596 results about "Analysis tools" patented technology

The invention relates to the technical field of computing power scheduling, and discloses a heterogeneous resource computing power intelligent scheduling method and system. According to the method, real-time state monitoring is conducted on heterogeneous computing resources, and resource state parameters such as the computing unit utilization rate and the memory occupancy rate are obtained; task attributes and user request parameters of the task queue are collected, historical task data are processed based on the genetic algorithm optimization model to execute task demand prediction, and predicted demand parameters are generated. A dependency graph containing resource unit nodes and communication link roadsides is constructed through a resource topology analysis tool, predicted demand parameters are input into a scheduling priority classifier trained by a graph neural network, and an actual scheduling priority is identified. And executing resource conflict prediction based on the priority, inputting task feature vectors into a conflict resolution module of a fuzzy logic decision maker, outputting actual conflict resolution parameters, and finally integrating to generate a scheduling scheme containing a resource allocation sequence and an execution time table.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing and analysis method, device, equipment and medium, and the method comprises the steps: obtaining a data source set, and registering a source label and field mapping relation; executing extraction, cleaning, conversion and loading in the data processing assembly line based on the data source set, the source label and the field mapping relation to generate a processing table set; performing quality verification and exception handling on the processing table set to generate a verified processing table set; generating an index cloud based on the verified processing table set and the configuration strategy; the index cloud is pushed to the analysis tool set through the interface transmission channel, and the analysis tool set generates analysis output according to the index cloud. According to the method, through unified data registration, standardized processing and quality verification, the index cloud is generated in combination with the configuration strategy and pushed to the analysis tool set, the analysis result is generated and transmitted, the data consistency is ensured, and the real-time performance is improved.

The invention discloses a phishing document de-obfuscation and feature extraction method and an application thereof in attack detection. The de-obfuscation comprises the steps of obtaining an obfuscation macro code of a phishing document, constructing a prompt engineering template by utilizing a pre-training language model, analyzing an obfuscation logic structure and generating a de-obfuscation rule and a reduction strategy; the method comprises the following steps: structuring a confused macro code into an abstract syntax tree through an analysis tool, matching a typical confusion mode based on a regular expression, and performing simulation and cell reference analysis in combination with a function to realize structure preliminary reduction, control flow semantic reduction and operation path construction; according to the unmixing rule and the abstract syntax tree, the confusion structure is converted into a readable macro statement, a macro code instruction sequence with confusion semantics removed is generated, and a semantic sequence obtained after unmixing is output. The feature extraction comprises word feature extraction, Token feature extraction, abstract syntax tree feature extraction and relation feature extraction. According to the method, the bottleneck that traditional phishing attack detection and confusion documents are difficult to recognize is broken through, and the detection accuracy of phishing document attacks is improved.

The invention provides a method and system for improving the flutter derivative recognition precision of a large-span bridge, and relates to the technical field of road and bridge safety, and the method comprises the steps: obtaining basic data and section geometric parameters of a target bridge; according to the geometric parameters of the section, constructing a model by combining the bluff body streaming characteristics of the section of the bridge to obtain a virtual model; performing forced vibration simulation on the virtual model based on preset working condition parameters to obtain a simulated self-excitation force amplitude; obtaining a virtual model based on the simulated self-excitation force amplitude and the actually measured self-excitation force amplitude, and outputting a simulated phase difference; based on the simulation phase difference and the actually measured phase difference, phase compensation is carried out on the virtual model after force amplitude correction, and individual digital twin bodies are constructed and obtained; and performing global digital twinborn construction according to the individual digital twinborn to obtain a flutter derivative global prediction model. According to the method, the global prediction model is established, the flutter derivative recognition precision is remarkably improved, and an analysis tool considering reliability and high efficiency is provided for bridge wind resistance design.

The invention relates to the technical field of automatic testing, in particular to an automatic software testing method and system based on multi-mode AI cooperation. According to the scheme, optical character recognition, voice recognition and natural language processing analysis tools are packaged into independent containers, and analysis results are input into a multi-mode semantic association model in real time; calculating the semantic similarity between the image description text and the corresponding voice transcription text; constructing a test demand complexity evaluation model; defining use case quality indexes, and establishing an error sample library to store manually labeled problem use cases and correction labels thereof; the intention demand is finally determined according to the screening result of the three-stage filter; and setting a test case template, optimizing a path according to the fusion quality, improving the case generation efficiency, and synchronously updating the optimized path to a knowledge base associated with the error sample library. According to the scheme, automatic software testing is achieved through multi-modal AI cooperation in combination with containerized deployment, model dynamic optimization and the like, and efficiency and recognition accuracy are improved.

The invention provides a terrain element automatic extraction method and system based on deep learning, and relates to the technical field of deep learning. According to the method, a convolutional neural network of an encoder-decoder structure is constructed in an open source deep learning framework, initial training and verification are completed in combination with an image enhancement strategy, and an initial extraction model is formed; reasoning a remote sensing image by using the model, and outputting a topographic element vector result; comparing a reasoning result with reference data through an automatic quality analysis tool, positioning missing extraction, mistaken extraction and boundary deviation regions, and generating an error report; and constructing and supplementing positive and negative samples based on the error region, updating the training set and executing model retraining to form a final model. According to the method, a training-inference-feedback-optimization self-evolution closed-loop system is constructed, and the precision of terrain element extraction, the boundary integrity and the shielding adaptability are remarkably improved.

The invention provides a code analysis method, system and equipment based on a multi-programming language sandbox and a medium, and belongs to the technical field of code analysis and detection.The method specifically comprises the steps that an input code and a corresponding unit test sample are obtained, and a programming language type specified by the code is determined; sending the code and the unit test sample to a sub sandbox environment of a corresponding language for execution; a compiler in the sub sandbox reports a missing library according to a code compiling result and prompts a user to install the missing library; calling an analysis tool of a code analysis module to analyze the code; compiler feedback of the sub sandboxes and various analysis results generated by the code analysis module are integrated into a large language model; inputting the integration result into a large language model through a preset template; and analyzing and evaluating the code case based on the user instruction. Through multi-language sandbox isolation, automatic analysis tool integration and large language model enhancement, the security, efficiency and quality of a code processing flow are improved.

The invention relates to the technical field of video image processing, and discloses a video image super-resolution enhancement method based on a generative adversarial network. The method comprises the following steps: segmenting a low-resolution video sequence, and analyzing frame timestamp information to generate a dynamic time axis containing key frame nodes so as to reflect video time change characteristics; then, in combination with the time axis and an image feature extraction system, feature mapping is carried out on the low-resolution frames, and a preliminary high-resolution image sequence with feature information is obtained; enhancing the preliminary sequence by using a generative adversarial network containing a generator and a discriminator in combination with a context-aware optimization technology to generate an enhanced sequence; and a machine learning algorithm is introduced to identify and classify different image quality region characteristic modes, and an intelligent identification rule base is established, so that an enhanced sequence is optimized. And finally, in response to user interaction, adjusting display parameters to realize personalized display, integrating a multi-dimensional analysis tool, calculating details of a specific region, and presenting a result in a visual interface, so that the video image quality can be improved.

The invention discloses an early warning method and an early warning device for a purchaser system enclosing mark, and relates to the technical field of information data processing. The method comprises the steps of performing abnormal behavior risk identification according to current bidding information, and obtaining a behavior risk identification result and a behavior analysis score; according to the historical bidding information, performing historical bid winning risk identification by using a mathematical probability statistical method to obtain a historical risk identification result; based on an IF-IDF algorithm and an NLP style analysis tool, performing file analysis according to the to-be-analyzed bidding information to obtain a file risk identification result; based on an external network, acquiring associated company information of the to-be-analyzed bidding information, and constructing an associated company atlas; bidding company association risk identification is carried out according to the association company atlas, and an association company risk identification result is obtained; and issuing direct early warning information and the risk level of the fund-string bidding, and taking countermeasures according to the risk level of the fund-string bidding. The early warning method provided by the invention is high in efficiency and quick in response aiming at bid enclosing and bidding of the purchasing system.

The invention belongs to the field of bioinformatics, and relates to a protein post-translational modification prediction method based on multi-modal deep learning. The method comprises the following steps: firstly, performing multi-modal feature extraction by inputting a protein sequence and three-dimensional structure data to obtain a sequence feature vector and a structure feature vector; secondly, carrying out feature fusion by adopting a cross-modal attention mechanism and a self-adaptive gating network; then, combining the fusion features with the disease type information, and performing fine adjustment on the prediction probability through a disease specific coding network; then, using a multi-task learning framework to predict the locus probabilities of various protein post-translational modification types in parallel; finally, feature importance is calculated through a gradient back propagation technology, and a comprehensive report is output in combination with variation influence analysis. According to the method, high-precision and explainable protein post-translational modification prediction with disease perception capability is realized, and an important calculation and analysis tool is provided for revealing a disease molecular mechanism and finding accurate drug targets.

The invention discloses a bacterial selenoprotein online resource platform, an application method, a terminal and a medium, and relates to the technical field of biological medicine, the online resource platform is deployed on a server, the cloud server is a Ubuntu cloud server, and Nginx, Waitpress and Flask are configured; the server side is in butt joint with a background resource, and the background resource is in butt joint with the constructed bacterial selenoprotein database; the server is in butt joint with a user interface of the front end, the user interface designs a corresponding interface framework and an interaction function based on static resources hosted by Nginx, and the interaction function is used for realizing query, analysis and use of relevant information of the bacterial selenoprotein. According to the method, a convenient online access channel of an integrated database and a real-time data analysis tool are provided for users in related fields, and important support is provided for accurate annotation of selenoprotein genes in a bacterial genome plan.

The invention provides a transient stability analysis method for a grid-connected system. The method comprises the following steps: establishing a mathematical model of a heterogeneous new energy grid-connected system comprising a network construction type new energy unit, a network following type new energy unit, a synchronous generator and a receiving end power grid, and defining a state variable set of the system; solving a stable equilibrium point and an unstable equilibrium point of the system according to the operation condition of the system; and by taking the unstable equilibrium point as a starting point, calculating by adopting a numerical integration method or a numerical continuation method to obtain a stable manifold starting from the unstable equilibrium point. The method provided by the invention realizes global and accurate depiction of the transient stability domain, overcomes the defects that a traditional small signal linearization method can only analyze local stability and a traditional energy function method depends on an artificial construction function and is limited in applicability, fundamentally changes an analysis normal form of the transient stability domain, and improves the analysis accuracy of the transient stability domain. And a more effective analysis tool is provided for safe and stable operation of the high-permeability new energy grid-connected system.

The invention discloses a multi-omics data integration and classification method, system and device based on hierarchical attention, and is applied to the field of precise medical big data analysis. The method comprises the following steps: firstly, generating feature embedding and feature importance scores through a plurality of parallel feature-level attention modules; then, embedding and inputting all the characteristics of the omics into a unified omics-level attention module, and generating omics embedding and omics importance scores; and finally, a classification prediction task is executed based on omics embedding, and a classification result is output for disease classification. The invention completely abandons a traditional dependency graph convolutional network and an integration normal form of variants of the dependency graph convolutional network, and provides a universal hierarchical attention integration architecture. The framework supports classification tasks of any complex diseases, is not limited by omics data types and combination modes, not only is remarkably superior to a traditional integration normal form in classification performance, but also shows a unique negative generalization distance, and proves that the framework has excellent generalization ability. Meanwhile, features and omics importance scores automatically output by the model provide a powerful analysis tool for biomarker discovery and precise diagnosis and treatment of complex diseases.

The invention relates to the technical field of computational mechanics, in particular to a DDDA-based circle-circle contact interface non-occlusion sliding numerical simulation method, which comprises the following steps: constructing a non-occlusion joint model; establishing a circular unit-non-occlusion joint initial correlation; establishing a DDDA total balance equation; applying a non-occlusion contact constitutive to the associated circle unit contact; and solving the model to obtain the non-occlusion sliding process of the circle-circle contact interface. According to the invention, the non-occlusion sliding process of the circle-circle contact interface is realized, so that the sliding simulation result of the structural surface better conforms to the real mechanical response of the structural surface, and a more convincing numerical analysis tool is provided for the sliding process analysis of slope stability, the slip instability prediction of underground engineering surrounding rock along a weak surface and other key engineering problems. And the method has great engineering application value.

The invention relates to the technical field of computer data processing and mode recognition, and discloses an intelligent handwriting record analysis system supporting translation process traceability and capability assessment, which comprises the following steps: collecting multi-dimensional handwriting data generated when a user translates by using an intelligent pen; the Hidden Markov model decodes the handwriting into translation behavior primitive sequences such as smooth writing, hesitant pause and the like; automatically dividing semantic regions such as a main translation region and a draft region by a density clustering algorithm; performing space-time fusion attribution to generate a space-time attribution log for accurately recording behaviors, time and positions; and establishing a multi-dimensional capability index system containing translation efficiency, quality and strategies based on the log, and carrying out quantitative calculation and generating a visual user capability portrait. According to the method, the invisible translation cognition process is converted into traceable and quantifiable objective data, deep insight and comprehensive evaluation of the translation process are achieved, and a scientific and efficient analysis tool is provided for translation teaching and interpreter ability evaluation.

The invention discloses an intelligent operation and maintenance alarm causal relationship analysis method based on BERT. According to the method, the semantic similarity of core switch link interruption and access layer equipment offline is improved by 40% through semantic modeling upgrading and joint coding of equipment types and alarm contents, and compared with an independent coding scheme, the misjudgment rate is remarkably reduced. Through organic combination of deep semantic modeling and dynamic time sequence analysis, the crossing of the intelligent operation and maintenance alarm causal relationship from rule driving to data intelligent driving is realized, and an efficient analysis tool is provided for intelligent operation and maintenance of a complex system. According to the method, accurate causal modeling of complex system alarm data is realized through semantic similarity calculation and multi-dimensional weight dynamic fusion under time sequence constraint, and the method is widely applied to large-scale distributed system fault analysis and root cause positioning scenes in industries such as power systems, communication networks, internet platforms, cloud computing infrastructures and industrial Internet of Things.

The invention relates to the technical field of artificial intelligence, can be applied to the medical field and the financial field, and discloses a software unit testing method and device, computer equipment and a storage medium. The method comprises the steps that an original version and an updated version of a production code are received, and the code difference between the original version and the updated version is analyzed based on a large language model; identifying an affected target element; calling a code analysis tool to obtain a context definition corresponding to the target element; constructing a structured prompt in combination with the code difference and the context definition, and generating an initial version of the updated unit test based on the structured prompt; and performing a verification correction loop test on the initial version of the unit test until a preset termination condition is met or the maximum number of iterations is reached, and outputting a corresponding test result. The method can solve the problems that the context is not fully acquired, only can be repaired but cannot be enhanced, the accuracy is low due to lack of verification and correction of a closed loop, task processing is not specialized due to single architecture, and the like.

The invention provides an energy policy text structuring and correlation analysis method, and belongs to the technical field of policy analysis. The method comprises the following steps: collecting and preprocessing an energy policy text; performing semantic analysis and structured processing on the policy text based on a large language model, determining release time and semantic tags, and disassembling the policy text into structured semantic units; constructing a time sequence association relationship, a hyponymy logic association relationship and a semantic association relationship among policies according to the release time and the semantic unit; constructing an energy policy knowledge graph based on the association relationship and visually displaying the energy policy knowledge graph; and policy interpretation, trend tracking and cross-time and cross-region policy association comparative analysis are carried out by using the constructed knowledge graph. According to the method, intelligent processing and deep mining of the policy text are realized through structured processing and multi-dimensional correlation analysis of the energy policy text, and a systematic policy analysis tool is provided for policy formulators and researchers.

The invention relates to an equipment main circuit parameter global optimization method based on a large model iterator, and belongs to the field of power electronic equipment design, and the method comprises the steps: generating an initial parameter set through a parameter generation LLM; performing feasibility grouping based on the feasibility flag bit; performing dominating relation judgment on the data in the feasible solution pool; carrying out frontier stability judgment; and the parameter optimization LLM executes a decision according to the termination flag bit and the maximum number of iterations to obtain a final main circuit parameter set. According to the method, systematic optimization meeting physical constraints can be realized through cooperation of multiple external tools when the calculation capability of a large model is limited. The method comprises the following steps: constructing a closed-loop feedback iteration mechanism, performing strict physical boundary verification on parameters by using a steady-state analysis tool, and grouping data according to Pareto dominating logic; and meanwhile, leading-edge stability judgment and differential optimization strategies are introduced, so that the physical feasibility of the design scheme is ensured, and global optimal design considering technical performance and economic cost is realized.

The invention belongs to the technical field of computer information security, and particularly relates to a log auditing technology based on multi-agent cooperation and an implementation framework thereof, which comprises the following steps that: a task decomposition agent performs structured modeling and logic slicing on original log data by utilizing a thinking chain reasoning mechanism to obtain a log auditing result; a complex internal threat detection task is decomposed into a plurality of subtasks with clear logic and independent semantics, and each subtask corresponds to a specific log analysis problem; and the tool generation agent automatically generates a reusable exclusive analysis tool by adopting an example-based programming normal form based on the semantic target and the input structure of each subtask. By constructing a three-layer collaborative architecture of a task decomposition agent, a tool generation agent and a task execution agent, the context window limitation of a single language model during processing of an ultra-long log sequence is effectively broken through, and structured and modular analysis of complex internal threat behaviors is realized.

The invention provides a test data processing method and device, equipment and a storage medium, chip test data in a test file is read according to a preset frequency, and the latest read part of chip test data is refreshed and displayed on a data display interface, so that a user can monitor a test flow in real time in a chip test process, and moreover, the test efficiency is improved. A virtual batching function is provided, statistical analysis can be performed on obtained chip test data according to a preset statistical dimension at any time in a chip test process, statistical analysis data can be displayed, a sub-test file which can be analyzed by a test analysis tool is generated based on the statistical analysis data, and an analysis report is generated by utilizing the test analysis tool. A user can determine the test condition of each test item or each to-be-tested chip in the chip test process without waiting for the end of a chip test project, and abnormity can be found in time.

The invention relates to a uranium migration prediction model based on particle swarm optimization and application of the uranium migration prediction model in uranium migration prediction in granite fractures. According to the method, by introducing a retention coefficient and an exponential decay item coupled with pH sensitivity, solute retention behaviors of a fissure domain and a matrix domain are dynamically corrected, and accurate description of adsorption / desorption, precipitation and re-release processes of U (VI) under an acid-alkali condition is realized. Parameter inversion is performed on the model by combining a particle swarm optimization (PSO) algorithm, so that the calculation efficiency and the prediction precision are remarkably improved. Experimental verification shows that the model has high stability and reliability (SSE is smaller than or equal to 0.026, RMSE is smaller than or equal to 0.045, and Error is smaller than or equal to 2.82%) under various pH, pressure and temperature conditions, and an efficient and interpretable analysis tool is provided for uranium migration risk assessment in the deep geological disposal environment.

The invention discloses an LLM reasoning acceleration method and device based on RISC-V vector extension, and belongs to the field of large language model design. The objective of the invention is to solve the problem of performance bottleneck in a large-scale language model (LLM) reasoning process, especially deployment on edge computing equipment. The method comprises the following steps: analyzing an inference process through a performance analysis tool, identifying bottleneck operators, and reconstructing the bottleneck operators by using an RISC-V vector extension RVV instruction set to improve the calculation efficiency. And the reconstructed operator can realize parallel calculation of SIMD, and the utilization of hardware resources is maximized by dynamically adjusting the length of a vector register. The method is further based on a dynamic vector length adaptation mechanism of RVV, it is ensured that the optimized operator can be automatically adapted according to different hardware platforms, and therefore efficient migration and execution of the reasoning task among multiple platforms are achieved, and the real-time performance of LLM reasoning and the system efficiency are effectively improved.

The invention discloses a chaos test scene supplementing method and device, equipment and a storage medium. The method comprises the steps that a software system is analyzed through a micro-service analysis tool, and a chaos test scene corresponding to the software system is generated in combination with a rule engine tool; executing the chaos test scene on a chaos engineering platform, and obtaining scene data when the chaos test scene is executed in combination with a code coverage rate tool; and when the current code coverage rate of the software system does not reach the expectation, inputting the scene data into a pre-trained machine learning model, and supplementing a new chaos test scene based on the machine learning model. According to the method, when the current code coverage rate of the software system does not reach the expectation, a new chaos test scene is supplemented through the machine learning model, and the code coverage rate of the chaos test scene can be improved.

The invention relates to a kernel hot patch symbol table generation method and device, equipment, a storage medium and a program product. The method comprises the following steps: in a kernel hot patch compiling stage, collecting a compiling log through a pipeline, storing the compiling log as a log file, outputting a hot patch module, and extracting and storing a modified kernel module name at the same time; reading a segment for storing patch function information in the hot patch module, acquiring the byte length of the segment by means of a binary file analysis tool, checking whether the byte length is an integral multiple of 72 or not, if not, terminating the process, if so, scanning the data of the segment line by line, extracting kernel symbol position information and a function address according to a remainder rule of modulo 9 by a line number, and storing the kernel symbol position information and the function address in the hot patch module; querying a function name matched with the function address, comparing the function name with a to-be-patched function name in a log, screening a matching item, and forming a scanning result; and writing a result into a file, and generating a kernel hot patch symbol table. By adopting the method, whether the current hot patch is suitable for loading or not can be conveniently detected in advance during operation and maintenance deployment, and the corresponding preposed environment is prepared.

The invention discloses a system and a method for automatically assembling a data analysis process by a large language model based on Faiss, and belongs to the technical field of data analysis and artificial intelligence. The system comprises a client, a server and a data analysis platform. The method comprises: receiving a natural language analysis demand of a user; identifying a user intention as process assembly or suggestion through a large language model; encoding the demand into a query vector, and performing similarity retrieval by using a Faiiss vector library pre-constructed based on the analysis tool description information to obtain a first candidate tool set; inputting the set and original requirements into a large language model for fine arrangement and logic assembly to generate a second candidate tool set; and finally converting into a structured process file which can be directly identified and executed by a data analysis platform. According to the method, the problem that a structured data analysis process cannot be automatically constructed in the prior art is solved, end-to-end intelligent generation from natural language description to an executable analysis scheme is realized, the use threshold is remarkably reduced, and the analysis efficiency is improved.

Provided in the present application are an incremental analysis method and apparatus for codes, and a related product, which can improve the accuracy of the incremental analysis of codes. The method can be applied to the incremental analysis apparatus. Specifically, the incremental analysis apparatus acquires an incremental analysis request, wherein the request is used for instructing the execution of incremental analysis on a second source code on the basis of a first source code, and the second source code is obtained by means of updating the first source code. Subsequently, the incremental analysis apparatus determines factors that affect the incremental analysis result, and executes incremental analysis on the second source code on the basis of the determined factors, wherein the factors that affect the incremental analysis result comprise at least one of changes in source codes, a change in a compilation command of the source codes, and a change in the configuration of a code analysis tool used for executing incremental analysis.

The invention discloses an endoscope image intelligent decision-making reflection agent system based on memory guidance. The system comprises an input module; the initialization module is used for establishing an initial multi-modal context; the action execution module is used for selecting an optimal tool from the expert tool set for calling; an expert tool set; a short-term memory module; the self-reflection module is used for forming a reflection feedback result; a long-term memory module; the updating module is used for updating the multi-modal context based on the output and reflection feedback result of the special analysis tool; and the evaluation module is used for judging whether the task is completed or not, and outputting a final analysis result if a termination condition is met. According to the method, traceability of an inference chain and avoidance of redundant operation are realized through short-term memory, cross-task experience accumulation and strategy optimization are realized through long-term memory, and a systematic collaborative inference process is formed by combining various endoscope analysis tools, so that analysis consistency, generalization ability and reliability in a complex scene are improved.

The invention provides a MySQL database rapid recovery method based on a Bin log, and relates to the technical field of database management and data recovery, and the method comprises the following steps: screening and extracting log content in a corresponding time period from the Bin log of a MySQL database according to a time interval of occurrence of misoperation specified by a user; the extracted log content is converted into a text format through an analysis tool, and the log content recorded in the ROW format is selected as a processing object; processing a DELETE operation record in the log content, converting a DELETE statement into an INSERT statement, and generating an SQL script used for recovering the deleted data; processing the UPDATE operation record in the log content, adjusting the sequence of a WHERE clause and an SET clause of a UPDATE statement, and generating an SQL script used for recovering updated data; and importing the generated SQL script for recovering the deleted data and the SQL script for recovering the updated data into a target database, and executing the scripts through a database client tool to complete data recovery operation.

The invention discloses a multi-source financial data management system based on an enterprise portrait. The system comprises a data acquisition module, a data processing module, a weight calculation module and a risk early warning module. The data acquisition module constructs a multi-source data source metadata registry and deploys a dynamic protocol analyzer; the data processing module captures incremental data by adopting a log analysis tool, cleans the incremental data through a lightweight rule engine, and dynamically divides hot data and warm data according to the average access times of a core active window; the weight calculation module sets an initial weight in combination with an expert knowledge base, an industry reference and a time decay factor, and introduces an LSTM model prediction result for dynamic correction; the risk early warning module sets a threshold value according to label historical data distribution characteristics, calculates a risk score and divides three levels of risk intervals, thereby realizing graded early warning response. According to the system, the problems of poor multi-source financial data access adaptability, weight staticization and inaccurate early warning are solved, and the data processing efficiency and the risk management and control capability are improved.