34 results about "Loop analysis" patented technology

The invention discloses a hybrid model driving-based space-time consistency regulation and control method for a central heating system, which comprises the following steps of: performing hydraulic equilibrium analysis on the central heating system, and performing hydraulic analysis on a space structure of a heating pipe network by using a graph theory method based on an energy conservation law, simulating the hydraulic working condition of the official website by using a basic loop analysis method; based on an energy conservation and mass conservation heat balance equation, a heat source and a heat user are indirectly connected through heat exchanger transition, a dynamic physical model of each subsystem is established based on a lumped heat capacity method, a refined model of the heat supply system is established, and the thermal dynamic characteristics of the system are researched and analyzed; based on a Pearson's correlation function, considering thermal inertia of equipment such as a pipe network of the central heating system and a building fence structure, and determining a regulation and control period and time of the regulation and control model; according to the method, by controlling the opening degree of the regulating valve of the heat exchange station, the suitability degree of the room temperature of a heat user is guaranteed.

The invention relates to the technical field of fund information management, in particular to a fund exchange information management system based on big data analysis. The system comprises a multi-source heterogeneous data acquisition and fusion module, an enterprise dynamic behavior portrait construction module, a hidden association network mining module and a trade background authenticity dynamic evaluation module. The method comprises the following steps: firstly, collecting and processing data from a plurality of data sources to generate a fused fund exchange record; constructing an enterprise dynamic behavior portrait based on the record; furthermore, a hidden association network graph is constructed based on the data, the data are received at the same time, the transaction behavior deviation degree is identified through behavior consistency check, fund closed-loop analysis is carried out on abnormal transactions, and finally a trade background authenticity index and a risk decision signal are generated. According to the invention, dynamic evaluation and risk early warning of supply chain financing trade background authenticity are realized, and the risk control capability of a financial institution is effectively improved.

The invention discloses a cross-platform income data closed-loop analysis method, and particularly relates to the technical field of data closed-loop analysis. The method comprises the following steps: collecting original multi-platform interaction data, and generating a cross-platform interaction data set; executing multi-layer anonymous identifier mapping, and outputting a unified user link identifier sequence and event sequence mapping table; calculating an observability matrix of each platform event chain to obtain a cross-platform observability score set; a cross-platform implicit path sequence set is mined based on a probabilistic graph reasoning method, and a backflow path complete sequence is constructed in combination with a cross-platform observability score set; performing income event association calculation and link credibility weight normalization, and outputting a closed-loop income attribution result; the closed-loop income attribution result is fed back to the cross-platform putting strategy database, closed-loop optimization is completed, accurate reduction of the real conversion path of the user and closed-loop quantitative analysis of multi-platform income contribution are achieved, and the optimization effect and the actual income performance of the cross-platform putting strategy are improved.

The invention discloses a hydropower engineering extra-high steep slope online dynamic prevention and control method. The method comprises the steps that S1, data dynamic coupling and abnormity elimination are carried out; s2, carrying out space-time matching on the data; s3, optimizing a displacement early warning value; s4, carrying out safety prevention and control multi-modal analysis; and S5, performing result feedback and closed-loop control. According to the online dynamic prevention and control method for the ultra-high and steep slope in the hydropower engineering, rapid and accurate fusion of multi-source heterogeneous spatio-temporal data can be realized, the false alarm rate of slope early warning is reduced, and online closed-loop analysis of automatic identification of dangerous rock body hidden danger, real-time analysis of rockfall trajectory speed and pre-consolidation range and anchoring differentiation decision of the high and steep slope is realized; and a scientific, accurate and timely decision basis is provided for safety management of the high and steep slope.

The invention discloses a remote sensing time series data analysis method and system considering geographic object space-time correlation, and belongs to the technical field of data analysis. The method comprises the following steps: acquiring and preprocessing a multi-source multi-temporal remote sensing image and auxiliary data to generate a consistent time sequence data stack; dividing the image into voxels in a three-dimensional time-space domain, carrying out adjacent tracking, identifying a geographic process object with an evolution behavior, and recording the attribute of the geographic process object; judging a space-time topological relation between geographic process objects, and generating a unified space-time relation table through composite reasoning; constructing a geographic process object space-time diagram model by taking the geographic process object as a node and the space-time relationship as an edge, and extracting and standardizing attribute features of the node and the edge; a graph convolutional network and a topology dynamic mechanism are used for joint modeling, and the importance index of each node is calculated. According to the method, closed-loop analysis from data processing, relation modeling and importance evaluation to decision support is realized, and the interpretability of remote sensing time sequence change detection and decision effectiveness are improved.

The invention belongs to the technical field of PCB detection, and particularly discloses a PCB copper thickness and line width uniformity detection method and system, and the method comprises the steps: dividing a PCB surface into a plurality of detection regions, carrying out the detection of the copper thickness uniformity and the line width uniformity in each detection region, constructing a uniformity evaluation mechanism of dual dimensions of space correlation and time continuity, and carrying out the detection of the copper thickness uniformity and the line width uniformity. Dynamic uniformity judgment of each detection area in the electroplating process is realized, early abnormity can be effectively identified, and meanwhile, after copper thickness and line width uniformity judgment is completed and an abnormal detection area is identified, current density of the abnormal area is dynamically associated with copper thickness fluctuation characteristics; and lagging correlation analysis is carried out on the plating solution flow rate and the line width fluctuation characteristics, so that main influence factors causing nonuniformity are determined, closed-loop analysis from anomaly detection to root cause tracing is realized, and clear directional guidance is provided for process parameter tuning.

The invention discloses a URL (Uniform Resource Locator) security analysis method and device for an end-side large language model and a medium, and belongs to the field of network and information security. The method comprises the steps of collecting multi-modal data such as a URL analysis structure, DNS / certificate metadata, front-end code sampling, an OCR text and a dynamic behavior event sequence; performing exponential decay weighted aggregation on the dynamic events in the sliding time window; generating a static structure feature, a text semantic feature and a threat intelligence similarity based on the multi-modal data; fusing the multi-modal features by adopting a gating attention mechanism; inputting the fusion features and the readable context into an end-side large language model, and calling a security analysis tool set under limited decoding; a risk score and a minimum sufficient evidence are obtained through two-stage scoring; and executing an end-side security policy according to the scoring threshold value and leaving a trace by using a JSONL structure. According to the method, in-end closed-loop analysis is realized, the detection accuracy is remarkably improved, and the method is suitable for various deployment scenes such as mobile terminals and edge gateways.

The invention discloses a neuroscience literature processing method and system for constructing a brain connection knowledge graph. The method comprises the following steps: training an entity recognition model; training a relation extraction model; literature data acquisition and standardization processing; entity and relation extraction; performing entity standardization; construction and storage of the knowledge graph; and application analysis of the knowledge graph. According to the method, the entity boundary recognition and relation classification accuracy is remarkably improved, the problems of inaccurate boundary prediction, wrong relation recognition and the like are remarkably relieved, and standardization and linking of entities are achieved by introducing a fuzzy matching and word vector similarity calculation method; meanwhile, reasoning and visual analysis of the brain region connection relation are supported, application scenes such as document retrieval, specific brain region input and output loop analysis and author community structure recognition are covered, and comprehensive and systematic knowledge support is provided for nervous system structure and function research.

The invention relates to the technical field of logistics data processing, and discloses a logistics business data query method, which comprises the following steps: collecting multi-source logistics business data, collecting original business data from a plurality of data sources, and cleaning the original business data to obtain preprocessed business data; extracting a time period and website features from the preprocessed service data, and screening the extracted features; dividing the preprocessed business data into historical data and real-time data according to a time sequence, selecting a machine learning algorithm according to an analysis target to construct an analysis model, training the selected model by using the historical data, inputting the real-time data into the trained model, and outputting a detection result; and constructing a knowledge graph, receiving a natural language query request of a user, and outputting a query result. According to the method, the problems of dispersion and non-uniform format of traditional logistics data are solved, the integrity and availability of the data are remarkably improved, a closed-loop analysis process is formed, and the efficiency and accuracy of logistics business management are greatly improved.

The invention discloses a memory alloy part durability experiment data analysis method and system, relates to the technical field of data processing, and aims to solve the problem of poor alloy performance detection effect caused by environmental noise interference and signal scattering of existing memory alloy durability experiment data. A differential signal processing strategy is adopted to carry out noise reduction optimization on multi-dimensional performance response data, characteristic factors representing deformation recovery, residual deformation and energy dissipation are extracted and corrected, and combined logic of the characteristic factors is dynamically judged by combining a recognition rule set based on an application scene so as to determine a specific performance degradation mode. Corresponding residual use cycle prediction models are constructed according to different degradation modes, the credibility range is calculated, finally, a performance optimization strategy is generated through matching, and full-process closed-loop analysis from data preprocessing and feature extraction to degradation mode recognition, service life prediction and strategy generation is achieved. And the performance prediction precision and the reliability evaluation efficiency of the memory alloy part under the complex working condition are remarkably improved.

The invention belongs to the technical field of low-voltage power distribution networks, and discloses a transformer area line loss anomaly analysis method based on a segmented metering mode and a related device.A transformer area anomaly reason is preliminarily diagnosed through a pre-trained line loss anomaly analysis model: after a power grid structure and meteorological parameters of a transformer area to be analyzed are input, the model outputs a line loss preliminary conclusion; if the result is fuzzy judgment, starting a step-by-step sectional metering method, in the first level, comparing the power supply quantity and the power sale quantity of each branch under the transformer outlet bus by group, and if the difference value exceeds a preset first threshold value, marking an abnormal branch group, terminating the detection of the level, and turning to a second level; and the second level repeatedly compares the sub-branch groups of the abnormal branch group, accurately locates the fault point, and finally locks the minimum abnormal unit. According to the method, through dynamic threshold management and grouping progressive detection, closed-loop analysis from model inference to physical troubleshooting is realized, and efficiency and precision of transformer area line loss anomaly analysis are both considered.

The invention discloses a large-scale generator main protection dead-zone-free transformation method and system, and relates to the technical field of generator protection, and the method comprises the following steps: analyzing a stator winding connection diagram of a large-scale generator, and predicting the fault number and distribution characteristics of same-phase and same-branch turn-to-turn short circuits, same-phase and different-branch turn-to-turn short circuits and interphase short circuits of the generator; analyzing and calculating the predicted internal faults of the generator by using a multi-loop analysis method, solving the magnitude and phase of each branch current under each fault, further calculating the sensitivity coefficient of each main protection scheme to the internal faults, and determining the fault number and the fault type which can be reflected by each main protection scheme; a generator main protection configuration scheme is generated for minimizing a protection action dead zone and minimizing electromagnetic TA. A corresponding main protection scheme is added through flexible winding of the optical current transformer, and a protection dead zone for turn-to-turn short circuit with small number of turns is eliminated, so that the technical improvement of a generator main protection configuration scheme without a dead zone is completed.

The embodiment of the invention provides a visual analysis method for diagnosis and optimization of an urban drainage system and related equipment, and belongs to the technical field of smart city and data visualization. According to the method, a closed-loop analysis framework formed by collaboration of a map view, an analysis view and a comparison view is constructed. The map view integrates a hydrological and hydraulic model simulation result, visualizes a drainage system state and supports interactive modification; the analysis view is used for extracting a key drainage subsystem based on rules, and performing causal visualization by adopting an optimized mulberry-based graph integrated with terrain elevation information, so that overflow causes are accurately positioned; a comparison view is visualized through a parallel histogram and version inheritance relation, and multi-scheme comparison and iterative optimization management are supported. According to the method, the problems of difficulty in causal analysis, poor interpretability and lack of iterative optimization support in the prior art are solved, the whole-process integrated support from problem identification and diagnosis to optimization verification is realized, and the analysis efficiency and decision scientificity of the urban drainage system are remarkably improved.

The application discloses a deep evaluation model and analysis method based on a computing power application scene, relates to the field of information technology and digital infrastructure planning, and specifically relates to a deep evaluation model and analysis method based on a computing power application scene. The model is constructed from ten dimensions of policy adaptability, regional industry integration, infrastructure support, construction operation mode, implementation strategy landing, operation and maintenance service, energy consumption control, cost return calculation, market supply and demand matching, and risk prevention and control system, taking actual landing requirements as the core. The importance of each item is graded and quantitatively scored, a feasibility determination standard is set, a full-closed-loop analysis system is formed, full-dimension and hierarchical deep analysis of the feasibility of computing power project construction and operation is realized, and support is provided for project decision-making.

The invention relates to the field of distribution box operation state monitoring, and discloses a distribution box operation state monitoring system and method, and the method comprises the steps: collecting temperature, current and environment humidity signals, and constructing a dynamic operation feature matrix; forming an exclusive safety threshold library according to the dynamic operation characteristic matrix of each node in combination with line attenuation characteristics and switch contact aging states; the safety threshold value of each node is used as a constraint state parameter to be introduced into a loop analysis framework, and a multi-layer causal association structure is constructed; based on the multi-layer causal association structure and the historical operation features, constructing an extreme operation condition structure, executing set statistics and entropy feature evolution analysis on corresponding operation states, and generating a risk candidate list; according to the conduction result of the safety threshold constraint in the multi-layer causal association structure and the potential risk candidate list, the nodes on the key causal path are subjected to prospective adjustment of the output power, an operation regulation and control mechanism is formed, and the method has the advantage of improving the early risk identification capability.

The present invention discloses a method and system for optimizing the generation of cosmetic adverse reaction analysis reports, specifically relating to the technical field of cosmetic safety supervision, and is used to solve the problem of inaccurate adverse reaction attribution caused by dispersed supply chain data and inconsistent batch labels in the prior art. By integrating full-process data of raw material procurement, production and processing, warehousing and logistics, and adverse reaction events, a batch labeling system with a unified index is constructed, and cross-link data deviations are repaired based on timestamp conflict detection and conditional generative adversarial networks to generate complete batch data after elimination. Further, a causal network is constructed through an attribute-constrained variational autoencoder to extract potential risk factors, and a spatiotemporal knowledge graph is constructed by combining supply chain disruption events and user location trajectories to identify abnormally associated batches. Finally, causal weights and spatiotemporal probabilities are integrated to generate a batch risk quantification report, realizing a closed-loop analysis from data conflict resolution to accurate attribution.

The application discloses a kind of remote sensing time series data analysis method and system considering the spatio-temporal correlation of geographical object, belong to data analysis technical field.The method includes: obtaining multi-source multi-temporal remote sensing image and auxiliary data and pre-processing, generate consistent time series data stack;In three-dimensional space-time domain, image is divided into voxel and is adjacent tracking, identify geographical process object with evolution behavior and record its attribute;Determine the spatio-temporal topological relationship between geographical process object, generate unified spatio-temporal relationship table by composite reasoning;With geographical process object as node, spatio-temporal relationship as edge constructs geographical process object spatio-temporal graph model, extracts and standardizes the attribute characteristics of node and edge;Importance index of each node is calculated by using graph convolution network and topological dynamic mechanism joint modeling.The application realizes closed-loop analysis from data processing, relationship modeling, importance evaluation to decision support, improves the interpretability and decision effectiveness of remote sensing time series change detection.

The invention relates to the technical field of deep groove bearing detection, in particular to a deep groove bearing rolling body surface defect detection method and system. Through layered vibration signal acquisition, low-frequency reference noise self-adaptive elimination and rolling body motion trail modeling, accurate detection of micron-sized defects on the surface of a deep groove bearing rolling body is realized, different signal channels of the rolling body and a bearing seat are effectively covered by constructing a multi-source vibration sensing layered acquisition system, and the detection precision of the deep groove bearing rolling body is improved. By synchronously acquiring low-frequency reference noise and high-frequency micro-impact signals and performing self-adaptive time domain noise cancellation, interference such as equipment background noise and gear meshing is remarkably suppressed, a rolling body motion and impact pulse correlation model is established by using structural parameters and real-time rotation parameters, and pure micro-impact signals are directionally enhanced, so that the precision of the rolling body motion and impact pulse correlation model is improved. Through multi-dimensional feature extraction and three-dimensional closed-loop analysis, accurate positioning and authenticity verification of defect features are realized, and graded early warning of rolling body surface defects is completed in combination with a defect grade threshold and an early warning rule.

The present invention provides a demand closed-loop analysis method, system and device, which includes: obtaining satellite view data, obtaining demand data, and demand timing coverage analysis and calculation. Starting from the two entrances of demand and view data, a small amount of calculation is performed in real time to obtain a certain correlation relationship, and finally the coverage rate, number of observations and observation duration are summarized and calculated, and the large amount of calculation at the same time in the prior art is amortized into small amounts of calculation at different times, which reduces the database calculation burden, thereby ensuring the calculation efficiency and alleviating the database pressure.

The application provides a large-span steel structure closed loop analysis method and system, the analysis method comprises the following steps: constructing a finite element model and a design parameter space, identifying key design parameters and key regions affecting the first N modal strain energy of the structure through initial sensitivity and modal analysis, and establishing a mapping relationship between the two; according to the parameter sensitivity contribution, a non-uniform sampling strategy is formulated, the sampling is encrypted in the high sensitivity parameter subspace, an initial sample set is generated, based on the sample and the finite element response, a hierarchical mixed kernel Kriging surrogate model containing a global long-range kernel and a multi-region local short-range kernel is constructed, the local kernel weight increases as the input point approaches the high sensitivity region, the iteration termination and the performance limit state are set, the composite point adding criterion integrating the prediction variance, the limit state distance and the cross-validation error is used to iteratively update the sample and the model, and after the condition is met, the structure performance analysis is completed by using the surrogate model.

The invention provides a collaborative innovation platform based on a DIKWP model and a TRIZ theory, and belongs to the technical field of artificial intelligence and innovative design. The platform takes an artificial consciousness operating system (AI-OS) as a core architecture, and deeply fuses data-information-knowledge-intelligence-intention (DIKWP) five-layer semantic storage and a TRIZ algorithm library reasoning solving function: when a user submits an innovation demand or a technical problem, the platform firstly uses a DIKWP model to perform hierarchical analysis on problem semantics, and identifies potential contradictions and key targets; and then a built-in TRIZ contradiction solving engine and a causal chain analysis engine are matched with corresponding conflict models and inventive principles, and an innovative solution is automatically generated. When the scheme is generated, a general problem solving thought is converted into an executable scheme in combination with domain knowledge, and an inference chain of semantic conversion, a TRIZ principle and a result is output for explanation and verification. The platform also supports plug-in extension, and can be in butt joint with an industry-specific knowledge graph, simulation software and external AI service to enhance the solving capability of problems in specific fields. Compared with the prior art, the method has the advantages that the target-driven semantic closed-loop analysis and the TRIZ algorithm are combined, the value alignment of the solution and the user intention is realized, the process can be explained, the robust iterative optimization capability is realized, the innovation threshold is remarkably reduced, and the problem solving efficiency is improved.

According to the earth-rock dam leakage flow velocity detection method based on the single-hole dilution method, the bottleneck of the traditional technology is broken through through multiple technological innovations, the accurate tracer solution is adopted, the injection mode is reasonable, forced convection mixing of drilling water columns is achieved in a pneumatic stirring mode, uniform diffusion of the tracer is achieved, and the leakage flow velocity of the earth-rock dam is detected. The problem of water layering caused by manual stirring is thoroughly eliminated, and the physical reliability of a conductivity attenuation curve is guaranteed; on the basis of a traditional dilution curve model, a drilling radius correction factor and a non-Darcy effect correction term are introduced, an optimized flow velocity formula is constructed, the error of more than 30% of a traditional method is compressed to be within 5%, and the output precision of a vertical seepage profile is remarkably improved. Integrated closed-loop analysis of tracer agent layered injection, pneumatic homogeneous mixing, multi-node synchronous monitoring and flow field intelligent inversion is achieved, the double defects of discrete operation and theoretical limitation of a traditional single-hole method are overcome, and a quantifiable and reproducible quantitative diagnosis tool is provided for dam leakage channel positioning.

The invention relates to the technical field of image recognition, in particular to an aggregate three-dimensional morphological feature analysis method and system based on laser scanning, and the method comprises the steps of data intelligent collection, three-dimensional point cloud data intelligent preprocessing, morphological feature parameter calculation, intelligent analysis and association and application. In the prior art, morphological analysis, virtual experiment and performance prediction of aggregate are generally regarded as relatively independent links, or only forward prediction from process, material to performance can be carried out, and closed-loop analysis cannot be formed; according to the method, on the basis of analyzing the aggregate accumulation characteristics through a virtual compaction experiment, a double-prediction model architecture is introduced, the raw material ratio is reversely deduced from morphological characteristics and the process through the first prediction model, and the process parameters are inversed through the second prediction model in combination with morphological and raw material information; and finally, inputting all the features into a performance prediction model for comprehensive performance prediction. And unprecedented deep analysis capability is provided for aggregate quality diagnosis, production process optimization and performance customization.

The present invention discloses an intelligent design and optimization method for tunnel blasting parameters in complex strata, specifically relating to the field of tunnel blasting technology. The method comprises the following steps: obtaining initial full-section blasthole design, detonation network design, and charge design information to construct an initial mathematical representation data set; establishing a three-dimensional tunnel model based on tunnel sampling data, inputting the initial data into a trained blasting simulation model to obtain preliminary simulation results; performing deviation analysis by comparing the simulation results with the expected results to determine whether to activate the blasting parameter optimization mechanism; if activated, performing sensitivity analysis and loop analysis to confirm all sensitive parameters. A genetic algorithm is used to optimize the blasting parameters, replacing the corresponding parameters in the initial data set, ultimately obtaining an optimized mathematical representation data set. The present invention can accurately adjust the blasting parameters, reduce unnecessary waste of blasting materials, and avoid secondary correction work caused by over-blasting or under-blasting.

Provided are a compiling device, a compiling method, and a compiling program with which the speed of a program can be increased when a loop in a program includes an access to adjacent data. The compiling device includes: a loop analysis unit for determining whether vectorization of a loop in a source program is possible when an access to adjacent data is included in the loop; and a vectorization execution unit for generating an instruction for reading elements required for computation from a memory to a vector register when access to the adjacent data is included and the vectorization of the loop is possible.

A password function identification method based on cycle analysis and binary similar code analysis, simulates execution of binary function code to be analyzed, extracts dynamic execution characteristic sequence in the simulation execution process, calculates the Jaccard coefficient of the characteristic sequence and the characteristic of the password function in the characteristic library as a similarity score, takes the password function in the characteristic library with the highest similarity score to the function to be analyzed as the identification result, and realizes automatic detection of the password algorithm. The application guarantees the accuracy of password algorithm identification under different compiler versions and optimization options, considers the efficiency and scalability of analysis, improves the accuracy of standard password algorithm identification in such scenes, reduces the time overhead of the analysis process, and has the advantages of cross-compiler configuration and cross-architecture.

The application discloses a video code stream analysis method and system, and relates to the technical field of image data processing, and comprises the following steps: acquiring a video code stream; judging whether the video code stream is a program stream or an audio stream; if yes, obtaining a corresponding code stream format; if not, extracting NAL unit types belonging to H.264 streams and H.265 streams from the NAL unit header of the video code stream and respectively storing the NAL unit types in H.264 queues and H.265 queues; and judging that the format of the video code stream is the code stream format to which the NAL unit types of the queue in which all the NAL unit types exist belong only when all the NAL unit types of one queue exist and the NAL unit types of the other queue do not completely exist. The code stream format is identified by code stream characteristics instead of being simply agreed by a transmission protocol, the real format of code stream coding is quickly and accurately judged, and multiple loop analyses are not required, so that the cost is low and the speed is fast.

The invention belongs to the technical field of information technology processing, and particularly relates to an analysis method and system for mobile terminal AI application software, and the method comprises the following steps: employing a dynamic symbolic execution algorithm DSE + + to analyze an encryption model structure, employing a heterogeneous perception federal average algorithm FedAvg + + to carry out cross-device collaborative analysis, and carrying out cross-device collaborative analysis; a deep reinforcement learning DRL algorithm is adopted to perform adaptive resource scheduling, and an analysis system for mobile terminal AI application software comprises an end side analysis module, an edge aggregation module, a cloud decision module and a security detection module. Through combination of dynamic symbolic execution and an attention mechanism, an encryption model analysis problem is solved, a FedAvg + + algorithm is proposed to break through a federated learning isomerism bottleneck, resource adaptive scheduling is realized through DRL, and the three parts cooperate to form a closed-loop analysis system.

This invention provides a method and apparatus for measuring latency in audio and video playback environments. The method includes: connecting test audio directly to an audio input port via an audio output port to obtain a recording of the test audio; performing loop analysis on the recording to calculate the sum of the audio output latency and the audio input latency; playing a test video and calculating the time difference between the played sound and the video to obtain the audio output latency; subtracting the audio output latency from the sum of the audio output latency and the audio input latency to obtain the audio input latency; directly measuring various latency parameters that can correct audio and video playback results, ultimately improving the user experience.

The invention relates to the technical field of intelligent analysis of a power distribution network, and discloses a regional current situation power distribution network development diagnosis and weak index analysis method. The method comprises the following steps: constructing a power distribution network basic cognitive model based on a general large model, and processing original operation data to generate a power distribution network knowledge graph containing an equipment state and a network topology; the knowledge graph is finely adjusted through industry documents and standards, industry rules are injected into the model, and an industry knowledge enhancement model with professional cognition is formed; performing cooperative processing on the structured knowledge by using a multi-agent cooperative system to generate a multi-dimensional diagnosis report; and collecting evaluation deviations through a scene feedback mechanism, driving the model and the system to carry out reinforcement learning training, and forming a closed-loop analysis process of iterative optimization. According to the method, deep combination of professional knowledge and artificial intelligence is realized, the problems of insufficient professional adaptability and stiff static analysis of a traditional method are solved, and the diagnosis accuracy and the self-adaptive capability of the system are improved.