39 results about "Preliminary analysis" patented technology

The invention provides an enterprise intelligent diagnosis method, system and device based on a large model and a medium, and belongs to the technical field of enterprise diagnos.The method comprises the steps that enterprise heterogeneous data are collected through a multi-source data interface, cleaning, feature extraction and cross-modal alignment fusion are carried out, and enterprise real-time data are obtained; constructing a knowledge graph through a graph attention network on the basis of an industry index to which an enterprise belongs, and updating association weights among nodes at regular time; inputting enterprise real-time data into the pre-trained multi-modal large model for preliminary analysis, and outputting a risk thermodynamic diagram; key abnormal indexes are identified from the risk thermodynamic diagram, sub-graphs related to the key abnormal indexes are extracted from the knowledge graph, structured prompt words are generated from the sub-graphs, then the structured prompt words and standardized enterprise real-time data are jointly input into a multi-modal large model for joint reasoning analysis, and then a visual diagnosis report is generated. Accurate identification and intelligent diagnosis of enterprise risks are realized, and decision-making efficiency is improved.

The invention discloses a medical data analysis method based on an intelligent agent, and the method comprises the following steps: S1, obtaining a multi-dimensional heterogeneous data source which comprises an unstructured log, a structured attribute tag, time sequence sensor data and an iconography data report; s2, performing structured processing on the dynamic rule base document; s3, performing multiple rounds of iterative reasoning by using the data analysis agent to generate a preliminary analysis decision scheme; s4, constructing a data set for training a process reward model PRM; s5, training a process reward model as a verifier of a data analysis agent reasoning process, and optimizing the process reward model by using a cross entropy loss function; and S6, optimizing a decision scheme generation process of the data analysis agent based on a process reward model, sampling M candidate states for each decision step by the agent in a step-by-step reasoning process, scoring each candidate state by using a verifier, and selecting the state with the highest score as a final decision of the step.

The invention discloses an intelligent report generation system, method and device and a storage medium, and relates to the technical field of data processing, and the system comprises a main research agent which is used for multi-source data information collection and preliminary analysis; the report writing agent is used for converting the research result into a professional report; and the review agent is used for reviewing the professional report. A multi-agent collaborative architecture and a domain knowledge enhancement technology are deeply fused, a full-link quality assurance system including data credibility evaluation, multi-source cross validation and dynamic knowledge updating is established, and collaborative optimization of professional report generation in three dimensions of timeliness, economy and specialty is realized.

The invention discloses a construction potential safety hazard identification method and system based on multi-agent cooperation. The method comprises the steps that an intelligent agent network is constructed, the intelligent agent network comprises a plurality of intelligent agents responsible for different tasks, and the intelligent agents cooperatively work in a serial mode and a parallel mode; obtaining construction related data; performing preliminary identification of potential safety hazards to obtain a preliminary analysis result; verifying and optimizing the potential safety hazard based on the preliminary analysis result in combination with an intelligent agent network to obtain a verification result; correcting the preliminary analysis result based on the verification result to obtain a conclusion of hidden danger identification and risk assessment; and generating a standardized security management document based on the conclusion according to the selected document type. By implementing the method provided by the invention, more accurate hidden danger identification, risk assessment and regulation matching can be realized, standardized and high-quality safety management documents can be generated, the working efficiency can be improved, and the professionality and accuracy of the documents can be ensured, so that the construction safety management practice is effectively supported.

The invention relates to an intelligent identification and evaluation technology for tornado ground disasters, and the technology comprises a data collection and preprocessing module which collects image data and text data of tornado ground disasters from a plurality of data sources, carries out the preprocessing and cleaning of the data, and generates a structured data set; the multi-modal disaster assessment model is based on a VLLM and an advanced visual identification technology, adopts a double-flow network structure and comprises an image feature extraction branch and a text processing branch, the image feature extraction branch extracts key disaster features from a high-resolution image, and the text processing branch deeply analyzes text information related to disaster grade assessment; and the interactive learning mechanism allows the multi-modal disaster assessment model to request feedback to a user after preliminary analysis, and performs self-adjustment and optimization according to the feedback. The invention further provides a map display method. According to the method, the problems of data scarcity, image diversity, low damage evaluation efficiency, rapid processing requirements and model generalization ability can be effectively solved.

The invention relates to the technical field of data reasoning, in particular to an intelligent dispute analysis system based on big data, and the system comprises an event collection module, a dispute analysis module, a scheme generation module, an emergency scheme module and an auxiliary decision generation module. Generating an original data set; extracting event key elements of the original data set to obtain a preliminary analysis report; in combination with the attribution feature data and the party background data, carrying out attribution secondary analysis on the preliminary analysis report to generate a personalized mediation scheme; when an emergency triggering condition is identified, rescue resource data are integrated based on an event geographic position, and an emergency disposal plan is automatically generated; and generating an auxiliary decision report based on the personalized mediation scheme and the emergency disposal plan. The dispute solving efficiency can be improved.

The invention discloses a dynamic adaptive operation, maintenance and metering optimization method for an electric energy metering box, and the method comprises the steps: collecting electrical parameters, environmental parameters and box body state parameters in real time, constructing a three-dimensional data set through preprocessing, and carrying out the local storage and preliminary analysis through edge calculation; the condensation occurrence probability is calculated based on the environmental parameters, and self-adaptive regulation and control are triggered and a ventilation structure is linked; abnormal features are extracted, abnormity is recognized in a rule judgment and trend analysis double-engine mode, and graded early warning is carried out; performing comprehensive state scoring in combination with the three-dimensional data set, and matching a differentiated inspection mechanism; presetting a metering error reference value of a temperature and humidity interval, calculating a compensation value in real time and remotely correcting parameters of the electric energy meter; and establishing a shared data platform of the power supply and the power consumption, and realizing full-process data tracing and automatic report generation. According to the method, the condensation prevention and control accuracy is improved, the abnormal false alarm rate is reduced, the operation and maintenance resource allocation is optimized, the metering accuracy is improved, and the data collaboration and tracing capability is enhanced.

The invention relates to the field of rainfall pollution detection, in particular to an automatic rainfall sampling method and device for a high-density built-up area. Geographic information parameters and rainfall dynamic parameters of the high-density built-up area are collected; performing format unification, space-time alignment and normalization processing on the obtained multi-source data, and constructing a standardized multi-dimensional feature data set; inputting the standardized multi-dimensional feature data set into a pollution risk prediction model, and dynamically calculating and outputting a pollution risk level of a rainfall event and a time window of a key pollution stage; generating a composite dynamic sampling strategy based on the pollution risk level and the time window; the method comprises the following steps: collecting and storing a water sample according to a composite dynamic sampling strategy, and carrying out preliminary analysis on the collected water sample by using an integrated water quality sensor to generate actually measured concentration data; and carrying out weighted compensation on the generated actually measured concentration data by utilizing geographic information parameters of the high-density built-up area, and calculating the equivalent pollution concentration representing the comprehensive pollution level of the high-density built-up area.

The invention discloses a large model training method for generating corn growth guidance. The method belongs to the technical field of agricultural intelligent monitoring and prediction. The method solves the problem that when applied to a corn growth management scene, a large model method depends on multi-source heterogeneous information such as remote sensing images and meteorological data, although preliminary analysis and prediction based on the crop growth state can be achieved, the large model method still has limitation in the aspects of accuracy, interpretability and actual adaptability of decision suggestions. During data set construction, besides fusion of remote sensing data and multi-dimensional meteorological information, expert suggestions are added, more multivariate fusion features are constructed, and due to similarity between the expert suggestions and farmland management suggestions, correspondence between the features and tags is more accurate; according to the method, corn growth guidance based on a large model architecture can realize a technical span from'visible 'to'understandable' to'guidance ', and a scientific basis and decision support are provided for precise management of corn production.

The invention provides an intelligent question answering system and method. The intelligent question and answer method comprises the following steps. And receiving question information. A plurality of knowledge associated with the question information is collected. Based on the multiple pieces of knowledge, executing the large language model to generate a preliminary analysis result according to the question information; the preliminary analysis result comprises a plurality of steps required for answering the information. And recalling a plurality of candidate APIs associated with the plurality of steps according to the problem information and the preliminary analysis result. And selecting the selected API corresponding to each step from the plurality of candidate APIs to generate a problem solving plan. And based on a plurality of knowledge, executing the problem solving plan by using the large language model and dynamically adjusting the problem solving plan according to the execution result, and finally outputting the solution information based on the final execution result by using the large language model so as to improve the accuracy of the answer.

The invention relates to an energy consumption analysis and energy-saving report generation method and device, and belongs to the technical field of energy consumption analysis and energy-saving report generation. The method comprises the steps that a planning agent analyzes user requirements and formulates an analysis process; the time and space analysis agents analyze the energy consumption data according to the analysis process to obtain a preliminary analysis result; the preliminary analysis results are fed back to the planning agent, and the planning agent combines the preliminary analysis results and submits the results to the questioning agent; the questioning agent questions the preliminary analysis result in combination with the content concerned by the user and the common pain point of energy consumption and feeds back the questions to the time and space analysis agent for enhanced analysis to obtain enhanced analysis results, and the planning agent combines the enhanced analysis results and submits the enhanced analysis results to the questioning agent; the questioning agent questions the enhanced analysis result until there is no problem, and the planning agent submits the final analysis result to the summarizing agent; and the summarizing agent generates an energy-saving report according to the final analysis result and the typical case data.

A large model training method for generating corn growth guidance. It belongs to the field of agricultural intelligent monitoring and prediction technology. It solves the problem that when the large model method is applied to the corn growth management scene, it depends on remote sensing images and meteorological data and other multi-source heterogeneous information. Although it can realize preliminary analysis and prediction based on the growth state of crops, there are still limitations in the accuracy, explainability and actual adaptability of decision-making suggestions. When constructing the data set, in addition to the fusion of remote sensing data and multi-dimensional meteorological information, expert suggestions are also added to construct more diverse fusion features. Due to the similarity between expert suggestions and farmland management suggestions, the correspondence between features and labels is more accurate, which can make the corn growth guidance based on the large model architecture realize the technical leap from "seeing" to "understanding" to "guiding", and provide scientific basis and decision support for the precision management of corn production.

The present invention discloses a digital factory monitoring data mining method and system, which relates to the field of artificial intelligence technology, including real-time capture of multimodal data through adaptive mode, fusion mapping to a unified data space after edge computing preprocessing, and the use of deep learning models to identify patterns and detect anomalies, thereby improving monitoring accuracy and response speed. The preliminary analysis results are uploaded to the cloud to build an equipment health status model, predict operating trends, achieve predictive maintenance, and reduce unplanned downtime. Based on the prediction results, maintenance recommendations and production scheduling strategies are generated to optimize production processes, improve efficiency, and reduce costs. This method enhances data processing capabilities, promotes intelligent decision-making, and is of great value in improving the level of factory automation and intelligence, ensuring production continuity and efficiency, and enhancing overall operational efficiency and market competitiveness.

The invention relates to the technical field of new energy, in particular to a hydrogen production system sensitivity analysis method, system and equipment and a medium. According to the method, the Tornado graph is taken as preposed analysis, the layered sensitivity analysis is performed by combining the Morris method and the Sobol method, and the method is used for the input variable influence analysis of the capacity optimization model of the hydrogen production system of the new energy base. The method can systematically and qualitatively identify the variables having great influence on the objective function, then performs fine evaluation, further depicts the interaction effect between the key variables and the proportion of the interaction effect in the overall output variance, provides a basis for subsequent model simplification and parameter setting, and improves the modeling efficiency and stability. The method adapts to a complex engineering model, and is especially suitable for a new energy system planning model with many input variables and high calculation cost.

The present invention discloses a cloud measurement method for a hydraulic support group based on AR calibration and matching, which includes a physical system, a remote cloud data processing system and a VR system. In the physical system, AR glasses provide an interactive interface for virtual-reality interaction and data display, and the inspection worker completes the inspection by manually matching and dragging the MRTK toolkit. In the remote cloud data processing system, the cloud service computing system performs data storage and preliminary analysis, and the centralized control worker and the GPT decision system send the mixed decision results to the AR glasses after secondary data processing to request additional inspection data, and finally the on-site inspection worker completes the measurement. In the VR system, a twin hydraulic support group model is constructed and corrected, the overall workspace is constructed, and the posture reconstruction accuracy is verified, thereby realizing real-time measurement of the virtual posture of the hydraulic support group.

The application discloses a kind of microbial detection waste liquid component intelligent analysis processing system and method, it is related to the technical field of waste liquid component analysis processing.Method includes: obtaining the basic attribute data of waste liquid to be processed, dynamically setting the weight coefficient of waste liquid attribute index;Set and execute stage analysis process, obtain preliminary analysis determination result;Determine whether to meet the set key index condition, if not satisfied, then waste liquid analysis process is placed in path locking state;Start cross-sample batch linkage judgment mechanism, obtain attribute index similar historical waste liquid sample from historical batch;Determine whether to start abnormal combination trigger mechanism, trigger abnormal supplementary determination process, generate supplementary determination path;Update analysis determination result, generate corresponding waste liquid processing mode suggestion.The application realizes the risk control and self-adaptive optimization of waste liquid processing whole process, improves processing efficiency while ensuring environmental safety.

The invention discloses a power line sag detection method based on an SAR image, and relates to the field of detection path analysis, and the method comprises a data acquisition step, a preliminary analysis step, an environment monitoring step, a power line change analysis step and a power line detection analysis step. The power line sag data set corresponding to the target power line and the environment information corresponding to the area where the target power line is located are combined and analyzed to obtain the detection route corresponding to the target power line, the inspection route can be shortened through an optimized route planning algorithm, power line detection is completed in the shortest time, and the detection efficiency is improved. According to performance parameters of radar equipment, route optimization is carried out, various hidden dangers can be effectively found through detection, the route can be adjusted according to real-time environment information and unmanned aerial vehicle state information, and stable flight and effective detection can still be kept under severe weather such as strong wind and rainstorm.

The present application relates to the field of data reasoning technology, and in particular to a dispute intelligent analysis system based on big data. The system includes an event collection module, a dispute analysis module, a solution generation module, an emergency solution module, and an auxiliary decision generation module. The system collects dispute event description data, territorial feature data, and party background data to generate an original data set; extracts key event elements from the original data set to obtain a preliminary analysis report; combines the territorial feature data and party background data to perform a localized secondary analysis on the preliminary analysis report to generate a personalized mediation solution; when an emergency trigger condition is identified, the system integrates rescue resource data based on the event's geographic location to automatically generate an emergency response plan; and generates an auxiliary decision report based on the personalized mediation solution and the emergency response plan. The present invention can improve the efficiency of dispute resolution.

The application relates to the technical field of neural network inference performance analysis, and discloses a neural network inference performance analysis method for an NPU computing architecture. The method obtains original data and standardizes the original data, extracts a computing unit, a task and an architecture load feature to generate a performance feature vector, identifies inference performance requirements to obtain a preliminary analysis result, integrates the preliminary result and the standardized data to generate an enhanced analysis matrix, analyzes performance in multiple dimensions, calculates a necessity score and an architecture influence value, obtains decision data through multiple verifications, combines an NPU computing feature library and the decision data to construct an enhanced computing feature space, performs computing matching and parameter optimization, optimizes an inference path and performs pre-inspection, collects execution data flow and historical monitoring data to generate comprehensive monitoring data packets, and performs abnormality detection, early warning and dynamic optimization. The method improves the accuracy and efficiency of network inference performance analysis, and realizes dynamic optimization and adaptive adjustment of an inference process.

The present application relates to the technical field of data processing, and more particularly to an airport security data intelligent analysis method and system. The method comprises the following steps: obtaining airport multi-source security data; performing preliminary analysis on the security data according to the airport multi-source security data to obtain security characteristic sequence data; performing behavior mutation feature correlation according to the security characteristic sequence data to obtain security mutation feature correlation data, and performing cross-modal feature correlation according to the security characteristic sequence data to obtain security cross-modal feature correlation data; performing mutation behavior threat level evaluation according to the security mutation feature correlation data to obtain first security danger data, and performing cross-modal threat level evaluation according to the security cross-modal feature correlation data to obtain second security danger data; and performing security decision auxiliary work according to the first security danger data and the second security danger data. The present application greatly improves the intelligent level and efficiency of airport security through quantitative threat and intelligent decision support.

The invention relates to the technical field of neural network reasoning performance analysis, and discloses an NPU computing architecture-oriented neural network reasoning performance analysis method, which comprises the following steps of: acquiring and standardizing original data, extracting computing unit, task and architecture load characteristics to generate performance characteristic vectors, and identifying reasoning performance requirements to obtain a preliminary analysis result; integrating the preliminary result and the standardized data to generate an enhanced analysis matrix, analyzing the performance in multiple dimensions, calculating a necessity score and an architecture influence value, and obtaining decision data through multiple verifications; combining an NPU calculation feature library and decision data to construct an enhanced calculation feature space, performing calculation matching and parameter optimization, optimizing a reasoning path and performing pre-inspection; and collecting execution data streams and historical monitoring data to generate a comprehensive monitoring data packet, and carrying out anomaly detection, early warning and dynamic optimization. According to the method, the accuracy and efficiency of network reasoning performance analysis are improved, and dynamic optimization and adaptive adjustment of the reasoning process are realized.

The invention discloses an intelligent waste liquid component analysis treatment system and method for microbiological detection, and relates to the technical field of waste liquid component analysis treatment. The method comprises the following steps: acquiring basic attribute data of to-be-treated waste liquid, and dynamically setting a weight coefficient of a waste liquid attribute index; setting and executing a staged analysis process to obtain a preliminary analysis judgment result; whether set key index conditions are met or not is judged, and if not, the waste liquid analysis process is set in a path locking state; starting a cross-sample batch linkage judgment mechanism, and obtaining historical waste liquid samples with similar attribute indexes from historical batches; judging whether an exception combination triggering mechanism is started or not, triggering an exception supplementary judgment process, and generating a supplementary judgment path; the analysis judgment result is updated, and a corresponding waste liquid treatment mode suggestion is generated. Risk management and control and self-adaptive optimization of the whole process of waste liquid treatment are realized, the treatment efficiency is improved, and meanwhile, the environmental safety is ensured.

The invention discloses a risk monitoring system and method for a mining frequency converter, relates to the technical field of frequency converter monitoring, and solves the technical problems that monitoring data is not comprehensive, fault diagnosis is not timely, and an analysis method is not intelligent enough. Similar records in fault data can be quickly screened with abnormal parameters as standards, preliminary analysis reasons are generated, efficiency and accuracy are improved, abnormal parameter changes are tracked with time as a period and matched with theoretical change results corresponding to the preliminary analysis reasons, the fault reasons are further verified, fault diagnosis is more scientific and reliable, and the fault diagnosis efficiency is improved. For the situation that no analysis signal exists, the system can comprehensively collect fault records and judge fault risks, meanwhile, a fault tree is constructed to calculate the probability of all fault events, a potential risk fault logic gate is defined, the occurrence probability of the potential risk fault logic gate is calculated, fault probability information is generated, and a quantitative basis is provided for risk assessment.

The invention relates to the field of multi-modal model and multi-agent cooperation, and discloses a multi-agent typesetting generation system based on rule formalization injection, which comprises the following core steps: a rule formalization generation agent can receive a design specification input by a natural language to generate a formalization rule base; the VLM multi-mode intention understanding agent receives user materials and design requirements, then carries out preliminary analysis, distributes tasks to downstream professional agents for processing, and creates an initial layout. Then, the formalized rule checking agent calls the rule base to carry out compliance checking; and if the layout is not compliant, the system automatically triggers an iteration correction loop until the layout passes the verification or reaches an iteration upper limit. And the user can carry out manual refined modification or version rollback on the basis. According to the method, subjective design specifications are converted into objective machine instructions, and a multi-agent cooperation process is established, so that the problem of specification conformity in automatic typesetting is solved.

The invention discloses an edge AI-based distribution network non-fixed point operation intelligent safety monitoring integrated system and method, and relates to the technical field of power distribution network safety monitoring. Comprising a data acquisition module used for acquiring motion state data and position information of an operator and video image data of a working site; and the edge calculation module carries an edge AI chip and carries out real-time preprocessing and preliminary analysis on the data acquired by the data acquisition module. According to the invention, through comprehensive utilization of the data acquisition module, the edge calculation module, the core logic processing module, the early warning decision-making unit, the early warning execution module and the remote monitoring center, whole-course intelligent safety monitoring of operators is realized; through an operation behavior analysis unit and a security risk assessment unit in a core logic processing module equipped with an edge AI chip, the system uses a deep learning algorithm to identify operation behaviors and assesses a security risk level in combination with environmental data.

The invention discloses an ammeter connector health state management method and a storage medium. The method comprises the following steps: constructing and binding an ammeter connector digital twin of a power grid topological relation; multi-source operation data such as temperature of a key connection point, electrical data flowing through a loop and environmental data are collected in real time through a sensor network; inputting the data into a health state evaluation model to output a health state grade, fusing preliminary analysis based on a preset rule base and deep analysis based on a data-driven model in the evaluation process of the model, and generating a final grade by adopting a dynamic weight fusion mechanism according to the uncertainty of the results of the preliminary analysis and the deep analysis; generating and triggering a differentiated maintenance decision instruction according to the grade; and optimizing and updating the evaluation rule base and / or the data driving model based on result data fed back by field maintenance. According to the invention, comprehensive perception, accurate evaluation, intelligent decision and closed-loop optimization of the health state of the electric meter connector are realized, and the intelligent level of state management and the reliability of power supply are improved.

The invention relates to the field of automatic control, and particularly discloses an automatic quality control system based on a multi-modal large language model, which comprises the following steps of: firstly, constructing a multi-modal vector knowledge base by using scattered quality documents, CAD (Computer Aided Design) files and standard legend; during detection, the system firstly performs preliminary analysis on a product image to generate a defect hypothesis, and starts a coarse-to-fine two-stage dynamic knowledge retrieval by taking the defect hypothesis as an index. The retrieval process not only matches text semantics, but also finely aligns recalled knowledge with an actual abnormal region in an image through visual positioning, so that a highly focused and credible enhanced context is constructed for a multi-modal large language model. Based on the context, the model can perform deterministic and explainable decision, and finally the general reasoning ability of the model is combined with the harsh standard of a specific industrial scene, so that accurate, flexible and traceable automatic quality control is realized.

The invention discloses a hydropower station trend analysis and professional report generation system and method based on an intelligent agent and a large language model, and belongs to the technical field of hydropower station intelligent operation and maintenance and data analysis. The system comprises a data perception and execution layer, an agent scheduling and analysis layer, a professional report generation layer and a dynamic causal knowledge base. The method comprises the following steps: analyzing a user instruction through a scheduling agent and scheduling a tool to perform preliminary analysis; the analysis agent queries a dynamic causal knowledge base based on the preliminary anomaly to generate a cross-device causal hypothesis, and autonomously schedules a tool to perform data verification; and finally, synthesizing a structured diagnosis report containing a core diagnosis conclusion, an evidence chain and operation and maintenance suggestions by a professional report generation layer by utilizing a large language model. According to the method, the defect that only isolated data description can be carried out in the prior art is overcome, the crossing from'phenomenon description 'to'disease cause diagnosis' is realized, and the analysis depth and decision support value of the report are remarkably improved.

The invention discloses a workshop logistics sorting virtual debugging system based on artificial intelligence and an implementation method, and particularly relates to the field of industrial control, and the system comprises a multi-source cooperative sensing module which obtains original sensing data through a multi-source sensor array; the streaming feature construction module is used for performing data cleaning and feature enhancement on the original sensing data to obtain a multi-dimensional basic feature vector, and performing dimension reduction processing on the multi-dimensional basic feature vector to obtain a multi-dimensional feature vector; the space-time prediction module is used for extracting space-time dependence from the multi-dimensional feature vector to obtain a preliminary analysis result; the enhanced decision module is used for generating an optimal control strategy based on the preliminary analysis result; and the virtual execution and verification module loads the control strategy into a virtual workshop model for simulation operation, and finally generates a control instruction and a debugging analysis report. The accuracy and stability of the sorting process are remarkably improved, meanwhile, the cost and risk of actual debugging are reduced, and the efficient sorting requirement of a modern workshop is met.

The invention discloses a fault root cause determination method and device and electronic equipment. The method comprises the following steps: acquiring abnormal information of multi-source data from an event sensing layer; according to a preset arrangement strategy, calling atomic power to analyze the abnormal information to obtain a preliminary analysis result; analyzing the service damage degree according to the preliminary analysis result to obtain the service damage level; and under the condition that the service damage level reaches a preset level, executing a root cause positioning process according to a preliminary analysis result to obtain a fault root cause description corresponding to the abnormal information. According to the invention, the technical problem of poor fault processing accuracy and timeliness caused by long fault positioning time when a large amount of alarm information exists in a network in the prior art is solved.