoring an execution result through the Internet of Things data, feeding back the execution result to the guarantee resource coupling network, and calibrating a duration parameter in the guarantee resource coupling network. Through the constructed support resource coupling network, the prospective performance and the cooperative robustness of ground support scheduling are improved.

The invention relates to the technical field of equipment state evaluation, in particular to an evaluation method and system for the health state of offshore operation equipment. The method comprises the following steps: acquiring component data of offshore operation equipment; performing initial life marking on the data of the offshore operation equipment component to generate initial life marking data of the offshore operation equipment component; performing real-time operation data acquisition on the data of the offshore operation equipment component to generate real-time operation data of the offshore operation equipment component; performing component division on the real-time operation data of the components of the offshore operation equipment based on the components of the initial life mark data to generate novel component operation data and old component operation data; performing environment change influence analysis on the operation data of the novel part to generate an external change influence factor of the novel part. According to the invention, through multi-dimensional data acquisition, environment and electrical influence analysis, fatigue damage assessment and intelligent prediction, the accuracy of health state assessment of the offshore operation equipment is improved.

The invention relates to the technical field of wireless communication, and discloses a wireless communication operation and maintenance data analysis method and system, and the method comprises the following steps: in all sub-regions of a wireless communication operation and maintenance region, carrying out the high-frequency similarity analysis of the business flow value data of each historical time period, and constructing the data analyzed in each time period to obtain a first set; performing distance analysis on the first set of each time period, and constructing a second set through a distance analysis result; and performing influence analysis on the sub-regions in the second set, and constructing a third set according to an influence analysis result. According to the method, the analysis sub-regions which influence each other and are key in the wireless communication network can be accurately identified, so that operation and maintenance personnel can focus on key regions, carry out key monitoring on the key analysis sub-regions, distribute network resources more reasonably, increase bandwidth in the key regions, optimize base station configuration and the like, and the resource utilization efficiency is improved.

The invention relates to an underground engineering construction design technology, discloses a method for analyzing the influence of underground excavation under-crossing on an existing subway station, and improves the efficiency and precision of analyzing the influence of underground excavation under-crossing construction on the existing subway station. The method comprises the steps that by defining a structure and geometrical shapes and material attributes of a stratum, boundary conditions and external loads are set, grid division is carried out, and a finite element three-dimensional basic calculation model is constructed; according to theories of porous media and the like, a multi-physical field coupling model of a seepage-deformation-temperature full coupling effect is constructed; the rigidity of the underground excavation vertical pile foundation is calculated, and the rigidity of the underground excavation vertical pile foundation is corrected based on the influence factors of the soil squeezing effect and the negative friction resistance; subsurface excavation construction is subdivided into a plurality of stages, structural stress, deformation and stratum displacement are calculated through an incremental method, and the timeliness of a supporting structure and the influence of different excavation modes on stratum disturbance are analyzed; and obtaining a structural response through finite element iteration solution, and judging the influence of underground excavation under-crossing on the existing subway station.

The invention discloses a code change influence analysis method and device, equipment and a storage medium, and relates to the technical field of software, the method comprises the following steps: performing grammar analysis on a multi-programming language code to obtain a back-end abstract syntax tree and a front-end abstract syntax tree, and converting the back-end abstract syntax tree and the front-end abstract syntax tree into a uniform syntax tree structure through a front-end and back-end calling mapping chain; a cross-language dependency graph is constructed according to the unified syntax tree structure, the cross-language dependency graph is composed of nodes and edges, the nodes represent code entities, and the edges represent dependency relations; and when the code change of the bank business is detected, traversing the cross-language dependency graph through a graph traversal algorithm to obtain a code change influence result. Due to the fact that the back-end abstract syntax tree and the front-end abstract syntax tree of the multi-programming language codes are constructed and connected in series, the perfect cross-language dependency graph is constructed, the front-end link and the rear-end link are connected, cross-language tracking can be achieved when the codes of the banking business are changed, and the influence of code change can be accurately analyzed.