184 results about "Multi dimensional analysis" patented technology

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing method and device based on factor weight optimization, equipment and a medium. Comprising the following steps: receiving structured data and unstructured data, and extracting a basic key factor set and monitoring object information in the data to generate a data feature set; executing multi-dimensional analysis and identification value determination based on the data feature set, and generating an analysis level and an identification value parameter; and performing weight correction processing on the basic key factor set, generating an optimized factor coefficient matrix, fusing the optimized factor coefficient matrix with associated equipment source data, and outputting a processing result. According to the method, structured and unstructured data are processed in a unified manner, so that the data integration capability is enhanced; multi-dimensional elements are extracted in combination with a pre-training model, and the key factor recognition effect is improved; and the analysis level and the identification value parameter are combined to drive weight correction and fuse the optimization matrix and the equipment source data to generate a processing result, so that the processing efficiency and the result accuracy are improved.

The invention discloses an operation and maintenance delivery abnormal event early warning method and system based on a cloud platform, and relates to the technical field of event early warning. The method comprises the steps of collecting a real-time operation data set of an operation and maintenance delivery link, performing multi-dimensional analysis based on the real-time operation data set, and constructing a multi-dimensional monitoring data set; performing context correlation analysis on the multi-dimensional monitoring data set, and constructing dynamic baseline parameters; deviation degree calculation is carried out on the real-time operation data set, a data deviation value is generated to trigger the cloud platform to carry out abnormal event judgment of operation and maintenance delivery, and an abnormal judgment result is obtained; performing event association according to an abnormality judgment result, determining an abnormality risk level, synchronizing the abnormality risk level to a cloud platform for tracing, generating a multi-level early warning instruction, and pushing the multi-level early warning instruction to a target terminal for graded warning. The technical problem that early warning of the operation and maintenance delivery event is not accurate and timely enough in the prior art is solved, and the technical effect of improving the accuracy and timeliness of early warning of the abnormal event is achieved.

The invention relates to the field of power system planning, in particular to a micro-grid boundary quantitative evaluation method and system for multi-dimensional analysis and dynamic verification. The method comprises the steps that power distribution network and micro-grid scheme data are acquired, and scene recognition and decoupling modeling are carried out after standardization processing; the method comprises the following steps: extracting end supply-preserving scene data, analyzing cost elements, and generating a critical cost threshold table through normalization processing and a threshold approximation algorithm; performing multi-dimensional parameter correlation analysis and integrated learning training based on the table, and constructing a multi-dimensional boundary index model; performing benefit matching calculation and green value accounting according to the result to generate an economic benefit decomposition structure; real-time streaming data processing and stability verification are combined to generate an economical efficiency boundary index set; and finally, generating a standardized evaluation file through matrix mapping and weight dynamic adjustment. According to the method, dynamic quantitative evaluation of the economy boundary of the micro-grid is realized, the capacity substitution benefit and the green value are effectively integrated, and an accurate basis is provided for planning decision.

The invention belongs to the technical field of display design evaluation, and particularly relates to an exhibition display layout design effect analysis and evaluation method, which comprises the following steps of: constructing a dynamic spatial topology network by collecting audience moving tracks, exhibit information and spatial structure data in real time; the problem of insufficient attention on space utilization influenced by dynamic factors such as human traffic fluctuation is solved. And dynamic changes in the space are converted into quantifiable network nodes and connection relationships, so that the space utilization state is changed from static design to dynamic monitoring, accurate capture of the real-time use condition of the exhibition space is realized, a comprehensive and real-time data basis is provided for subsequent analysis, and the real-time monitoring of the exhibition space is realized. Meanwhile, the dynamic space topology network is analyzed by means of spatial-temporal feature fusion to determine the edge weight and the exhibition area correlation degree, the spatial efficiency index and the dynamic line rationality index are generated in combination with an analytic hierarchy process and multi-dimensional analysis, and the defect that a space utilization rate core evaluation index is not established in the prior art is overcome.

The present disclosure relates to a system (102) and method (500) for analyzing network performance parameters. A user interface module (212) enables reception of comprehensive network speed test requests that specify multiple network performance parameters for analysis. A test execution module (214) triggers coordinated operation of multiple testing units (302a, 302b, 302c) to perform simultaneous network speed tests across different platforms. A data collection module (216) systematically aggregates the generated performance information from all testing units. One or more processors (202) transform the collected information through advanced processing algorithms to generate standardized performance data for each network parameter. An analyzing module (218) performs multi-dimensional analysis of the processed data based on specific attributes to provide comprehensive network performance insights. The integrated approach enables automated cross-platform testing, unified data collection, and sophisticated analysis of network performance characteristics, offering significant advantages over conventional single-platform testing methods.

The invention relates to the technical field of voice recognition, in particular to a voice interaction system and method for customer service based on artificial intelligence, and the system comprises a voice input processing module, an intention classification and routing module, a context dynamic adjustment module, a user behavior learning module, a multi-level intention fusion module and a final result module. According to the method, a multi-dimensional feature system is constructed by extracting tone intensity, speech speed frequency and emotional fluctuation amplitude, intention categories, priority weights and confidence scores are generated to realize accurate acquisition of appeals, and dialogue history, context and emotional change dynamic reconstruction path nodes, switching rules and response time sequences are tracked during interaction. Historical behavior mining preference features, habit fusion intention relevance, emergency calculation of an optimal strategy, construction of service steps, resource allocation schemes and execution timelines, adjustment of an interactive interface, a service process and a feedback mechanism according to multi-dimensional analysis, guarantee of differentiated service experience, and improvement of response accuracy and user satisfaction.

The invention discloses a marine ecological protection and restoration project comprehensive evaluation method based on multi-dimensional analysis, and relates to the field of marine ecological analysis, and the method comprises the steps: obtaining multi-dimensional monitoring data through constructing a target region map model and setting a monitoring point, and generating an ecological feature vector; calculating a relative distance and a deviation between the monitoring point and the comparison feature vector to obtain a repair expected goodness of fit; screening monitoring points with low goodness of fit and integrating the ecological data set; training and generating an optimized simulation feature vector based on a GAN model; and analyzing the simulation parameters and evaluating the ecological evolution trend to complete comprehensive evaluation. According to the method, multi-dimensional and high-precision ecological assessment is realized, the accuracy of restoration effect prediction and the scientificity of an assessment result are improved, reliable support is provided for a marine ecological protection restoration decision, and the problems of one-sided monitoring, insufficient prediction and the like of an existing method are solved.

The invention relates to the technical field of data processing, in particular to an engineering geological survey data processing method and system, and the method comprises the following steps: obtaining hole site space positioning and lithologic boundary information, collecting a structure segment sequence, extracting resistance change direction classification disturbance behaviors, recognizing node sequence conflicts, and judging hydrological turning positions. Identifying the corresponding relation between the structural section and the water level track, and generating a structural section water level merging mapping table. According to the method, the spatial information, the lithology identification, the sampling level and the penetration resistance of the drilling structure sections are collected and serialized, disturbance identification and non-sampling section classification are performed based on the resistance change trend between the sections, and node conflicts are identified in combination with the numbering relation and the lithology combination; continuous modeling and classified merging of geological information on the spatial level are achieved, the linkage adaptability of data in a multi-dimensional analysis scene is enhanced, misjudgment interference caused by information isolation is avoided, and dynamic recognition and hydrological response analysis of a geological structure are supported.

The invention provides a multispectral-based early fire point identification method and system, and the method comprises the steps: carrying out the multi-dimensional data collection of an early fire point monitoring region, obtaining a fire point monitoring original data set, carrying out the data preprocessing, obtaining a fire point monitoring data set, carrying out the feature extraction of the fire point monitoring data set, and obtaining a multi-dimensional fire point feature data set; early fire point recognition is carried out based on the multi-dimensional collaborative fire point recognition strategy and the multi-dimensional fire point feature data set, an early fire point recognition result is obtained, multi-dimensional analysis is carried out on early fire points by introducing multi-dimensional data, the early fire point recognition accuracy is improved, an early fire point verification decision is executed according to the early fire point recognition result, and the accuracy of early fire point recognition is improved. And performing early fire point verification based on the early fire point verification decision, obtaining an early fire point verification result, and optimizing the multi-dimensional collaborative fire point identification strategy according to the early fire point verification result, thereby improving the accuracy and stability of early fire point identification.

The invention discloses an automatic mural protection and repair area identification method and system based on image analysis, and the method comprises the steps: generating degradation states of a mural at different time nodes through an image simulation algorithm, converting a two-dimensional damage image into a three-dimensional space model through the combination of a three-dimensional mapping technology, and precisely calibrating a high-risk damage point. The clustering analysis algorithm further integrates and disperses damage points, the property and spatial features of a damage group are extracted, and the curve feature detection algorithm automatically identifies damage boundaries by analyzing texture continuity and line trend. Dynamic sequence analysis tracks inter-frame differential change, a degradation extension track is disclosed, damage information is efficiently compressed and stored by a coding technology, and a matched degradation mode is mined and a potential damage area is updated by historical comparison analysis. According to the method, three-dimensional space modeling is taken as a core, multi-dimensional analysis and efficient storage are fused, accurate positioning, dynamic monitoring and preventive protection of mural damage are realized, and the scientificity and efficiency of cultural relic protection are remarkably improved.

The invention provides a privacy protection advertisement monitoring method, system and device and a medium, and is used for solving the problem of effectively monitoring dynamically generated advertisement contents on the premise of protecting user dialogue context privacy in a generative artificial intelligence application. The method comprises the steps that firstly, an advertisement text and context information are obtained, and a first vector is obtained through semantic embedding processing; performing privacy protection conversion on the vector by adopting a random projection method to obtain a second vector, wherein the conversion can reduce the dimension and scatter semantic distribution; inputting the processed vectors into a pre-trained multi-dimensional analysis model to obtain real-time analysis results of content compliance, content correctness, emotional tendency, creativity quality and the like; and finally summarizing analysis results according to advertisement activities and outputting a report. According to the method, differential privacy noise can be selectively added, and a delivery decision is generated based on a multi-threshold judgment model. According to the invention, the privacy of the user is protected, and the advertisement content is efficiently monitored in real time.

The invention discloses a non-natural earthquake phase identification method based on image processing, and relates to the technical field of image processing, and the method comprises the following steps: capturing a continuous signal with the amplitude changing with time in a seismic wave propagation process in real time based on a sensor distributed by an earthquake monitoring station network, forming an original amplitude sequence, and carrying out the calculation of the original amplitude sequence; collected continuous signals are converted into three-channel images, and the image brightness is automatically adjusted according to the seismic wave data dynamic range. According to the method, a seismic wave multi-dimensional analysis system is constructed through a three-channel construction strategy, the first channel clearly shows the time domain difference between natural seismic progressive fluctuation and non-natural seismic pulse type sudden change, and the second channel accurately captures microsecond-level sudden change which is easy to neglect in traditional time domain analysis; the third channel clearly distinguishes natural earthquake low-frequency energy concentration and non-natural earthquake intermediate-frequency characteristic spectral lines, and the adaptive brightness adjustment technology aims at the intensity difference of multi-source earthquake signals, improves the distinguishing degree of non-natural and natural earthquake edges, and builds a recognition foundation firmly.

The invention relates to the technical field of production quality control, and discloses a production data hierarchical processing and quality tracing system. The system comprises a production data acquisition module, a data hierarchical processing module, a quality fluctuation index module, a multi-dimensional quality difference analysis module and an abnormal source positioning module. The production data acquisition module acquires a real-time parameter data sequence of each production link; the data layering processing module is used for layering the data into a plurality of data hierarchies; the quality fluctuation index module obtains a quality fluctuation index based on the fluctuation condition of the data hierarchy; the multi-dimensional quality difference analysis module analyzes the multi-dimensional quality difference based on the index; and the abnormal source positioning module positions a production abnormal source based on the multi-dimensional quality difference. According to the system, through hierarchical data processing, quality fluctuation quantification and multi-dimensional difference analysis, accurate positioning of a production abnormal source is realized, and systematicness and accuracy of production data processing and quality tracing are improved.

The invention relates to the technical field of large model service tracking, and discloses a large model service chain type calling tracking system and a root cause analysis method, and the method comprises the steps: carrying out the semantic coding of the input and output of a large model, generating a final fingerprint, and carrying out the cross-Span fingerprint inheritance and extension; semantic uncertainty is calculated and analyzed, and the sampling rate is dynamically adjusted; based on sampling data acquired by a sampling rate, fusing a call chain topological structure and a time decay factor, constructing a dynamic space-time atlas, optimizing an atlas index, constructing a multi-dimensional analysis framework, and realizing positioning from surface anomaly to deep root cause in combination with an anomaly propagation contribution degree obtained by calculation; carrying out statistics on distribution parameters of local indexes and carrying out differential privacy processing, and calculating a global threshold value of the distribution parameters and carrying out issuing execution; and based on a final positioning root cause obtained by global threshold analysis, evaluating a root cause repair effect and performing model iteration, and converting historical root cause data into a driving force for system optimization.

The invention particularly relates to a spatial analysis method based on large model agent driving. According to the spatial analysis method based on large model agent driving, syntactic analysis and intention recognition are carried out on a spatial problem described by a natural language through a large model, and spatial-temporal clustering, network analysis and a field simulation algorithm module are dynamically matched; packaging a heterogeneous algorithm into a standardized service component based on a model context protocol (MCP), and automatically constructing an analysis process chain; and integrating a GIS engine and a visual template library, generating a dynamic thematic map according to the processed spatio-temporal data through the visual engine, and superposing multi-dimensional analysis annotations. According to the spatial analysis method based on large model agent driving, the full-chain intelligent capability from data processing to decision support is constructed, the efficiency and precision of spatio-temporal evolution modeling are remarkably improved, meanwhile, the system integration and maintenance cost is reduced, the method is suitable for high-complexity scenes needing real-time spatio-temporal data analysis, and the method is suitable for popularization and application. And the method has remarkable technical innovation and industrial application value.

The invention provides a dam safety monitoring data analysis method and system based on a multi-modal knowledge graph and a large language model, and belongs to the technical field of calculation models. The method comprises the following steps: collecting multi-modal data of dam monitoring, and preprocessing the multi-modal data; constructing a dam safety analysis knowledge graph by adopting a water conservancy object multi-modal knowledge graph construction mode; an intelligent positioning function is used for finding an associated object for the monitored abnormal signals, information of the associated object is obtained, and a dam analysis model is used for association analysis; generating an abnormal associated sub-graph; and forming a multi-dimensional analysis report conclusion by using a pre-trained water conservancy professional knowledge large language model in combination with cue words and user questions. Multi-aspect information generated in the dam safety management process is integrated by utilizing the knowledge graph to form a comprehensive information network, efficient query and reasoning are realized by virtue of a graph computing technology, and a monitoring analysis result for explaining the dam is clearly, intuitively, accurately and comprehensively presented.

The invention relates to the technical field of steel structure assessment, and discloses a steel structure connection risk assessment system and method based on industrial data analysis, and the system comprises a multi-dimensional collection module and an intelligent analysis module. According to the steel structure connection risk assessment system and method based on industrial data analysis, management data of all components of a steel structure, inspection data of all joints and sensing data of all environmental factors are acquired through a multi-dimensional acquisition module and are classified to form a data set, and an intelligent analysis module analyzes the assemblability of each component and generates a performance index; the weight distribution proportion can be adjusted according to actual engineering requirements, multi-dimensional analysis is flexible and high in applicability, and an intelligent analysis module analyzes the erosion effect of environmental factors on the performance of the steel structure, generates attenuation coefficients, sets threshold values in a fixed range and evaluates whether the steel structure has potential risks or not; and targeted suggestions are provided, preventive maintenance is realized, and the intelligent evaluation precision is high.

The invention provides a method for predicting the working failure rate of a railway vehicle relay, and the method comprises the steps: classifying data according to a preset dimension to generate a structured feature set, constructing a time sequence feature matrix, and defining a multi-dimensional analysis coordinate system based on the classification dimension of the feature set; determining an initial prediction constraint condition according to the relay life distribution model and the fault mode library, executing dynamic prediction simulation on the feature set based on the time sequence feature matrix, simulating degeneration behaviors of the relay under various working conditions through a state transition simulation engine, generating an initial fault rate curve, and calculating the fault rate of the relay according to the initial fault rate curve. And finally, carrying out iterative correction on the initial fault rate curve by adopting an integrated optimization algorithm to generate a multi-dimensional fault rate prediction model. The invention further provides a system for predicting the working failure rate of the railway vehicle relay. According to the method, the adaptability and accuracy of prediction are greatly improved, and the degeneration behavior of the relay under the actual complex working condition can be simulated more truly.

The invention relates to a multi-modal data fusion 3C assembly process quality accurate prediction method, and belongs to the field of 3C intelligent manufacturing, and the method comprises the steps: carrying out the multi-modal data fusion, and forming a unified feature space containing process parameters and quality characterization; performing multi-dimensional analysis on process quality, performing hierarchical analysis on structured process parameters and unstructured images, and establishing cross-modal feature mapping; predicting the process quality, evaluating the contribution degree of process parameters to the quality by utilizing an integrated learning model based on a fused feature set, screening key parameters to reduce the complexity of the model, dynamically adjusting the prediction model, designing a'sliding window + multi-dimensional index 'monitoring system, and evaluating the model performance and the data distribution drift in real time. Establishing a time-performance-distribution three-dimensional trigger mechanism, and starting an adjustment strategy according to different priorities; a'cleaning-complementation-mechanism-mapping-self-adaption 'closed loop is realized, and the timeliness and accuracy of 3C assembly quality prediction are remarkably improved.

The invention provides an archive classification and filing management method based on digital economy and data governance, and relates to the technical field of computer data processing, and the method comprises the following steps: 101, establishing a standardized system; 102, constructing a unified data acquisition platform; 103, performing data auditing and returning processing; 104, transferring and archiving the data; and 105, data multi-dimensional utilization. According to the invention, standardized integration of full-service data is realized, heterogeneous confusion is eliminated, and data islands are radically treated; through automatic acquisition verification, manual auditing and intelligent return closed loop, completeness and availability are ensured, and the quality of the whole life cycle of data is guaranteed; and secondly, through intensive storage and multi-dimensional analysis, supporting transaction trend prediction, archiving monitoring and business decision, directly driving enterprise data governance and digital economic transformation, and enabling data value release.

The invention relates to the technical field of large models, in particular to a reading recommendation large model generation method and system based on big data resources. The method comprises the following steps: acquiring a to-be-assessed text, collecting reading materials with multiple learning segments and multiple styles, labeling the reading materials, carrying out aggregation and consistency verification on labeling results, and constructing a verified recommendation database; performing low-rank adaptive fine tuning on the general pre-training large model based on the recommendation database to obtain a domain model, and performing performance and deviation monitoring and correction in the training process; performing multi-dimensional analysis on a to-be-evaluated text to generate a text feature vector, and performing mixed retrieval on a recommendation database in combination with semantic vector similarity retrieval; according to the invention, through the construction of the large reading recommendation model, the reading recommendation is more accurate and efficient.

The invention relates to the field of intelligent work order dispatching, in particular to a distributed work order management system based on intelligent dispatching. The invention provides a distributed work order management system based on intelligent assignment, and the system comprises an organizational structure engine module which employs a father-child node tree structure, divides operation and maintenance regions according to the three levels of province, city and district, and carries out the node binding of the responsible person authority level of the region; the intelligent order dispatching module is used for realizing automatic matching of the work order and the operation and maintenance personnel through a preset algorithm; the data analysis module collects work order data in real time to generate a multi-dimensional analysis chart, and predicts the regional fault probability through machine learning; and the early warning feedback module is used for setting an SLA threshold value and automatically upgrading and alarming an overtime work order. The work order system realizes digital management through an automatic and standardized process, greatly reduces the time consumption cost of offline communication, and improves the operation and maintenance efficiency.

The embodiment of the invention provides a multi-dimensional detection method and system for steam-water pollution, and the method comprises the steps: setting sampling points at target positions of condensed water, feed water, boiler water and a steam system, and detecting sampling parameters at the sampling points; identifying a water quality abnormal signal based on the sampling parameter and a preset threshold value of the water quality index; associating the water quality abnormal signal with target equipment and carrying out chemical analysis so as to position a pollution source; monitoring the thermotechnical state of the target equipment in real time, collecting thermotechnical parameters, and establishing a linkage model of the thermotechnical parameters and the water quality indexes to identify the influence of abnormal working conditions on the water quality; according to the influence result of the abnormal working condition on the water quality, the water delivery module, the shaft seal module and the dosing module are subjected to sealing performance test and purity detection so as to identify hidden source abnormal signals; and performing multi-dimensional analysis on the water quality abnormal signal, the pollution source, the abnormal working condition and the hidden source abnormal signal, and building a fault tree to generate a pollution source priority list and dynamically adjust a pollution discharge strategy.

The invention provides a hydraulic engineering prevention and control information digital management method and system, and the method comprises the steps: firstly obtaining a real-time data stream, extracting overflow risk features through employing a data fusion technology, and then screening out a core feature index from the overflow risk features through constructing a feature weight analysis logic; then, performing anomaly detection on the core characteristic indexes, and when an abnormal index is detected, generating a dynamic risk distribution diagram corresponding to the abnormal index and determining an influence range so as to predict a change trend of the abnormal index in a future target time period in a risk distribution area and lock a risk evolution time interval; and finally, carrying out comprehensive risk analysis based on the abnormal indexes and the initial characteristic indexes associated with the abnormal indexes, and generating standardized early warning information for outputting. According to the method, real-time monitoring, multi-dimensional analysis and accurate early warning of the overflow risk of the target reservoir are achieved, the response efficiency of water conservancy risk early warning is improved, and then the safety management level and the disaster prevention and reduction capacity of the water conservancy project are effectively improved.

The invention discloses a data aggregation processing method and device, computer equipment and a storage medium, belongs to the technical field of big data, and is applied to data aggregation in the financial field. The method comprises the following steps: firstly, grouping dimension fields by utilizing a mapping rule and a hierarchical clustering algorithm; and secondly, dynamic grouping mapping is established, one-time scanning is performed on the source data table, and each data record is directly attributed to a corresponding grouping structure, so that the data processing efficiency is improved. Meanwhile, a correlation index of the index and the data partition is constructed based on a Hash mapping algorithm. Under the support of an indexed data structure, the aggregation calculation function can efficiently execute operations such as summation and averaging, and rapid aggregation of multi-dimensional data is realized. Generally speaking, according to the method, the correctness of an aggregation result is ensured, meanwhile, the calculation and storage pressure of the system is remarkably reduced, the timeliness and expandability of data analysis are improved, and the method is particularly suitable for real-time or quasi-real-time multi-dimensional analysis requirements in a large-scale business scene.

The invention relates to an electric power resource adjustability resource multi-dimensional analysis and visualization method, which comprises the following steps of: selecting four dimensions of response time, service time, adjustable capacity and credibility in electric power resource adjustability characteristic indexes as a rating system, and drawing an electric power adjustability resource adjustability characteristic radar map; and constructing a power adjustable resource characteristic cube by taking the power adjustable resource adjustment characteristic radar map as a bottom surface and taking the corresponding scale capacity as a high surface to represent the adjustable characteristic characteristics of the source network load power resources. Compared with the prior art, the multi-dimensional visualization of the adjustment performance of the power adjustable resource is realized by constructing the characteristic cube, the adjustable characteristic characteristics of the source network load power resource can be comprehensively and accurately represented, and a large number of, multiple and dispersed flexible resources can be aggregated to participate in system adjustment.

The invention discloses a fund portfolio analysis method and device and a storage medium, and relates to the technical field of data analysis, and the method comprises the steps: based on fund product metadata, fund underlying asset data and market reference data, executing fund deep analysis including performance index analysis, position holding analysis and attribution analysis, and obtaining a deep analysis result; based on the user fund portfolio position data and the deep analysis result of each fund product, performing combination-level multi-dimensional analysis on the fund portfolio to generate an overall analysis result of the fund portfolio; according to the fund portfolio analysis method, multiple target expert agents corresponding to the fund portfolio are determined, a preliminary analysis report output by each target expert agent is obtained, the preliminary analysis reports are integrated and summarized, a corresponding fund portfolio analysis report is generated, and the technical problem that an existing fund analysis method is lack of intelligent integration due to data piling is solved. Depth, integration and generalization of fund portfolio analysis are realized, and a customized analysis report with a low interpretation threshold is provided for a user.

The invention discloses an unmanned vending equipment state monitoring method and system based on streaming and batch integration, and the method comprises the steps: collecting the multi-dimensional state data of equipment through a data collection layer, and carrying out the unified structural processing based on a data model; the streaming processing layer acquires a structured equipment state event stream from a Kafka data source, performs real-time processing through timestamp allocation, data grouping and a window aggregation strategy, and calls a multi-dimensional health scoring algorithm to calculate a real-time health score; the storage layer adopts a streaming-batch integrated architecture, stores the data to corresponding data tables respectively, and meets the requirements of real-time query and historical analysis; the analysis query layer performs data aggregation calculation based on the aggregation table to obtain an analysis result; and the intelligent decision-making layer calls an anomaly detection algorithm to carry out anomaly judgment, and equipment health assessment and predictive maintenance are realized. The problems that an existing monitoring system is insufficient in real-time processing, poor in storage adaptability, lack of intelligent prediction, weak in multi-dimensional analysis and limited in expansibility are effectively solved.

This invention relates to a method, apparatus, and medium for automatically generating test cases by integrating multi-source human programming experience. The method includes: a preparation step: based on a target programming problem P, searching for existing correct human programming code for the target programming problem P, as well as human error submission code and human correction code for each error submission; a multi-dimensional analysis step: obtaining a first analysis result generated by a large language model; a difference analysis step: obtaining a second analysis result generated by the large language model; and a test case generation step: based on the first and second analysis results, generating executable test script construction prompts to obtain a test script generated by the large language model, and running the script to generate test cases for the programming problem P. Compared with existing technologies, this invention has advantages such as significantly improving the discriminative power of test cases.

The present invention provides a novel integrated platform capable of verifying the production process, standardizing and evaluating complex natural products, thereby enabling "as an integral system" characterization of complex mixtures contained in products (based on or consisting of natural substances) with therapeutic and / or health care effects, the characterization of the metabonomics is not limited to a reductive chemical method aiming at a single marker, and qualitative and quantitative data and fingerprint characterization metabonomics of the mixture are integrated with physical and biophysical activity data, so that the characterization of the product is realized, which is the basis of the verification process of the production process; and the reproducibility of different batches of products in the aspects of efficacy and safety can be ensured. The analysis platform described herein can ensure that a complex natural product is a whole, and all quality and efficacy characteristics of the product are considered, thereby ensuring reproducibility of effect and safety through a multi-dimensional analysis method.