3577 results about "Automatic processing" patented technology

The invention discloses a network equipment integrated monitoring method and system. The method comprises the following steps: collecting multi-source heterogeneous data, constructing a protocol compatible layer, and supporting multi-protocol adaptation; data fusion and intelligent analysis: constructing a dynamic topology, analyzing an equipment configuration file, and generating a network topological graph; performing time sequence prediction according to a root cause analysis model, and predicting an abnormal trend; mining association rules, analyzing historical data, and extracting fault association rules; constructing an equipment fault knowledge base under the assistance of a knowledge graph, and accelerating root cause positioning; self-adapting an alarm threshold, analyzing historical data distribution, and dynamically adjusting the threshold; visual decision making and automatic processing are carried out, a 3D topological map is provided, and layered display is supported; and performing fault grading processing, comprehensively calculating a fault influence degree score, mapping to a fault grade and a work order type according to an influence degree score interval, and formulating a dynamic work order generation rule. A protocol compatible layer is constructed by deploying a lightweight agent program, multi-protocol adaptation is supported, and various network devices can be fully covered.

The invention provides a complex task full-automatic processing method based on multi-agent cooperation and a related device, and relates to the technical field of artificial intelligence such as large language models, generative models, agents and task intelligent scheduling. The method comprises the following steps: interacting with a target user who puts forward an original task demand to obtain a complete task demand; the complete task demand is split into a plurality of sub-tasks at least comprising a complex sub-task, the complex sub-task refers to a sub-task needing at least two sub-agents to process according to a cooperation process, and a single sub-agent is used for processing a simple sub-task; issuing the complex sub-tasks to corresponding target execution intelligent units, and issuing the simple sub-tasks to corresponding target sub-agents; and summarizing sub-task execution results returned by each target execution intelligent unit and each target sub-agent. According to the method, the execution intelligent unit specially used for processing the complex sub-task is introduced, and the complex sub-task is processed more intensively according to the cooperation process through the at least two sub-agents contained in the execution intelligent unit, so that the task disassembling difficulty is reduced; and a better sub-task processing result can be obtained through the execution intelligent unit integrated by the multiple sub-agents.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses a multi-modal event data processing and collaborative circulation method, device, equipment and medium. Generating a stereoscopic scene model based on the event data file and positioning a target area, performing multi-modal fusion by combining a positioning result and non-spatial data of the event data file to generate a fusion analysis result and form a processing report, and constructing an auditing and judging item set containing a conditional strategy and an execution action to execute auditing and generate an auditing and judging result. And generating a collaborative circulation instruction set based on the processing report and the audit judgment result, and triggering execution through distributed message transmission to obtain an execution result. According to the method, through multi-modal data processing, three-dimensional scene modeling, cross-modal fusion analysis, rule-driven auditing and distributed execution, full-process automatic processing is realized, and the processing accuracy and efficiency are improved.

The invention discloses a government affair intelligent interaction and information extraction method and device, equipment and a medium, and relates to the technical field of digital government affair, digital cities, intelligent interaction and the like. The traditional manual item-by-item filling is converted into end-to-end automatic processing, so that the operation steps of a user are remarkably reduced, the filling burden of a hundred-item-level form is solved, and the filling efficiency is improved; and an ID voice corrector is adopted to realize three-layer progressive verification, so that the output ID offset of the multi-modal large model is effectively inhibited, the accurate mapping of field identifiers is ensured, and meanwhile, a lightweight knowledge graph is adopted to automatically capture logic rules among fields, interspecific compliance conflicts of government affair scenes are intercepted in real time, the logic error risk is reduced, and the filling accuracy is improved.

The invention discloses a multi-agent computer-aided engineering and a computer-aided design method. CAE simulation and design optimization for the design of a complex product are high in threshold and difficulty. According to the method, autonomous planning and automatic execution of CAE simulation and CAD design optimization tasks are realized by using a multi-agent technology based on a large language model. A user interacts with the multi-agent system in the modes of text input, voice, a graphical user interface and the like, the multi-agent system autonomously plans workflow steps according to the requirements of the user, all agents cooperatively execute the working steps in a labor division mode, CAE and CAD software is called to execute simulation and design change, CAE and CAD result data are read and analyzed, the task completion condition is evaluated, and the task completion condition is evaluated. And the task can be re-planned if necessary. The system can automatically process error information, and can notify a user to process when user intervention is needed. The performance of the system can be improved through fine tuning of a large language model and retrieval enhancement generation technology.

The invention relates to the technical field of financial bill management, and discloses a financial bill automatic identification generation and decision-making method and system, and the method comprises the steps: firstly receiving an original bill image, carrying out the preprocessing of the original bill image, carrying out the format analysis and classification of the enhanced image through a computer vision technology, and distinguishing a known standard format from an unknown format. For a known format, accurately extracting a key region text block by applying an OCR module based on template positioning; and for an unknown format, a general document understanding model is adopted to obtain a preliminary key information key value pair. And then, the extracted information is converted into structured data through a key information structured extraction module, and the structured data is verified by a data verification and business rule engine. If the verification is passed, structured bill information is output; otherwise, sending to a manual intervention queue for further auditing. According to the method, the efficiency and accuracy of financial bill automatic processing are effectively improved, and meanwhile, the flexibility of the system for adapting to different bill formats is enhanced.

The invention relates to the technical field of surveying and mapping information processing, and discloses a surveying and mapping information intelligent analysis and visualization system based on the Internet of Things. According to the system, a building space data acquisition terminal acquires geometric feature point cloud and environmental parameters of the surface of a building structure in real time through distributed Internet of Things nodes; the multi-source heterogeneous data fusion engine performs space-time alignment processing on the geometric feature point cloud to generate a building total-factor topological skeleton with an absolute coordinate system; the three-dimensional topological relation reconstruction module is used for extracting a spatial constraint relation of building components according to the topological skeleton and establishing a hierarchical building information model capable of being edited in a parameterized manner; and the dynamic visual rendering platform is used for generating a multi-scale spatial index based on the hierarchical building information model, and driving real-time light and shadow effects and interactive sectioning analysis. According to the system, full-process automatic processing and dynamic visualization of building surveying and mapping data are realized.

The invention discloses a multi-modal data automatic processing and information extraction method, and belongs to the technical field of data processing. Comprising the steps of establishing an original knowledge base according to original training data in a business scene; preprocessing the original multi-modal data in the original knowledge base to obtain a preprocessed knowledge base; inputting the preprocessing knowledge base into a knowledge retrieval unit; a text to be queried is converted into a query vector, the distance between the query vector and a knowledge fusion vector representation vector in the knowledge retrieval unit is calculated, and a retrieval result is obtained through an approximate nearest neighbor retrieval algorithm; and performing multi-modal data fusion on a retrieval result through a cross-modal Transform model, and combining fused semantics with user query to generate an answer. According to the method, data of multiple modes such as texts, images, audios and videos can be processed, multi-mode fusion and reasoning are carried out through the visual language model, accurate extraction and structured storage of information are achieved, and the efficiency and quality of data analysis and mining are improved.

The invention provides a non-technician-oriented script intelligent adaptation and visual arrangement method and a non-technician-oriented script intelligent adaptation and visual arrangement system. The method comprises the following steps: acquiring a script file uploaded by a user, extracting a dependency library through an AST analysis technology, and constructing an isolation container environment; performing intelligent identification and conversion on a file path and database connection in the script to generate a standardized API call; carrying out input and output analysis on the basis of the converted script, and carrying out DAG workflow arrangement in a visual mode; performing function-level packaging and standardization processing on the script component in the workflow to realize component multiplexing and version management; and finally, configuring a scheduling strategy and carrying out automatic operation monitoring. According to the method, a script environment depends on automatic processing and intelligent path adaptation, and visual arrangement and modular multiplexing are combined, so that non-technical personnel can independently complete the whole process from AI script generation to a production-level automatic process, the problem of'last kilometer 'in script deployment is solved, and the working efficiency is improved.

The invention discloses a cross-border e-commerce anti-counterfeiting tracing method and system based on a block chain, and belongs to the technical field of computer application. The method comprises the steps of obtaining full-link information of commodity production, storage, logistics and sales links, performing encryption processing through a composite hash function, storing the information to a block chain by using an improved consensus mechanism, constructing an intelligent contract to realize automatic processing of commodity state changes, and scanning a code by a consumer to compare a hash value to verify authenticity. The system comprises an information acquisition module, a hash encryption module, a consensus storage module, an intelligent contract module and a verification module. According to the scheme, it is guaranteed that data cannot be tampered through the composite hash function, the consensus efficiency is improved through the hierarchical verification structure and the dynamic node weight, state monitoring and automatic claim settlement are achieved with the help of the intelligent contract, the problems of data fragmentation, difficult anti-counterfeiting, poor supervision cooperation and the like in cross-border e-commerce are solved, full-link credible evidence storage and automatic supervision are achieved, and the safety of cross-border e-commerce is improved. And the cross-border commodity anti-counterfeiting and traceability efficiency is improved.

The invention discloses a hydraulic engineering dam body crack detection method and system based on machine vision, and relates to the technical field of computer vision. A high-definition camera is used for shooting a dam body image, obtaining a sample data set, extracting feature parameters of different noise types, and constructing a feature database; filtering the current dam body image to obtain a first dam body image, enhancing the crack gray scale difference of the first dam body image, obtaining a second dam body image, segmenting the second dam body image, screening candidate cracks in the crack image, and identifying real cracks according to the edge features of the candidate cracks. According to the method, a feature database is constructed, a targeted dam body image processing flow is combined, a high-definition camera collection and automatic processing flow is used, filtering parameters are dynamically adjusted by constructing the feature database, gray level enhancement and precise segmentation are combined, real cracks are precisely recognized through edge feature analysis and comparison, and quantitative parameters are output; and a reliable basis is provided for dam body safety assessment.

The invention relates to a large model business logic processing method and system based on workflow engine and domain knowledge fusion, and is suitable for automatic processing of complex multi-node and multi-branch business processes. According to the method, natural language input of a user is analyzed through a large language model, a service intention is recognized, and the service intention is converted into an executable task process through a workflow engine. And the workflow engine dynamically adjusts an execution path according to rules and data in the domain knowledge graph to realize efficient parallel processing of tasks. The system integrates a workflow engine, a domain knowledge graph and a large language model, supports real-time data processing, rule matching, conflict resolution and decision optimization, is suitable for the fields of water conservancy, medical treatment, finance and the like, automatically generates and executes a complex business process, and optimizes a decision process. The system improves the accuracy and execution efficiency of business decision through deep fusion of domain knowledge.

The invention relates to the technical field of equipment abnormity control, and discloses an equipment abnormity self-closed-loop control system. An anomaly detection engine of the system collects operation state data of target equipment in real time, and performs preliminary anomaly marking according to a preset anomaly judgment rule; the dynamic analysis unit receives the preliminary anomaly marks, generates anomaly feature vectors in combination with historical operation data, and inputs the anomaly feature vectors into a multi-dimensional evaluation model to divide anomaly levels; a strategy generator is matched with a preset repair strategy library according to the abnormal grade division result, and a corresponding repair instruction sequence is generated; the execution control module analyzes the repair instruction sequence, and dispatches hardware resources to execute repair operation according to a preset execution priority; and the feedback verification unit collects the running state data again after restoration, and compares the running state data with the initial abnormal mark to generate a restoration verification result. The system realizes full-process automatic processing of equipment exceptions, and improves exception processing efficiency and equipment operation stability.

The invention provides an infrared thermal imaging image processing method for a pain area, and relates to the field of image enhancement, and the method combines multi-scale adaptive contrast enhancement and a bidirectional circulation mechanism, captures global and local temperature features of an infrared image, constructs an iterative feature enhancement module through an incremental fusion strategy, and carries out the image enhancement through the iterative feature enhancement module. Different level features are fused in stages, learnable parameters are introduced to dynamically adjust the new and old feature fusion proportion, the adaptability and flexibility of the model are improved, and a staged training strategy is adopted, so that the model can learn low-level thermal image information and also can extract high-level structures and thermal anomaly semantic features. According to the method, the adaptability of the model to different types of focus areas is enhanced, and the actual demand of efficient and automatic processing of the infrared image is met.

The invention discloses an unknown pollutant non-target identification method and system based on mass spectrum and a generative model, and is applied to the technical field of environmental monitoring and analytical chemistry. The method comprises the following steps: collecting mass spectrum data of a water body sample to be detected; preprocessing the original mass spectrum data, and outputting standardized features; inputting the standardized features into a pre-trained generative model to generate a plurality of candidate molecular formulas; using chemical and physical constraints to eliminate candidate molecular formulas which do not conform to rules; executing a rule-driven algorithm to obtain candidate pollutant molecular structures; carrying out comprehensive scoring and sorting on candidate pollutant molecular structures through chemical prior and environmental prior; and semi-quantitative or relatively quantitative concentration determination is carried out. According to the method, a rule-driven expert system and a data-driven generation model are combined, unknown pollutants which do not exist in a standard library can be effectively recognized and analyzed, and full-process automatic processing from original mass spectrum data to pollutant structures and concentrations is achieved.

The invention relates to the technical field of human resource management, and provides a one-stop talent screening method and system. The method comprises the steps of performing text analysis on a job seeker resume, identifying structured information in the job seeker resume, performing weight distribution on the analyzed information through a preset post competency model, and generating a resume analysis result containing a candidate and target post matching degree score; consultation information about enterprise information, position treatment and office environment of the job seeker is obtained, the consultation information is analyzed to obtain a question intention of the user, and response content corresponding to the question intention is generated; performing semantic analysis on the communication record corresponding to the job seeker, and identifying the job hunting willingness level of the job seeker; and dynamically generating personalized interview questions through an interview question generation model in combination with the matching degree score and the job hunting willingness level to complete talent screening. The method is suitable for full-process automatic processing of resume screening, intelligent communication and adaptive interview in the enterprise recruitment process.

The invention discloses a clinical test document quality detection and processing method and system and computer equipment. The method comprises the following steps: acquiring a to-be-processed clinical test document; performing format conversion and optimization processing on the to-be-processed clinical test document to obtain a preprocessing result; automatically classifying the preprocessing result to obtain a classification result; extracting document key information according to the classification result, and generating a file name to obtain an extraction result; checking the integrity, compliance and archivability of the corresponding document according to the extraction result, and performing automatic processing according to the detection result to obtain an automatic processing result; and storing and archiving the automatic processing result. By implementing the method provided by the invention, efficient and accurate document quality detection and automatic processing can be realized, so that the document processing quality and efficiency are improved, and the problems of low efficiency, poor consistency and high error rate in the prior art are solved.

The invention discloses a remote sensing image segmentation method and system based on a high-similarity transmission attention mechanism. The method comprises the steps that a high-similarity transmission attention feature extraction network is constructed, multi-scale local features are extracted through preprocessing and a local exploration block, details are reserved in combination with jump connection, and high-similarity features are screened through an attention module; the features are fused with original features to obtain enhanced features; performing multi-scale up-sampling on the enhanced features to recover spatial information, optimizing details by combining coding features, and realizing model training by combining cross entropy and Dice loss; and inputting a target remote sensing image needing to be processed into the optimized model, outputting a segmentation mask image with the same size as the target input remote sensing image, and marking regions of different surface feature categories. According to the invention, the segmentation precision in a complex scene can be obviously improved; the generalization ability of the model is enhanced; the method provides powerful technical support for automatic processing of remote sensing images, and is suitable for practical applications such as urban planning and environment monitoring.

The invention relates to the technical field of data integration, in particular to a multi-source data integration system. According to the technical scheme, the system comprises a plurality of heterogeneous data acquisition devices, a data processing module, a data storage module and a data transmission module. Differentiation processing is carried out on data of different devices, accurate time synchronization and data alignment are achieved through an NTP protocol and dynamic calibration, intelligent noise processing and data cleaning are carried out through multiple algorithms, compression encryption is adopted in storage, and a double-queue priority and breakpoint resume mechanism is applied in transmission. And fault monitoring and automatic processing are realized by means of hierarchical log recording and intelligent analysis, so that the problems of data isomerism, time synchronization, noise interference, storage and transmission efficiency, system reliability and the like are efficiently solved, and efficient, stable and safe data support is provided for a real-time monitoring platform.

The invention provides an automatic processing method for real-time observation data of an ocean station, and belongs to the technical field of ocean observation data processing. A wavelet packet decomposition multi-scale noise separation algorithm is established to distinguish environmental noise and real signals, dual-sensor redundancy configuration is combined with Bayesian inference to identify sensor drift, and a calibration coefficient is updated in real time through a recursive least square method. A one-dimensional time sequence is mapped to a high-dimensional phase space by utilizing a phase space reconstruction algorithm to realize high-precision prediction of a chaotic signal, a hierarchical data storage architecture is established, and a data migration strategy is iteratively optimized through a hierarchical correlation degree function; the technical problem that time synchronization signals are difficult to reconstruct accurately according to asynchronous sampling data of multiple sensors of an ocean station is solved.

The invention relates to the technical field of building structure health monitoring, in particular to an unmanned aerial vehicle laser radar-based building point cloud deformation quantitative extraction method, which comprises the following steps of: based on building corner structure characteristics in point cloud data, carrying out quantitative evaluation on the overall inclination state of a building through point cloud segmentation, straight line fitting and angle calculation; based on an ideal plane hypothesis, performing plane fitting and point-to-plane distance analysis on the building facade point cloud in the point cloud data, identifying a local concave-convex deformation area of the building facade, and performing quantitative evaluation; and roof plane detection and wall surface normal vector analysis are carried out based on the point cloud data, and accurate quantification is carried out on parapet wall inclination based on region growth segmentation and PCA principal direction analysis. Through automatic processing and quantitative analysis of an algorithm, subjective errors and inconsistency possibly occurring in a traditional manual evaluation method are avoided, and objectivity and repeatability of an evaluation result are enhanced.

The invention relates to the field of finance, and discloses an AI-based investment project feasibility intelligent analysis and decision support system, which comprises a multi-modal data fusion engine used for extracting a feature vector of an unstructured text through a BERT variant model, carrying out joint coding on the feature vector and a structured data feature, and outputting fused multi-modal data; the dynamic knowledge graph construction module is connected with the multi-modal data fusion engine; the deep reinforcement learning decision module comprises a feasibility prediction network and a risk assessment network which are cooperatively trained; an interpretable AI module; and a closed loop feedback module. By means of a multi-modal data fusion engine and a deep reinforcement learning decision module, automatic processing and intelligent analysis of data are achieved, and the analysis time of a single project is greatly shortened to 8 minutes. The high-efficiency processing speed enables the investment institution to quickly respond to the market change, timely grasp the investment opportunity, and occupy the decision-making precedence in the financial market which changes instantaneously.

The invention relates to the technical field of intelligent aquaculture and automatic processing, in particular to an appearance characteristic-based hippocampus intelligent grading and pond-dividing culture method and system, and the method comprises the following steps: S1, collecting sparse visual data and multi-source physiological data of hippocampus; s2, generating a three-dimensional digital twinborn model based on the sparse visual data; calculating a growth state grading index based on the three-dimensional digital twinborn model; s3, calculating a physiological stress index based on the multi-source physiological data; s4, comparing the physiological stress index calculated in real time with a preset safety stress threshold value and a critical stress threshold value, and generating a hierarchical adaptive correction strategy; s5, according to a grading self-adaptive correction strategy, controlling a flexible mechanical arm to execute fishing and pond separation operation; according to the method, the appearance and physiological data of the hippocampus are synchronously collected in a non-contact mode, the technical problems that the real condition of the hippocampus cannot be evaluated and damage is possibly caused due to target stress response caused by traditional physical intervention are solved, and the authenticity and effectiveness of original data are guaranteed.

The invention provides a business rule automatic processing method and system based on natural language understanding. The method comprises the following steps: extracting text content from an original document containing business rules and preprocessing the text content; utilizing a BERT-based metadata extraction model to extract structured metadata from the preprocessed text content; confirming and perfecting the structured metadata; on the basis of the confirmed metadata, a rule execution flow chart and / or a test case are / is automatically generated and used for verifying the correctness of rule logic; and according to the target format, converting the verified rule into a corresponding symbolic representation through a template engine to obtain a defined rule. According to the invention, the automatic conversion of the business rule from the natural language description to the symbolized executable form is realized, so that business personnel can directly use the natural language to define and maintain the business rule, the manual conversion cost is reduced, and the accuracy and efficiency of rule processing are improved.

The invention discloses an integrated machining device suitable for a large marine propeller, which comprises a machining adjusting assembly, the machining adjusting assembly is composed of an X-axis moving mechanism, a Y-axis moving mechanism and a Z-axis moving mechanism, one side of the Z-axis moving mechanism is fixedly provided with a rotating mechanism, one end of the rotating mechanism is fixedly connected with a cutting assembly, and the other end of the rotating mechanism is fixedly connected with a cutter; according to the large marine propeller integrated machining device with the automatic machining function and the self-adaptive clamping function, through the integrated multi-axis linkage machining adjusting assembly and the three-dimensional scanner, automatic path planning can be conducted, and automatic operation is achieved; therefore, the dependence on advanced technicians is greatly reduced, the machining efficiency and the product consistency are remarkably improved, and meanwhile, through the collaborative design of the rotatable three-jaw chuck and the center supporting mechanism, simultaneous reliable supporting from the interior and the exterior of the workpiece can be automatically achieved during clamping.

The invention discloses a method for dynamically monitoring and automatically processing abnormity of an entrance and an exit of a parking lot, relates to the technical field of intelligent parking lot management, and aims to solve the problems that existing unattended parking lot entrance and exit management depends on manpower, abnormity processing is passive, and the misjudgment rate is high. The method is based on an information acquisition and execution layer-edge processing layer-cloud platform three-layer architecture, the core realizes high-precision scene understanding through a multi-algorithm fused visual perception module, multi-modal data weighted fusion is combined with a ground induction coil, a radar and the like, and the misjudgment rate is reduced; a sensing result is converted into an automatic processing action through a configurable intelligent rule engine; and meanwhile, the cloud platform stores passing data and an evidence chain, optimizes an AI model through incremental training and returns the AI model to an edge layer, so that closed-loop evolution is realized.

The invention discloses an automatic processing device and method for PCB lamination, and the method comprises the steps: depositing an erodable film on a lamination material layer and arranging a cavity concentric with a through hole array before the lamination of a PCB, and collecting acoustic resonance frequency data to obtain a reference matrix; dynamic dot frequency tracking processing is carried out on the multi-point acoustic time sequence signals in the pressing heating stage, and a through hole instantaneous acoustic track library is obtained; performing coupling judgment on time-temperature-pressure ternary working condition data according to the trajectory library to obtain a blockage risk matrix; carrying out time delay and gradient adjustment on a pressing temperature and pressure curve based on the risk matrix; and carrying out three-dimensional inversion processing on the whole board broadband acoustic spectrum collected before cooling, calculating through hole filling rate distribution and judging the quality of the board. According to the scheme, the resin filling state of the micro through hole can be monitored in real time in the PCB pressing process, an acoustic detection channel is constructed through the erodable film and the cavity structure, early warning and active intervention of the blocking risk are achieved, and the pressing quality is ensured.

The invention discloses a fault co-seismic sliding surface inversion method and system based on Beidou and artificial intelligence. The method comprises the steps of collecting Beidou data and seismic waveform data of a target area in real time, and performing preprocessing to extract co-seismic displacement and waveform features; fusing the Beidou deformation features and the seismic wave features to form a multi-modal feature vector; a fault prediction model is constructed based on a graph neural network, forward modeling and historical earthquake example data training are utilized, and multi-modal features are input to obtain preliminary fault sliding distribution prediction; rapid optimization under physical constraint is carried out through the elastic dislocation model, and a final fault sliding model conforming to the geophysical law is obtained; finally, uncertainty quantification is carried out, and a visual product of the dynamic process including sliding distribution, seismic moments and fault deformation is generated. According to the system, full-process automatic processing is achieved through cooperation of all the units, the inversion speed, precision and physical credibility are improved, and reliable support is provided for earthquake emergency response and disaster assessment.

The invention relates to a constraint condition and multi-objective optimization fused investment attraction target enterprise screening method. The method comprises the steps of obtaining target industry field information and generating an initial target enterprise pool; obtaining regional resource constraint information and enterprise multi-dimensional information, and inputting the information into a constraint discrimination model to obtain a candidate enterprise set; inputting enterprise multi-dimensional information associated with the candidate enterprise set into the tendency evaluation model to obtain an enterprise comprehensive score set; obtaining optimization target information, and constructing a multi-target optimization function; obtaining optimization condition information, matching a corresponding multi-objective optimization algorithm and adjusting parameters; solving the multi-objective optimization function, generating an enterprise screening strategy scheme set, and generating an enterprise screening decision report; in conclusion, by fusing the regional resource constraint condition and the multi-target optimization mechanism, the automatic processing and objective evaluation of the enterprise screening process are realized, and the method has the advantage of improving the efficiency and objectivity of screening the investment attracting target enterprises.

The invention relates to the technical field of automatic fault processing, and discloses an automatic fault processing method based on an AI large model. Comprising the steps that the running state of IT resources is obtained, important fault problems are recognized through an AI large model, the fault problems are combined, and meanwhile alarm information is pushed; a historical knowledge base is retrieved by using an RAG technology, a fault problem is preliminarily analyzed, and a suggestion and a solution are generated by using a large-scale LLM; a solution capable of being automatically executed is confirmed, and the fault processing process is automatically executed through the AI large model; generating a fault report according to the fault processing process, automatically filing the fault report, and learning the fault report through an AI large model; the method has the advantages that faults can be automatically processed, and fault solutions can be archived in time and accurately called at any time.