459 results about "Diagnostic analysis" patented technology

The invention relates to the technical field of ultra-high performance concrete detection, in particular to an ultra-high performance concrete construction process detection and analysis system based on multi-source data fusion. According to the technical scheme, the system comprises a multi-physics field sensing network, a heterogeneous data fusion engine, an intelligent diagnosis and analysis system and a visual decision support platform, and the multi-physics field sensing network is composed of multi-modal sensors deployed in links of raw materials, stirring, transportation, pouring and maintenance; and cross-scale data acquisition and heterogeneous data fusion engines from nanoscale interface reaction to meter-scale structure morphology are realized. Cross-scale and multi-physics-field comprehensive monitoring is achieved through the multi-physics-field sensing network, efficient data processing and analysis are conducted through the heterogeneous data fusion engine, construction quality dynamic evaluation is conducted through the intelligent diagnosis and analysis system, and scientific decision making and construction management are assisted through the visual decision support platform. Therefore, the construction quality is improved, the construction process is optimized, the construction risk is reduced, and comprehensive guarantee is provided for ultra-high performance concrete construction.

The invention relates to the technical field of medical data processing systems, in particular to an artificial intelligence-driven medical diagnosis and treatment data processing method and system. The method comprises the steps that a multi-modal medical data acquisition module acquires and processes multi-source heterogeneous medical data of a patient, and a standardized data set is generated; a medical feature depth extraction module performs multi-dimensional feature extraction on the data set, and constructs a dynamic evolution feature matrix; a multi-dimensional health state space construction module constructs a patient health state multi-dimensional space according to the matrix and determines a key medical early warning index set; the real-time medical data fusion module maps real-time data to the space to generate real-time health risk factors; and the medical risk prediction and decision-making module establishes a personalized model, outputs a disease occurrence probability and generates personalized treatment suggestions. The system solves the problems that medical data processing is difficult, diagnosis analysis is not comprehensive, and a treatment scheme lacks personality, and diagnosis accuracy and treatment pertinence are improved.

The invention discloses a power cable fault diagnosis and analysis method and system based on multi-source data, and relates to the technical field of diagnosis and analys.During operation of the system, various sensors are installed in a cable and the surrounding environment of the cable, operation data of the power cable are collected in real time, and after the collected multi-source data are preprocessed, the power cable fault diagnosis and analysis method and system based on the multi-source data are obtained. The method comprises the following steps: calculating key diagnosis parameters by using a machine learning algorithm, obtaining a comprehensive fault diagnosis index CDI, comparing the comprehensive fault diagnosis index CDI with a preset first threshold M and a preset second threshold N, obtaining a fault level and outputting a corresponding processing strategy, and carrying out fault point positioning by using a reflected wave method in combination with multi-source data according to fault characteristics and a cable model. And outputting a specific position of a fault point, accurately positioning the fault point in the cable, integrating analysis results of the modules, and automatically generating a cable fault diagnosis report which comprises a fault type, a fault point position, potential risk analysis and maintenance suggestions.

The invention discloses a numerical simulation prediction method and system for typhoon binocular wall structure reconstruction judgment and maintenance duration after terrain interference and a medium, and relates to the technical field of typhoon fine structure evolution description and numerical simulation. According to the method, the binocular wall typhoon is identified through multi-source data fusion, a high-resolution numerical simulation system is constructed, and a typhoon structure is divided into four quadrants for differential diagnosis according to the wind shear direction based on vertical wind shear phase limit analysis. And the distribution characteristics of the rapid vortex silking area are captured by calculating vortex silking time parameters. And establishing a ditch sinking airflow detection mechanism, and quantitatively evaluating the contribution of radial advection, tangential advection, vertical advection and friction force items to tangential wind evolution by using tangential wind income and expenditure diagnostic analysis. And calculating an energy growth rate, and predicting a double-eye wall maintaining time length. According to the method, the outer eye wall re-formation judgment is output, the outer eye wall radius zone, the maintenance duration interval and the uncertainty are predicted, and the method is suitable for real-time business and research.

The invention discloses a battery health diagnosis and analysis method based on big data. The method comprises the following steps of performing real-time monitoring and data acquisition through various sensors and data sources; performing multi-source data fusion based on feature selection of the multi-source data to obtain a key feature set; creating an adaptive deep learning model, and inputting features in the key feature set into the adaptive deep learning model to obtain a battery health state prediction result; generating a battery health score based on the battery health state prediction result and the real-time data of the battery; based on the real-time battery health score, an early warning threshold value is adjusted, a dynamic alarm is generated, and corresponding maintenance suggestions and early warning measures are provided according to different health scores; and continuously optimizing the adaptive deep learning model according to historical battery health data and a model prediction result. According to the method, the data weight is dynamically adjusted based on the data quality, the sensor precision and the influence degree of the sensor precision on health prediction, and more accurate battery health state prediction can be realized.

The invention discloses a lingual surface diagnostic information acquisition, diagnosis and analysis method and system, and belongs to the technical field of medical auxiliary diagnos.The method comprises the steps that an acquired lingual surface image is matched with patient information, a symptom associated lingual surface area is determined, the associated lingual surface area is subjected to priority division, and whether the acquired image meets a clear standard or not is judged; the method comprises the steps of constructing an image index evaluation model based on tongue vibration and image texture, performing tongue vibration, texture stability and water vapor fuzzy interference evaluation on an image needing to be processed, constructing a stable frame evaluation model, and importing vibration intensity, texture stability and a water vapor proportion into the stable frame evaluation model to evaluate image area stability. Image registration is carried out on the stable frame set, multi-frame registration and fusion are carried out based on an image stable region, region stability and processing information are recorded, a high-quality image for tongue picture analysis is generated, the image definition of a key diagnosis region is improved, and the accuracy and stability of tongue picture analysis are improved.

The invention discloses a diagnostic analysis method and system based on a digital twinborn technology and an intelligent management and control platform. The method comprises the following steps: acquiring environmental data of a photovoltaic power station in real time and matching an inspection scheme; calling a corresponding inspection device and completing data acquisition according to the inspection scheme by using the intelligent management and control platform; preprocessing the collected data, performing fusion arrangement on the data by utilizing a knowledge graph of the photovoltaic power station, and dividing the data by adopting a preset layering and blocking mode; mapping the divided data to a pre-constructed three-dimensional model of the photovoltaic power station according to a progressive verification and construction strategy by using a digital twinborn technology, and generating a digital twinborn body of the photovoltaic power station; and performing fault prediction and judgment on the photovoltaic power station by utilizing the digital twinborn body of the photovoltaic power station and combining a machine learning algorithm, and obtaining a prediction and judgment result and displaying the prediction and judgment result in the digital twinborn body. According to the invention, the fault of the photovoltaic power station can be diagnosed timely and accurately in a complex or extreme environment.

The invention discloses a primary and secondary fusion pole-mounted circuit breaker intelligent fault diagnosis and monitoring system, and relates to the technical field of electric appliance fault diagnosis analysis, and the system comprises an information collection module, a sensing monitoring module, a diagnosis prediction module, an evaluation module and an optimization module. Communication barriers among different devices are broken through, the system can be compatible with various devices, comprehensiveness and compatibility of data acquisition are improved, it is ensured that data of various devices can be effectively acquired and integrated, accuracy and reliability of the data are ensured, data transmission quantity is reduced through compression transmission, network bandwidth pressure is reduced, and key fault information can be transmitted in time. Operation and maintenance personnel can quickly respond and process, the fault influence range and time are reduced, key information can be accurately extracted according to actual requirements through the feature extraction module, and the pertinence and accuracy of fault diagnosis are improved.

The invention provides a circuit breaker action coil current characteristic diagnosis and analysis system and method. Belongs to the technical field of power system automation and substation state monitoring. The method comprises the following steps: carrying out synchronous wave recording on current of a plurality of circuit breaker actuating coils on a direct current feeder screen side to generate multi-channel synchronous wave recording data; meanwhile, SOE message information is collected, a circuit breaker with specific action is automatically matched and positioned by utilizing a timestamp and action identification information in an SOE message, and action circuit breaker positioning data is generated; the current waveform data of the circuit breaker actuating coil are collected in real time, and a high-precision algorithm is used for feature extraction and analysis, so that abnormal fluctuation in current change can be accurately captured, such as overlarge starting current and unstable holding current, the risk of misoperation or rejection of operation of the circuit breaker caused by abnormal current characteristics is reduced, and the service life of the circuit breaker is prolonged. Electric power system accidents caused by equipment faults are avoided, and the operation reliability of the circuit breaker is improved.

The invention relates to the technical field of fault diagnosis, and discloses a transformer adaptive fault diagnosis method, system and equipment based on a sensor, and the method comprises the steps: collecting an operation signal of a transformer through a sensing device, carrying out the feature extraction of the operation signal, and obtaining a plurality of signal feature data; each signal feature data is input into each corresponding signal fault diagnosis model to obtain a plurality of fault diagnosis sub-results, and each signal fault diagnosis model is constructed by adopting a probabilistic neural network model; according to a preset weight value, weighted summation is carried out on the fault diagnosis sub-results, a fault diagnosis result of the transformer is obtained, and the weight value is obtained through calculation based on the diagnosis accuracy of each signal fault diagnosis model. According to the invention, comprehensive data processing and diagnostic analysis are carried out on various sensor signals, so that efficient and accurate diagnosis of the transformer fault can be realized, and thus a powerful guarantee is provided for stable operation of a power system.

The invention relates to the technical field of power system power distribution network fault detection, in particular to an arc fault diagnosis and analysis method based on an artificial intelligence algorithm, and the method comprises the four steps: synchronous data collection and topological excitation, deployment of terminals at transformer area nodes, injection of characteristic current, and synchronous collection of response waveforms; signal preprocessing: separating a key frequency band through double-digital band-pass filtering, and calculating energy ratio and other enhanced fault features; performing multi-source feature fusion and intelligent diagnosis, constructing a vector containing statistical features, time domain distortion features and topology identification, and inputting a gradient boosting decision tree and deep neural network hybrid model to obtain fault confidence and type; and based on fault positioning and verification of topology, scheduling multi-node cooperative monitoring, and determining a fault point in combination with a topological relation. The method improves the data reliability and diagnosis precision, achieves the precise positioning of a fault, and guarantees the safe operation of a power distribution network.

The invention discloses a big data-based machine tool fault diagnosis and analysis method and system. The method comprises the following steps of: acquiring signal data of each part of a machine tool by utilizing a sensor; carrying out de-noising processing on the acquired signal data; the de-noised signals are decomposed by using successive variational mode decomposition, screening is carried out based on a Pearson's correlation coefficient, and the unscreened signals are fused and then are subjected to signal reconstruction with the screened signals; the method comprises the following steps: constructing a fault diagnosis model based on an Adaboost-Reformer integrated algorithm, and optimizing hyper-parameters of the Reformer model by using an improved water circulation optimization algorithm; the reconstructed signals are divided into a training set and a test set, the training set is input into the optimized model for training, machine tool signals collected in real time are input into the trained model after being subjected to denoising and reconstruction processing, and corresponding fault categories are obtained. The method can effectively extract fault features from the multi-mode sensor signals and perform accurate diagnosis, has strong robustness and high efficiency, and is suitable for intelligent fault diagnosis of industrial equipment.

The invention provides a disease diagnosis result evaluation method and device, equipment and a storage medium. The method comprises the steps of obtaining at least one to-be-evaluated disease diagnosis result determined based on a target medical record; for each to-be-evaluated disease diagnosis result, inputting the to-be-evaluated disease diagnosis result and the target medical record into a large language model to obtain at least two diagnosis roles output by the large language model and diagnosis dimensions of each diagnosis role; for each diagnosis role, based on the diagnosis role, the diagnosis dimension corresponding to the diagnosis role, the to-be-evaluated illness state diagnosis result and the target medical record, determining first prompt information, and inputting the first prompt information into a large language model to obtain a diagnosis analysis report corresponding to the diagnosis role output by the large language model; and based on the diagnosis analysis report corresponding to each diagnosis role, determining a target evaluation result of the to-be-evaluated illness state diagnosis result, the target evaluation result being used for representing whether the to-be-evaluated illness state diagnosis result is correct or not. The evaluation accuracy of the illness state diagnosis result can be improved.

The invention relates to the technical field of report optimization, in particular to a user medical history-based physical examination report conclusion priority adjustment method, which comprises the following steps of: acquiring multi-source data, and preprocessing the multi-source data to obtain a structured multi-source data set; based on the structured multi-source data set, mining an association relationship between health data and diseases through association analysis, constructing a health trend prediction model in combination with a deep Q network, and obtaining a multi-dimensional diagnosis analysis result; determining a diagnosis conclusion priority in combination with a multi-dimensional diagnosis analysis result and a priority calculation formula; calculating a comprehensive risk score through the data fusion score, and dividing risk levels according to the comprehensive risk score to match corresponding stage intervention strategies; and inputting a multi-dimensional diagnosis analysis result, intervention strategy execution feedback data and user health preference into a comprehensive diagnosis decision model, and obtaining a differentiated health association strategy in combination with a diagnosis conclusion priority. According to the scheme, the severity of the disease is accurately identified by dynamically adjusting the priority.

The invention discloses a sample concentration detection method, a standard curve group establishment method, a matrix specificity standard curve optimization method, a standard curve group establishment device and an immunity analyzer, and belongs to the field of diagnostic analysis. Acquiring matrix information of a to-be-detected sample; selecting a matrix specificity standard curve corresponding to the matrix of the sample to be detected; and detecting the sample and calculating the concentration of the analyte in the sample to be detected by using a matrix specificity standard curve selected from a plurality of standard curve groups of which the specificity corresponds to different matrixes. The corresponding substrate specificity standard curves are configured for various types of substrates in advance, and when a detection task is carried out, the corresponding substrate specificity standard curves can be selected and called, so that the detection is more targeted, the substrate effect caused by using a general standard curve can be avoided, the substrate effect can be reduced, the implementation operation is simpler, and the detection efficiency is improved. And the working time of detection personnel can be remarkably saved, and the detection cost is effectively reduced.

The invention relates to the technical field of medical assistant systems, and discloses a comprehensive intelligent medical assistant system, which comprises an information acquisition unit used for acquiring user state information; the user state information comprises user static data, dynamic data and feedback data; the model construction unit is used for performing feature extraction according to the user state information and establishing data association indexes of physiology, environment and behaviors; the information processing unit is used for automatically capturing symptom characteristic information and carrying out diagnostic analysis on the input information in combination with user state information; the pushing unit is used for performing local decision processing on the analysis result, forming a diagnosis path diagram and pushing the diagnosis path diagram to a user side; and the learning optimization unit is used for optimizing a recommendation strategy according to the feedback data and integrating long-term health features to carry out optimization suggestions. According to the invention, the service mode of medical health is remodeled, the unique health characteristics of each user can be more accurately understood, the service quality and credibility of a medical assistant system are improved, and the occupancy rate of medical resources is reduced.

The invention particularly relates to an engineering wire harness testing method and system, and relates to the technical field of engineering wire harness testing. A measurement and self-calibration module; a test channel health degree real-time monitoring module; and a data processing and diagnosis analysis module. According to the invention, the standard gold wire harness and an automatic self-calibration mechanism solve the problems of long period and low efficiency of traditional manual calibration; the system can automatically trigger self-calibration according to multiple conditions such as time, events and the like, the measurement deviation is captured through multi-dimensional reference measurement, real-time deviation compensation is achieved by means of software closed-loop calibration, meanwhile, fault components can be accurately positioned according to hardware deviation, a maintenance report can be generated, and reference drift caused by environment fluctuation and component aging in a calibration interval is avoided.

Systems and methods for analyzing multimodal data using one or more large language machine learning models may include inputting medical image data to a trained image-based machine learning model and generating medical image data embeddings; inputting genomic data to a trained genomics machine learning model and generating genomic data embeddings; inputting text data to a trained large language machine learning model, and generating a mapping of extruded data; and feeding the medical image data embeddings, the genomic data embeddings, and the mapping of extruded data to trained large language machine learning model at a foundation layer, the trained large language machine learning model at the foundation layer being trained to generate one or more of a data and analytics report, a diagnostic analysis report, a large language model agent, or user data for display in a user interface at an output device.

The invention discloses a production operation benchmarking system and method based on multi-source data analysis, and belongs to the technical field of production operation benchmarking, and the system comprises a multi-source confrontation fusion module, a production situation awareness map module, a self-adaptive diagnosis analysis module, a cross-domain collaborative deduction strategy module, a hidden variable benchmarking analysis module and a cross-scene migration module. According to the method, enterprise resources are quantized into a matrix, cross-domain resource allocation is optimized in combination with a collaborative gain function, collaborative gains are measured through a nuclear norm and a Frobenius norm, multi-target weighting is combined, priorities of different fields are balanced, the global optimality of a collaborative strategy is improved, the enterprise resource state is synchronized to a digital twinborn model, and the collaborative resource allocation efficiency is improved. A strategy execution process is simulated, burst interference is injected, strategy stability is verified, a cross-domain cooperation efficiency comprehensive value is calculated through a four-flow state, a single-domain independent optimization mean value is compared, and cooperation gain is quantified.

The invention discloses a river-groundwater response and connectivity quantification method based on an event scale. The method comprises the following steps: acquiring rainfall, river water level and groundwater level time sequence monitoring data of a research area; identifying an independent precipitation event; identifying an effective runoff response event, and calculating the features of the quantitative event scale river response; effective groundwater response events are identified and classified, and features of groundwater responses of quantitative event scales are calculated; and calculating the response peak time lag of the river and the underground water, and constructing a dynamic connectivity index. The method breaks through the limitation of a traditional steady-state analysis method, can realize dynamic response characteristic and connectivity evaluation with high time resolution, has high universality and expandability, can systematically reveal the event-driven coupling process of the river and the underground water, and improves the reliability of the river and the underground water. The method is suitable for water resource evaluation and connectivity diagnostic analysis in areas with complex climate-geological conditions.

The invention discloses a sluice intelligent control data real-time processing method based on edge calculation, and relates to the technical field of sluice control data analysis, and the method comprises the following steps: obtaining the basic feature data of a sluice, obtaining the sluice opening degree flow relation, and collecting the related data of a water body in real time; in the water gate opening or closing process, the gate passing flow under different opening degrees is collected, data preprocessing is carried out, and basic opening degree flow data are obtained; theoretical opening flow analysis is carried out to obtain theoretical opening flow data; gate blockage analysis is carried out based on the basic opening flow data and the theoretical opening flow data, and the gate blockage condition is obtained; the method is used for solving the problems that when an existing water gate control data analysis technology is used for diagnosing and analyzing the water gate blocking condition through the lockage flow, blocking abnormity and opening abnormity cannot be accurately distinguished according to the deviation between the theoretical lockage flow and the actual lockage flow in the gate opening change process, and meanwhile the blocking condition cannot be judged.

The invention provides a household small electric appliance diagnosis system based on AR imaging and AI interaction, which relates to the technical field of intelligent household appliance intelligent diagnosis and comprises a multi-mode intelligent sensing framework, a modular AR detection accessory, an AI diagnosis analysis center and a human-computer interaction interface. According to the modular AR detection accessory, the sensor module is rapidly replaced through the magnetic type universal interface base, the requirement for collecting temperature, circuit, sound and other multi-mode data is met, the hardware adaptability is remarkably improved, the modular AR detection accessory has the functions of low power consumption and efficient power supply, a battery is arranged in the base, reverse wireless charging is supported, and the reliability is high. The problem of endurance of a high-power-consumption module is solved, a modular AR detection accessory is combined with an AR terminal through a magnetic type sensor module, fault three-dimensional coordinates are superposed on real equipment in real time, a user can visually check internal problem points, a mixed reality operation table supports gesture, voice and eye movement control, the operation threshold is lowered, and the operation efficiency is improved. According to the invention, the user is directly guided to carry out precise maintenance through the AR marking system.

The invention belongs to the technical field of medical diagnosis, and discloses a quantum multicolor immune digital pathological diagnosis analysis system, which comprises the following steps: acquiring high-resolution digital images of an immunohistochemical or immunofluorescence slice and a slide, and analyzing multicolor signal distribution in the images to obtain a quantum chromatographic image set; pixel-level color separation and intensity quantization are carried out on the quantum chromatographic image set, and a quantum intensity index set of each pixel is generated; performing multi-target color overlay analysis and post-translational modification protein specificity evaluation on the quantum intensity index set to construct a multi-color modification index set; identifying a single cell boundary and a multi-cell community based on the multicolor modification index set, and extracting a cell morphological parameter and a spatial adjacency relation to obtain a cell quantum analysis set; performing consistency correction and traceable recording by applying an automatic quality control mechanism, and generating a digital diagnosis report; the accuracy of accurate qualitative and quantitative analysis of immunohistochemistry and immunofluorescence is greatly improved.

The invention provides a digital beauty teaching system based on artificial intelligence, and the system comprises a learning condition collection and analysis module which is used for collecting learning condition data, constructing a student file, and carrying out the calculation to obtain a learning condition comprehensive index; the course planning and recommendation module is used for performing course recommendation, learning planning, course dynamic adjustment and cross-domain course recommendation; the intelligent tutoring and question answering module is used for constructing an error type database and an operation diagnosis model and carrying out diagnosis analysis and intelligent question answering on real-time operation; the learning condition evaluation module is used for constructing an AI scoring model and carrying out evaluation on real-time homework; and the visualization and adjustment module visually displays the learning condition evaluation result and dynamically adjusts the homework improvement suggestions and the recommended courses. The system can quantify the overall learning participation degree and expression level of students, accurately analyze the learning condition, accurately recommend courses, intelligently diagnose common errors, generate improvement suggestions, and score works, so that the scoring result is more accurate, and efficient proceeding of beauty teaching is promoted.

The invention relates to a river and lake ecosystem health management diagnosis method and system, and belongs to the technical field of water ecological management. The method comprises the steps of data acquisition, river and lake health condition calculation, monitoring index abnormity alarm, supervisor management, governance technology library construction and river and lake ecological health monitoring and early warning. The system comprises a data acquisition system, a monitoring display system, a supervisor management system, a treatment technology library system, a repair tracking management system, a monitoring and early warning system and a diagnostic analysis system. According to the invention, multi-source data can be integrated, visual display and deep analysis of complex data are realized, and intelligent decision support is provided for managers. Meanwhile, by formulating a unified technical standard, data islands are broken, interconnection and open sharing of water environment data are promoted, and favorable conditions are created for further analysis and application of water environment big data.

The invention relates to the technical field of electric energy metering anomaly detection and diagnosis analysis, in particular to a dynamic return and compensation method and system based on an electric energy metering data restoration model, and the method designs a distortion traceability algorithm of multi-dimensional feature fusion and improved SVM (Support Vector Machine), and extracts the characteristics of load features, environment features, packet loss rate, sampling delay distortion and the like. And load fluctuation-dominated distortion, environment interference-dominated distortion and coupling-dominated distortion can be accurately positioned. For multi-source distortion restoration, a load self-adaption-environment compensation-coupling correction three-level restoration model is provided, and through differential compensation of different load types, dynamic calibration of environment parameters such as temperature and humidity, electromagnetic interference and the like and coupling coefficient matrix optimization, the measurement data restoration precision is stably maintained to be 98% or above. According to the invention, through the research on the metering data distortion traceability-coupling distortion data reduction-dynamic return and supplement calculation-return and supplement result closed-loop optimization method, the precise repair of metering errors and the scientific calculation of electric quantity return and supplement in a complex scene are realized.

The invention belongs to the technical field of equipment maintenance, and discloses an intelligent prediction maintenance system and method for a high-speed continuous rolling cold pilger mill. The method comprises the following steps: constructing a data acquisition network of the high-speed continuous rolling cold pilger mill, and acquiring operation data of each part of the high-speed continuous rolling cold pilger mill by using the data acquisition network; constructing a weighted undirected connected graph by taking each part of the high-speed continuous rolling cold pilger mill as a node; dividing the weighted undirected connected graph into a plurality of grids, and combining nodes in each grid into a super node to obtain a simple connected graph; presetting a maintenance rule, and performing node detection on the simple connected graph by using an improved scanning line algorithm to obtain super nodes in abnormal states; according to the method, the super nodes in the abnormal state are expanded to the component set, the maintenance suggestions are generated for the components in the component set, various possible abnormal conditions can be comprehensively considered, and diagnosis analysis is more comprehensive and accurate.

The invention discloses a heat supply energy consumption diagnosis and analysis system fusing a big language model and cloud-mist edge collaboration, and the system comprises an edge processing layer which is used for monitoring the energy consumption related data and heat supply operation data of a heat supply system diagnosis object; the fog processing layer is used for extracting strong correlation data characteristics influencing energy consumption and establishing an energy consumption prediction model of a heat supply system diagnosis object; the analysis module is also used for establishing an energy consumption anomaly analysis knowledge graph according to the heat supply operation data and energy consumption anomaly analysis mechanism knowledge; the cloud processing layer is used for training a heat supply system energy consumption abnormity diagnosis large model according to the energy consumption predicted value of the heat supply system diagnosis object in combination with a preset energy consumption diagnosis standard value; and the server is also used for obtaining an energy consumption abnormality diagnosis type and an abnormality diagnosis object according to the heat supply system energy consumption abnormality diagnosis large model, performing question and answer intention recognition and constructing an energy consumption abnormality analysis question and answer agent based on the large language model in combination with the energy consumption abnormality analysis knowledge graph, and generating an energy consumption abnormality analysis and energy-saving recommendation scheme answer.

The invention discloses a cement kiln thermotechnical diagnosis analysis method and system based on heat balance calculation, and relates to the field of cement kiln thermotechnical diagnos.The analysis method comprises the steps that a corresponding heat balance state is calculated by obtaining cement kiln thermotechnical parameters and material component data, the material balance relation between the material input quantity and the material output quantity of the cement kiln is analyzed; evaluating the energy utilization efficiency of the cement kiln firing system by taking the ratio of the cement kiln clinker formation heat to the input total heat as a thermal efficiency index, and generating a diagnostic index by combining a thermal balance state and a material balance relationship; and comparing the diagnosis index with a historical operation optimal value to determine a deviation condition, evaluating heat loss abnormity and an operation state of the cement kiln thermotechnical engineering according to the deviation condition, constructing a causal atlas to predict a development trend of the cement kiln thermotechnical engineering, and obtaining a cement kiln combustion control result. The method can help to understand how each factor affects the overall performance of the thermal system, and facilitates the optimization and adjustment of different parameters according to the causal relationship in the later stage.

The invention belongs to the technical field of heat supply equipment diagnosis, and discloses an equipment diagnosis analysis system based on a performance attenuation characteristic curve. The method comprises the following steps: a sensor network acquisition layer, a data governance layer, a curve construction module layer, a dynamic calibration layer and a prediction output layer are included; the sensor network acquisition layer is responsible for comprehensively acquiring operation state data of heat supply equipment and constructing an initial data set by using the data in a stable operation period of the first 7-10 days after the equipment is put into production; and the data governance layer is used for processing and standardizing the collected state data and the original data set to construct a high-dimensional original feature set. According to the method, the equipment performance attenuation characteristic curve is generated through the health score and the curve generation model, the time-varying trend of the performance, the attenuation rate and possibly existing stage characteristics are clearly displayed, the problems of threshold comparison and statistical analysis are solved, and meanwhile, the remaining service life of the equipment can be dynamically predicted.