52 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 relates to the technical field of constructional engineering, in particular to a cement concrete durability optimization method and system for an airport runway, and the method comprises the steps: building a data collection frame, and collecting the data of the airport runway; preprocessing the collected data, and performing preliminary analysis based on the processed data to obtain main factors influencing the durability of the cement concrete; based on the preliminary analysis result, using machine learning and deep learning technologies to construct a durability prediction model; a self-learning mechanism is introduced, a feedback loop is established, actual operation data is compared with a prediction result, and model parameters are dynamically adjusted; and based on a prediction result of the durability prediction model, combining a plurality of durability indexes, and adopting a multi-objective optimization algorithm to generate an optimal cement concrete formula and a construction process scheme so as to obtain a final durability optimization scheme. By means of the method, the defects of a traditional method in the aspects of data accuracy and model adaptability are effectively overcome, and the durability optimization level of the airport runway cement concrete is improved.

The invention provides an unmanned aerial vehicle automatic inspection method for a transformer substation, and the method comprises the steps: transmitting shot image data, flight state data and equipment state data back to a nest in real time through a wireless communication link in the inspection process of an unmanned aerial vehicle, carrying out the primary processing of the received data through the nest, and carrying out the butt joint with a remote intelligent inspection system, uploading the data to a remote intelligent patrol system; the remote intelligent inspection system receives and displays the inspection data of the unmanned aerial vehicle in real time, analyzes and processes the image data by using an intelligent analysis algorithm, identifies equipment defects, fault hidden dangers, abnormal operation states and environmental abnormalities, and generates a preliminary analysis result; according to the intelligent analysis algorithm, deep learning target detection, time sequence analysis and other technologies are utilized to carry out deep processing on image data, and various equipment defects, fault hidden dangers and environment anomalies can be accurately recognized. The defect finding capability is greatly improved, measures are taken in time for repairing, equipment faults are avoided, and safe and stable operation of the transformer substation is guaranteed.

The invention relates to the technical field of design examination architecture, in particular to a general design examination architecture method for comprehensively scheduling small models based on AI large models. Comprising the following steps: constructing an intelligent scheduling system which takes a general AI large model as a core and comprises a visual analysis model, a text understanding and segmentation conversion model and a rule generation model, enabling a user to submit a design review task through a dialogue interface or a file uploading mode, receiving a task request through a large language model, and carrying out preliminary analysis; through multi-module cooperation and intelligent scheduling, the automation degree of design drawing review is greatly improved, the overall review efficiency is improved, visual analysis, text processing and rule generation technologies are fused, compared with a rule-based drawing review system, wider review content can be covered, and the design drawing review efficiency is improved. The versatility of the architecture enables the architecture to rapidly adapt to new rules and standards, and enables the iteration updating of the drawing checking system to be faster.

The invention discloses a construction site dust fall and pollution prevention and control method and system based on multi-modal perception, and relates to the technical field of construction management, and the method comprises the steps: collecting PM data at a boundary in real time through a boundary monitoring device preset at the boundary of a construction site, and meanwhile, using an unmanned plane carrying the monitoring device to monitor the PM data at the boundary; performing flight monitoring on the interior of the construction site according to a preset route, and collecting PM data of different areas of the site; performing preliminary analysis on the multi-modal sensing data in real time by using an edge calculation module, and identifying dust abnormal conditions; and the data processing center analyzes a current pollution scene of the construction site, matches an optimal dust falling strategy for the current pollution scene, controls surrounding dust falling equipment to perform dust falling operation, constructs a self-organizing communication network among the dust falling equipment, and adjusts a dust falling operation plan by using dust falling feedback information. According to the invention, real-time data collected by the boundary monitoring equipment and the unmanned aerial vehicle are fused, and environment parameters and construction working conditions are combined, so that precise regulation and control of the dust falling equipment are realized.

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 the technical field of data processing, in particular to an intelligent analysis method and system for airport security check data. The method comprises the following steps: acquiring airport multi-source security check data; performing security check data preliminary analysis according to the airport multi-source security check data to obtain security check characteristic sequence data; performing behavior mutation feature association according to the security check characteristic sequence data to obtain security check mutation feature association data, and performing cross-modal feature association according to the security check characteristic sequence data to obtain security check cross-modal feature association data; performing mutation behavior threat level evaluation according to the security check mutation feature associated data to obtain first security check danger data, and performing cross-modal threat level evaluation according to the security check cross-modal feature associated data to obtain second security check danger data; and performing security check decision-making auxiliary operation according to the first security check danger data and the second security check danger data. According to the invention, through quantitative threat and intelligent decision support, the intelligent level and efficiency of airport security check are greatly improved.

The invention discloses a regional power grid load prediction management method and system based on meteorological data, and relates to the field of regional power grid load prediction, and the system comprises a meteorological data acquisition module, an electric power information acquisition module, a preliminary analysis module, a power grid load early warning module, and a comprehensive evaluation module. The method comprises the following steps: comprehensively analyzing the generating capacity influence and the power consumption influence of a target area corresponding to various types of meteorology, and obtaining a natural generating deviation coefficient corresponding to each type of meteorology in each power consumption time period of the target area according to meteorological data corresponding to each power consumption time period of the target area and basic power information corresponding to the target area; and comprehensive analysis is carried out in combination with the meteorological data corresponding to the target region and the basic power information corresponding to the target region to obtain the electrical load early warning coefficients corresponding to the various types of meteorology corresponding to the target region in the various power consumption time periods, and the accuracy of the regional power grid load prediction result by the various types of meteorological data is improved through a scientific and effective analysis mode.

The invention discloses a course document understanding method based on multi-agent logical reasoning. The method comprises the following steps: (1) selecting a model; (2) performing data acquisition and preprocessing on the course document; (3) performing course content symbolization; (4) generating reasoning; (5) performing reasoning execution; (6) carrying out multi-agent collaborative confrontation; according to the method, a plurality of agents are integrated, and the defects, such as logic inconsistency, precondition hypothesis deviation or argumentation chain weakness, possibly existing in the reasoning process of the course content symbolization module, the reasoning step generation module and the reasoning step execution module are examined in a systematic mode, so that the curing dependence of a single model on a preliminary analysis result is avoided.

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.

Disclosed herein are an evidence collection guidance method and apparatus for file selection. The evidence collection guidance method includes generating pieces of preliminary analysis information that are pieces of collection target information, setting levels of the pieces of preliminary analysis information based on predefined rules, and generating and outputting notification information including summary description information and follow-up measure items related to the pieces of preliminary analysis information corresponding to the levels.

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 invention provides an onboard flood data processing method, device and system based on the aviation emergency mode, the method comprising: based on the ground observation image, performing image mosaicking to obtain the target image; inputting the target image into the water body recognition model to obtain the water body recognition result; based on the water body recognition result and the geographic information data, performing spatial analysis to obtain the comprehensive flood analysis information; when the aircraft lands, the comprehensive flood analysis information is sent to the remote terminal, so that the remote terminal receives the comprehensive flood analysis information, makes a preliminary judgment on the disaster situation, and guides the generation of rescue command decisions. The onboard flood data processing method, device and system based on the aviation emergency mode provided by the present invention can realize the splicing, correction, information extraction and the like of flood data in real time and quasi-real time on board during the flight, and transmit the information to the ground command system after the aircraft lands, so as to improve the efficiency and timeliness of information processing, which is of obvious significance to emergency rescue.

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 present invention discloses a gas drainage system for a high-level drainage roadway in the initial mining face, which relates to the technical field of gas drainage in the drainage roadway. The present invention constructs a preset three-dimensional space of the working area through the coordinates of the three-dimensional marking unit, and then combines the preset three-dimensional space of the working area with the images generated by real-time induction to generate a visual dynamic display three-dimensional map, realizing the dynamic and intuitive changes of the dynamic drainage area, the dynamic goaf area and the rock area, indirectly assisting in judging the safety of the working area by the staff, and then obtaining a lighting signal or an alarm signal through the preliminary analysis of the environmental information at the coordinates, so as to directly remind and assist the staff to judge the safety within the fault point or the fault point area range of the working area, and then combining the environmental information with the volumes of the dynamic drainage area and the dynamic goaf area to generate a stability judgment of the corresponding area, thereby directly or indirectly assisting the staff to give an early warning to the dynamic drainage area and the dynamic goaf area again.

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.