265 results about "Hierarchical analysis" patented technology

The invention provides an enterprise process intelligent analysis system based on a large language model. The enterprise process intelligent analysis system comprises a master control scheduling module, a data preprocessing module, a hierarchical analysis module, an insight extraction module, a report generation module and a knowledge retrieval module. The master control scheduling module generates a scheduling plan based on chain thinking reasoning, and dynamically calls each module; the data preprocessing module carries out cleaning and structured conversion on the enterprise event logs and outputs standardized JSON (JavaScript Object Notation) data; the knowledge retrieval module is combined with an RAG technology and a vector database to provide context support for a large language model; the hierarchical analysis module drives a model to execute process discovery and bottleneck identification through a structured cue word template; the insight extraction module converts an analysis result into a commercial insight text containing reasons, influences and suggestions, and has a self-repairing mechanism to guarantee consistency; and the report generation module automatically generates an image-text report. The system can improve the efficiency and accuracy of process analysis.

The invention discloses a long document intelligent retrieval method and system based on hierarchical analysis and multi-modal fusion, and the method comprises the following steps: carrying out hierarchical analysis on an input long document, and obtaining a physical layout structure, a logic structure and a division and association relationship of multi-modal elements of the long document; respectively extracting text features of the text elements and visual features of the multi-modal elements; performing deep fusion on the text features and the visual features through a cross-modal fusion model to generate a unified multi-modal semantic representation vector; constructing a hybrid index; receiving user query, and performing preliminary retrieval by using the hybrid index to obtain a candidate result set; reordering the candidate result set; and outputting the retrieval result after reordering. Through visual-logic collaborative analysis, gating enhanced bilinear attention fusion and graph attention-based context reordering, a collaborative gain effect is generated in a long document intelligent retrieval task, and the retrieval precision and the user experience are remarkably improved.

The invention discloses a ship collaborative collision avoidance decision-making method based on a multilayer coding genetic algorithm. The method comprises the following steps: obtaining and processing related information of each ship in an encounter situation based on a sensor; based on a collision risk degree model, constructing a dynamic early warning mechanism in a multi-ship encounter scene: constructing a multi-stage ship collision avoidance strategy based on a queuing theory theoretical framework, establishing a multi-ship collision avoidance decision queuing system with specific space-time coupling characteristics, introducing a dynamic time window distribution mechanism and a space-time constraint coupling network modeling method, and establishing a multi-ship collision avoidance decision queuing system; multi-objective optimization is carried out on the ship decision scheme of the first priority gradient, and a ship intelligent collision avoidance strategy with dynamic adaptability is provided; a multi-ship collaborative collision avoidance decision optimization model is constructed on the basis of a genetic evolution mechanism, a multi-target adaptation function is integrated, a decision scheme when multiple groups of ships meet in a first priority is determined through the synergistic effect of dynamic priority management and multi-layer coding optimization, and hierarchical analysis of decisions in a multi-ship meeting situation is achieved.

The invention discloses a complex system data visualization modeling method based on GIS and topological graph fusion, and relates to the technical field of data visualization modeling, and the method comprises data fusion modeling, space-time anomaly reasoning, state propagation modeling and multi-dimensional visualization enhancement. The method comprises the following steps: performing unified access, cleaning and standardization processing on operation data, topological relation data, geographic space data, function configuration data and environment data of a complex system, and constructing a unified data model fusing geographic attributes and topological attributes; realizing abnormal root cause reasoning and spatial constraint propagation modeling of a system state by using a convolutional time sequence modeling method combined with a graph neural network; a dynamic evolution rule of a node state is described through a time gating mechanism, and interactive display is realized; according to the method, global visualization of the operation state of a complex system, dynamic backtracking of an abnormal propagation path and hierarchical analysis of key nodes can be realized, and the intelligent monitoring, situation awareness and decision support capabilities of the system are improved.

The invention provides an artificial intelligence semantic processing system and method oriented to digital media creation, and relates to the technical field of artificial intelligence semantic process.The artificial intelligence semantic processing method comprises the steps that predicate argument relation pairs of language texts are extracted, object space relation pairs of sketch images are extracted at the same time, and a basic semantic unit set is constructed; the integrity and accuracy of cross-modal semantic understanding are ensured, further, semantic units are clustered by using a dynamic routing algorithm, a semantic concept cluster with a clear importance weight is generated, deep mining and structured representation of creation intentions are realized, and the creation intentions are quickly and accurately understood. An initial semantic relation graph is constructed, a graph attention network is used for dynamic reweighting, finally, an enhanced dynamic semantic graph is generated, complex association and a hierarchical structure between semantic concepts are effectively captured, finally, hierarchical analysis is carried out on the semantic graph, and a structured semantic blueprint is output, so that the dynamic semantic graph is obtained. And a reliable semantic processing technology is provided for creation of high-quality digital media contents.

The invention discloses a low-altitude take-off and landing infrastructure grading site selection method, device and equipment, and relates to the technical fields of low-altitude economy, urban planning and the like, and the method comprises the steps: obtaining a low-altitude scene demand, and carrying out the quantification processing, and obtaining demand data; a facility grading system is established based on the demand data, configuration data corresponding to the facility grading system is obtained, and the facility grading system comprises at least three grades; performing overlay analysis on the limiting factor data, and determining a candidate region; and performing hierarchical analysis on the candidate region to obtain suitability distribution data under the low-altitude scene requirement, and performing solution based on the suitability distribution data and the configuration data to obtain site selection data corresponding to the facility grading system.

The invention relates to the technical field of crop image quality inspection, and discloses a crop sample live-action image automatic warehousing and intelligent quality inspection system and method. An image acquisition module of the system acquires an initial real-scene image and preprocesses the initial real-scene image to generate a target real-scene image, and shooting interference is eliminated to guarantee the quality of basic data; the hierarchical analysis module performs multi-level feature analysis on the target live-action image, generates initial quality calibration, and divides the image into a plurality of sub-quality inspection areas when to-be-verified calibration is identified, so that accurate analysis from the whole to the local is realized; and the dynamic processing module extracts spectral feature values of the sub-regions, compares the spectral feature values with a standard threshold value, divides a feature set, performs correlation verification through adjacent feature values and performs statistics on an effective correlation number, so that the scientificity and objectivity of quality inspection are improved. According to the system, automatic warehousing and intelligent quality inspection of crop sample images are realized, manual intervention is reduced, the quality inspection efficiency and reliability are improved, and high-quality data support is provided for related work of national land change investigation.

The invention relates to the technical field of language barrier evaluation, and discloses a deep learning-based language barrier executive intervention effect evaluation method. The method comprises the following steps: acquiring real-time voice data and facial expression data of a language barrier patient in intervention training through a multi-modal data acquisition device to form an original behavior feature set; performing acoustic feature hierarchical analysis on the voice data by adopting a time sequence feature extraction network to generate a voice time sequence feature vector; performing micro-expression dynamic capture on the expression data through a three-dimensional convolutional neural network to generate an expression state feature vector; inputting the two types of vectors into a multi-modal feature fusion layer to carry out cross-modal correlation analysis, and generating a comprehensive behavior evaluation matrix; on the basis of the matrix, an intervention effect analysis model driven by an attention mechanism is adopted, a behavior improvement degree index of the current intervention stage is calculated, accurate evaluation of the intervention effect is achieved, and support is provided for dynamic adjustment of language barrier rehabilitation intervention.

The invention provides a remote control method and device for intelligent multimedia interaction equipment, equipment and a medium, which are used for improving the real-time performance and the execution success rate of remote control and enabling complex multimedia interaction to be reliably operated on a heterogeneous network and heterogeneous equipment. The method comprises the following steps: carrying out hierarchical analysis processing on interaction data input by a user to generate perception command data, and carrying out execution planning and resource allocation on the perception command data according to equipment performance characteristics to generate an executable operation sequence, and performing transmission quality evaluation and path selection on the propagation path network according to the executable operation sequence to obtain a target transmission path, and performing protocol encapsulation on the executable operation sequence according to the target transmission path to obtain a remote control instruction data stream.

The invention discloses a fault information acquisition method and system for transformer substation relay protection equipment. The method comprises the following steps: acquiring a multi-source fault data sequence of relay protection equipment; generating a fusion fault feature sequence through a multi-source data fusion strategy; identifying a key fault period based on a dynamic sudden change threshold, and intercepting target and non-target data subsequences; the morphological difference degree of adjacent target subsequences is calculated, if the morphological difference degree exceeds a threshold value, the adjacent target subsequences are marked as a doubt sequence, and fault feature analysis is carried out by adopting a hierarchical analysis strategy; and further carrying out fault wave and range judgment on adjacent non-target subsequences. Through the multi-source information fusion and dynamic threshold technology, the limitation of a single data source and a fixed threshold is overcome, early faults and hidden defects can be accurately recognized, normal operation and real faults can be effectively distinguished, the accuracy, reliability and early warning capability of fault recognition are remarkably improved, and the method is suitable for popularization and application. The method is suitable for state monitoring and fault diagnosis of intelligent substation relay protection equipment.

The invention relates to the technical field of call center control, and discloses an unmanned vehicle formation control and obstacle avoidance reconstruction system based on complex road conditions. The system call data acquisition module is used for acquiring multi-source interaction data in real time; the dynamic feature extraction module is used for performing semantic intention hierarchical analysis on the voice stream text conversion data, performing event fragment segmentation on the user behavior time sequence data, and generating an operation track feature vector; the scene clustering engine module is used for performing space-time alignment on the semantic intention hierarchical analysis result and the event fragment segmentation result to generate a multi-dimensional scene clustering tag; the strategy matching core module is used for retrieving an interaction strategy set matched with the current scene from a distributed strategy library according to the multi-dimensional scene clustering label and generating a dynamic control instruction sequence; and the instruction distribution execution module is used for distributing the dynamic control instruction sequences to corresponding seat terminals and voice routing equipment after sorting the dynamic control instruction sequences according to priorities.

The invention relates to the field of data processing, and provides a software cost method and system based on dynamic configuration and hierarchical analysis. The method comprises the following steps: dynamically loading corresponding configuration parameters and algorithms by evaluating a method identification field; constructing a standardized cost parameter configuration system, including configuring an estimated function point reference value, an estimated function point weight coefficient and a scale adjustment factor; based on the evaluation method, a tree budget configuration table is preset, hierarchical node design is adopted, a budget tree is constructed, and a tree budget configuration and cost declaration / evaluation header is formed; transferring the configuration right of the tree budget configuration and the cost declaration / evaluation header to the back end, so that the back end dynamically loads a preset header template according to the enabled cost standard, and synchronously injects field attribute rules and calculation formula metadata; obtaining an unstructured document uploaded by a user, and analyzing the unstructured document and the header template by adopting a large language model to obtain software cost details; and generating an evaluation report through the expert review software cost details.

The invention belongs to the technical field of enterprise information technology resource management, and provides a tool performance evaluation method and device, and the method comprises the steps: carrying out the data preprocessing of a pre-constructed tool performance evaluation index pool matrix, and outputting a standardized matrix; n is the number of tools, and p is the number of selected indexes; performing PCA dimension reduction operation on the standardized matrix to obtain a principal component score matrix and a load matrix; d is the dimension after dimension reduction; performing k-means clustering on the principal component score matrix and the load matrix, outputting cluster division, and generating a clustering label for each cluster; and on the basis of an AHP analytic hierarchy process, tools in each cluster are sorted respectively. According to the method, the tools are grouped based on the clustering result through a clustering independent ranking mechanism, so that the intra-group homogeneity and inter-group difference of the tools are obvious, misjudgment of heterogeneous tools, monopoly sorting of dominant clusters and mismatching of resources are avoided, and the global ranking stability is improved.

The invention discloses a self-adaptive compensation system based on cutter wear of a numerical control machine tool, and particularly relates to the field of machining precision control of the numerical control machine tool, and the self-adaptive compensation system comprises a multi-dimensional data acquisition module, a progressive data processing module, a hierarchical analysis module, a multi-stage compensation output module, a data storage module and a self-diagnosis module. The multi-dimensional data acquisition module synchronously acquires cutting force, spindle dynamic state and vibration parameters; the progressive data processing module performs preprocessing, feature extraction and data fusion on the acquired parameters; the hierarchical analysis module is used for realizing wear grade evaluation through single-mode identification, multi-mode association verification and fuzzy comprehensive evaluation; the multi-stage compensation output module outputs a state snapshot and a graded compensation instruction and predicts the service life of the cutter; the data storage module adopts local and cloud dual backup to store full-process data; and the self-diagnosis module detects the state of each module and pushes a diagnosis report when the state is abnormal. According to the system, the wear evaluation precision and real-time performance are improved, the misjudgment rate is reduced, and the machining stability is guaranteed.

The invention provides an electric two-wheeled vehicle topological structure optimization method and system based on a W algorithm, and relates to the technical field of electric two-wheeled vehicle topological structure optimization. The method comprises the following steps: collecting performance data, structural parameter data and working condition data of the whole electric two-wheeled vehicle and key parts; and constructing a topological structure initial model based on the improved convolutional neural network, and inputting core features to output a preliminary topological design scheme. And constructing a topology scheme evaluation model based on a fuzzy analytic hierarchy process, and obtaining a comprehensive evaluation score of the preliminary topology design scheme. And constructing a multi-target topological optimization model, and solving to generate a plurality of groups of candidate topological optimization schemes. And solving to obtain an optimal topological structure scheme. According to the method, the multi-target model is used, the W algorithm is used for solving, the improved convolutional neural network and the fuzzy analytic hierarchy process are combined, the performance cost balance is achieved, the design efficiency is improved, the scheme practicability is enhanced, the algorithm framework is extensible, data can be reused, and enterprise innovation is assisted.

The invention relates to the technical field of cross-border supply chain optimization based on LLM, and discloses an LLM-based cross-border supply chain optimization method and device, electronic equipment and a medium, the method comprises the steps of performing hierarchical analysis on related legal texts, accurately extracting legal rule features, mapping the legal rule features into a preset mathematical space, forming mathematical constraints, and establishing a mathematical model; and automatically identifying and optimizing the initial suppliers which do not conform to the mathematical constraints, and optimizing the supply chain. The method has the advantages that legal risks are reduced, transparency and flexibility of the supply chain are improved, cost is saved, efficiency is improved, legal requirements can be automatically applied to supply chain management, and efficiency and accuracy of compliance auditing are greatly improved.

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 discloses an Internet of Things alarm root cause analysis method based on gas Internet of Things construction, and belongs to the technical field of gas pipe network safety monitoring. The method aims at solving the problems that in the prior art, alarm root cause analysis is low in efficiency and poor in accuracy, and particularly composite alarms are difficult to process. The method comprises the steps that firstly, multi-dimensional data such as alarms, equipment attributes, pipe network topology and historical cases are comprehensively collected and subjected to standardization processing; then, a dual-drive hybrid reasoning strategy is adopted, rule reasoning based on a knowledge graph and case reasoning based on a machine learning model are operated in parallel, two paths of results are subjected to weighted fusion, and candidate root causes are generated; particularly, aiming at composite alarms generated in a short time, a complex alarm disassembling module is used for carrying out hierarchical analysis on time sequence and space dimensions, and core influence factors are positioned based on dynamic weight calculation. The most innovative part of the method is that a closed-loop feedback mechanism can be constructed according to a final verification result of field operation and maintenance personnel, and dynamic weights of influence factors are automatically updated, so that self-adaptive optimization of an analysis model is realized. According to the method, the accuracy and efficiency of alarm root cause positioning are remarkably improved, and self-learning and evolution of the system are realized.

The invention relates to an industrial production data AI risk assessment method and system based on multi-dimensional correlation analysis. The method comprises the steps of obtaining operation state data of a target production line at multiple time points and performing primary processing to obtain a standardized data set; performing multi-dimensional correlation analysis on equipment operation parameters, process flow parameters and environment monitoring parameters in the standardized data set, inputting a risk factor set into a multi-dimensional data analysis model for hierarchical analysis, generating risk grade distribution information, and realizing accurate grading of risks; performing priority ranking and deep analysis on each risk factor according to the risk level distribution information, wherein a generated risk coping strategy suggestion comprises a specific adjustment scheme for the high-risk factor and an implementation sequence of the specific adjustment scheme; converting the risk coping strategy suggestion into an optimization control instruction and sending the optimization control instruction to an industrial control system; according to the scheme, real-time optimization of the operation state of the production line can be achieved, risks in the operation process of the production line are effectively reduced, and production efficiency and product quality are improved.

The invention relates to a testing method and device for a network-forming type energy storage converter, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a plurality of control instructions of a network-forming type energy storage converter, respective parameter value ranges of a plurality of first control parameters of power grid simulation equipment and respective parameter value ranges of a plurality of second control parameters of environment simulation equipment; generating a plurality of control data sets; based on the plurality of control data sets, controlling the operation of the network-forming energy storage converter, the power grid simulation equipment and the environment simulation equipment, and based on the data acquisition equipment, obtaining a test data set; and determining a test result corresponding to the test data set based on a pre-constructed analytic hierarchy process model. By adopting the method, the performance of the network-forming type energy storage converter under different power grid working conditions can be comprehensively and accurately tested.

The invention relates to the technical field of edge computing resource management, in particular to an edge server load evaluation method based on analytic hierarchy process and performance loss. According to the method, firstly, edge servers are divided into a common type, a computing type, a memory type and an I / O type according to heterogeneous types; aiming at each type of edge server, constructing a judgment matrix by adopting an analytic hierarchy process and solving a feature vector so as to calculate load weight coefficients of CPU (Central Processing Unit), memory and disk I / O (Input / Output) resources; calculating a real-time load based on the weight coefficient, wherein the real-time load is the weighted sum of the resource utilization rates; the performance loss is further calculated according to the nonlinear relation between the real-time load and the total load ratio; and finally, restraining the real-time load through the load threshold value, and evaluating the performance state of the server in combination with the performance loss. According to the method, the accuracy and reliability of load evaluation of the heterogeneous edge server are effectively improved, the resource utilization rate is optimized, overload of the server is avoided, and the system stability is enhanced.

The invention relates to an accelerated test system and method based on a weak link of an electronic component, and belongs to the technical field of electronic product testing, and the method comprises the steps: carrying out the hierarchy analysis and modeling preparation of a whole machine structure, and carrying out the structural layering of a target whole machine system; weak link identification: identifying a key failure path influencing the reliability in the whole machine, and determining a weak component; acceleration pre-test: carrying out acceleration pre-test on the identified weak component, and obtaining degradation behavior data of the weak component through multi-stage acceleration stress loading; constructing a complete machine acceleration model, and calculating the influence of temperature stress, electric stress and vibration stress on the failure degradation rate of the product; acceleration factor calculation: calculating an acceleration factor based on the activation energy of the weak link part; and predicting the service life of the whole machine through acceleration factors, failure mode analysis and component service life data. The failure of the whole machine is converted into the failure of a weak link; the service degradation process of the whole machine can be reflected more truly, and the accuracy of a prediction result is improved.

The invention relates to a big language model security detection system, device and equipment based on double-model adversarial evaluation, and the method comprises the steps: carrying out the variation of sensitive vocabularies through a dynamic load variation technology based on user configuration and an attack strategy template, generating diversified attack loads, and carrying out the recognition of a big language model according to the generated attack loads. Attack testing is carried out through a double-model adversarial evaluation architecture, model response content and a security judgment result are obtained, a risk score is calculated through a multi-dimensional quantitative scoring method according to the security judgment result, and a comprehensive security risk evaluation report is generated. According to the method, diversified hidden attacks are generated through a plug-in strategy library and a dynamic load variation technology; a double-model adversarial architecture is adopted, attack and detection roles are separated, an unbiased test closed loop is realized, multi-dimensional indexes are fused based on an information entropy-analytic hierarchy process, a risk assessment value is calculated, a report is generated, and scientific and accurate quantitative assessment of the security of the large language model is realized.

The invention relates to the technical field of artificial intelligence, and discloses a content generation system based on cross-domain multi-agent collaboration and a dynamic regulation and control method. A distributed intelligent agent acquisition module of the system dynamically adjusts the acquisition frequency and range according to a negotiation protocol, and performs de-noising and standardization processing on data to generate a multi-modal data set; the feature hierarchy analysis module is used for dividing a key region and a non-key region according to the feature distribution density and generating a feature hierarchy graph; the cross-modal fusion module establishes a space-time corresponding relationship between surface features and deep features, performs feature interaction calculation, and performs weighted fusion on different levels of features to generate unified feature representation; the intelligent decision-making module generates a parameter sequence according to the unified feature representation generation content, and encodes the parameter sequence into a control instruction; and the adaptive regulation and control module monitors the state change in the generation process in real time, calculates a deviation index of the generation content and an expected target, and dynamically updates the generation parameter sequence based on the deviation index until the deviation reaches a convergence threshold.

The invention relates to the technical field of distribution network scheduling, and discloses a distribution network intelligent scheduling method, system and device based on cloud edge fusion and a medium, and the method comprises the steps: building a unified credit edge access mechanism, and achieving the identity authentication of the device and the data credibility guarantee. The graph neural network is combined with an attention mechanism to extract the node features of the power grid unit, and a basis is provided for intelligent scheduling decision making. A scheduling strategy is designed for a power grid unit by adopting a non-cooperative game mechanism, and a Nash equilibrium strategy is solved through an iterative optimal response algorithm, so that the scheduling strategy is more flexible and adaptive. The long-short-term memory neural network model predicts load and power supply output trend, multi-dimensional risk assessment is carried out in combination with a fuzzy analytic hierarchy process, a scheduling strategy is optimized, and distribution network scheduling accuracy and efficiency are improved. The cloud-edge fusion architecture design gives play to the real-time advantages of cloud computing data processing and edge computing, solves the problems of delay and computing pressure of a traditional centralized scheduling network, and improves the stability and reliability of the power distribution network.

The invention relates to the technical field of machine learning, and discloses a mapping operator-based automatic generation method for a sub-mirror rough sketch, which comprises the following steps of: carrying out hierarchical analysis on a sub-mirror script text by adopting a power attention-Transform model; generating a visual composition scheme through a semantic visual mapping operator; wherein an improved Bezier curve method of a four-parameter Bernstein substrate is introduced into an action track, flexible reconstruction and key frame extraction of complex actions are realized, and picture colors, illumination and line styles can be adaptively adjusted by emotional style parameters to ensure that a narrative atmosphere is consistent with a director intention; and finally, rendering into a complete sub-mirror sketch sequence. According to the method, efficient, accurate and uniform-style sub-mirror sketch generation can be realized in film and television, animation and advertisement production, and creation efficiency and picture expressive force are remarkably improved.

The invention belongs to the technical field of multi-level RCM, and discloses a multi-level RCM analysis and reliability structure construction method and a related device. The multi-level RCM analysis and reliability structure construction method comprises the following steps: establishing a unit system function model after constructing a unit system database, performing multi-level classification on equipment of a unit system in combination with a fuzzy clustering algorithm, and then determining levels and weights of various types of equipment in the unit system function model, analyzing a failure mode of each type of equipment after multi-level classification through a harmfulness analysis method in combination with a fault tree analysis technology, establishing a correlation model between the equipment failure mode and a unit system function, determining an influence weight of the equipment failure mode on the reliability of the unit system, and establishing a unit system reliability model; inputting the unit system database into the unit system reliability model to obtain a unit system maintenance strategy library; the analysis result accuracy and the system reliability can be greatly improved.

The invention belongs to the technical field of cooperative communication, and discloses an analytic hierarchy process adaptive relay selection method based on machine learning feedback, which adopts a lightweight multi-layer perceptron (MLP) to learn normalized matrix characteristics, can dynamically adjust the importance distribution of each index according to a real-time service scene and a channel state, and improves the relay selection efficiency. The method gets rid of the limitation that a traditional method depends on artificial experience to set the weight, and greatly improves the use convenience and scene adaptability. Meanwhile, in consideration of inconsistency possibly generated by weight distribution, the method combines a consistency check mechanism of an analytic hierarchy process (AHP) to carry out rollback or convex combination correction on weight configuration which does not conform to a consistency condition, so that the reasonability and interpretability of decision logic are ensured, and instability caused by weight jump is avoided.

The invention discloses an electric energy meter measurement error analysis method and system based on line loss stratified analysis, and relates to the technical field of electric energy meter monitoring. According to the electric energy meter measurement error analysis method based on line loss hierarchical analysis, power supply structure information of a power supply area is obtained, multiple power supply levels are divided, comprehensive power data are obtained for each level, an actual line loss value of each level is analyzed through a feature extraction rule, and a theoretical line loss value is predicted in combination with a preset line loss prediction model. By comparing the actual line loss value with the theoretical line loss value, identifying the line loss abnormal level and generating early warning information, the power grid is divided into a plurality of power supply levels, the data of each level is independently analyzed, the measurement error of the electric energy meter is accurately identified, and the early warning information is generated by accurately calculating and identifying the deviation between the theoretical line loss and the actual line loss. The error source is accurately positioned, the operation reliability of the power grid is improved, the equipment loss and the operation cost caused by error correction are reduced, and the power grid efficiency and the measurement precision of the electric energy meter are improved.

The invention relates to the technical field of intelligent teaching and medical nursing, and discloses a clinical nursing teaching and training intelligent interaction system based on a large language model. According to the system, teaching behaviors and text data are collected, and multi-dimensional evaluation and quantification are carried out, so that an evaluation result containing a capability label is generated. Based on this result, the system dynamically constructs a personalized progressive teaching knowledge stream from the clinical care knowledge map. And meanwhile, the system carries out context-aware intention hierarchical analysis on the interactive content to form a hierarchical graph containing a core intention and a derivative demand. And fusing the knowledge flow and the analysis atlas, and driving the large language model to generate and execute a series of teaching instructions. The system achieves the dynamic adjustment of teaching contents along with the performances of students, can deeply understand the teaching intention for intelligent guidance, and improves the individuation and adaptability of training.