9 results about "Effect analysis" patented technology

The invention discloses a logistics distribution deployment and rider operation management system, which relates to the technical field of intelligent logistics, and comprises a time sequence observation module, a causal analysis module, a conflict extraction module, a sequence construction module, a scheduling agent module and a beat regulation and control module, a scheduling beat, resource lock delay and a memory access sequence are collected, and a priority drift distribution diagram is generated; and the causal analysis module is used for constructing a time-space mapping structure dependent on topology based on the priority drift distribution diagram, performing time sequence correlation analysis of task paths, identifying a causal chain and determining race trigger nodes. According to the method, scheduling abnormal root cause identification is realized through time sequence observation, causal analysis and conflict fingerprint extraction, the consistency of a task sequence and resources is guaranteed in combination with monotonic task sequence construction and double-track scheduling control, dynamic balance of scheduling priorities is realized through beat reconstruction, and the stability and controllability of logistics scheduling in a high-concurrency environment are remarkably improved.

The invention discloses a bid evaluation result clustering analysis method based on reinforcement learning, and relates to the technical field of data processing and bidding analysis, and the method comprises the specific steps: firstly collecting multi-source heterogeneous bid evaluation data and carrying out the standardization processing through relying on an Internet data service platform; inputting a reinforcement learning clustering model, outputting a result and intermediate data and storing the result and the intermediate data; carrying out abnormity verification, and if the standard is not reached, triggering causal analysis to generate a correction instruction; performing causal intervention and iteration on the model according to an instruction; and finally, sorting up standard results to form a report, pushing a final result, and visually displaying evidence storage data and a causal map. According to the method, multi-source heterogeneous bid evaluation data are integrated and standardized, a reinforcement learning clustering model is constructed, and distributed storage and multi-dimensional verification are combined; through causal intervention closed-loop iterative optimization, dynamic clustering is realized, results and the like are visually displayed, accuracy and transparency are improved, and bid evaluation analysis is promoted to develop towards the direction of intelligence and the like.

The invention relates to the technical field of power equipment operation and maintenance management, and discloses a power generation equipment maintenance management method based on RCM and size model cooperation. The method comprises the steps of collecting multi-source data such as operation data, inspection texts and historical records through a monitoring platform; analyzing and quantifying the severity, the occurrence probability and the detectability of the fault mode by utilizing the influence of the fault mode under an RCM framework, calculating the risk priority number and sorting the risk priority number; identifying key fault causes based on the sorting result; performing cue word compression and key information extraction on the multi-source data by adopting a small model to obtain a semantic feature vector, performing deep analysis on the feature vector through a DPO fine-tuning large model, and outputting a fault prediction probability; a knowledge graph retrieval enhancement mechanism is embedded in large model reasoning, and a causal reasoning chain is constructed; and finally, automatically generating a structured maintenance work order according to a reasoning result. According to the invention, the accuracy of power generation equipment fault prediction is effectively improved.

The application provides a model-based power system failure mode and effect analysis method, and relates to the technical field of failure mode and effect analysis, and comprises the following steps: obtaining development requirements of a power system, and constructing a system model of the power system based on the development requirements; wherein the system model comprises an activity graph, a parameter graph model, a module definition graph and an internal module graph; identifying power system failure modes based on the system model; wherein the power system failure modes comprise functional failure modes, performance deviation failure modes and structural failure modes; identifying failure transmission relationships of the power system failure modes based on the system model; modeling the failure transmission relationships to obtain a failure transmission relationship matrix; and generating power system FMEA entries based on the power system failure modes and the failure transmission relationship matrix. The application improves the accuracy and analysis efficiency of power system failure mode and effect analysis.

The application discloses a state report improvement measure effect detection method and device, belongs to the nuclear power field, aims to solve the problem that the traditional detection method is strong in subjectivity, low in efficiency and insufficient in data utilization. The application contains five steps of data collection, preprocessing, index calculation, effect detection and report generation. First, multi-source data such as operation logs and fault records are collected, stored after cleaning and standardization; then, key indexes such as event frequency, severity, average repair time and average fault interval time are calculated; through statistical and machine learning algorithms such as multivariate regression model, the index changes before and after improvement are compared; finally, a detection report containing index comparison, effect analysis and improvement suggestions is generated. The application realizes quantitative detection, improves the objectivity and efficiency of detection, provides data support for nuclear power station operation decision, helps to improve the operation safety and efficiency, and has good flexibility and expansibility.

The invention discloses an industrial operation data visualization display method and system based on an artificial intelligence platform, and relates to the technical field of industrial equipment operation data visualization. Multi-dimensional operation data of industrial equipment are collected and associated identifiers are marked, and a part physical association relation table and a global unique identifier are established; binding the data and the global unique identifier, converting the causal analysis conclusion of the artificial intelligence platform into a global unique identifier association chain, generating a numerical label and a thermodynamic diagram based on the binding relationship and the global unique identifier association chain, and performing highlight labeling and global unique identifier association chain display when the fault data reaches the standard. According to the method, the relevance and intuition of industrial equipment operation data visualization are improved, a user is helped to quickly grasp the equipment operation state and the abnormal cause, and the actual requirements of industrial operation and maintenance are met.

The application relates to the technical field of construction safety detection, in particular to an abnormal behavior detection system and method. The system comprises a multi-modal perception layer, an edge processing layer, a behavior cognition reasoning engine, a dynamic risk assessment and early warning module and a visual management platform connected in sequence. The multi-modal perception layer collects visual, Internet of Things and environmental data; the edge processing layer performs real-time target detection, behavior preliminary classification and space-time registration; the behavior cognition reasoning engine realizes behavior understanding and intention prediction by constructing a space-time graph and fusing rule reasoning and a deep learning model, and integrates a cause-and-effect analysis submodule to perform root cause tracing; the risk assessment module dynamically calculates a risk level according to a reasoning result and triggers early warning, and deduces risk evolution by using a risk transmission simulator; and the visual management platform realizes three-dimensional display and emergency linkage, and continuously optimizes a model by feedback data driving. The application realizes real-time and accurate identification and closed-loop risk control of abnormal behaviors on a construction site.

The invention relates to the technical field of chemical informatics, in particular to a free implementation analysis knowledge base construction method based on an agent and workflow, and the method comprises the following steps: firstly, constructing a free implementation knowledge graph by using chemical structure data and patent text data, and establishing an association relationship among molecular nodes, claim nodes and target nodes in the graph; markush templates are generated on the basis of claims with structural formulas in the knowledge graph, free implementation distance vectors are calculated for each molecule and each Markush template, and the Markush templates and the distance vectors are written into the knowledge graph; on this basis, a target analysis workflow is constructed, and a candidate molecule generation agent and a free implementation assessment agent are driven to carry out free implementation risk assessment and recommendation on candidate molecules; and further establishing a causal analysis model by using a workflow log, a patent review result and an infringement judgment result, and calibrating an aggregation mode of a distance vector and a risk assessment rule to realize continuous optimization and traceable management of free implementation analysis.

The application discloses an intelligent software development auxiliary system, and relates to the field of artificial intelligence and software engineering, and the system comprises: a cause-effect analysis module which disassembles natural language requirements into structured data; a topology modeling module which quantifies node correlation and constructs a code cause-effect topology graph; an intervention regulation module which corrects large model deviation and generates initial code; a verification optimization module which covers all logic paths through counterfactual reasoning, automatically generates test cases and iteratively repairs defects; a traceability interpretation module which traces back all link cause-effect paths and outputs a visual explanation report; the application eliminates requirement deviation and improves code generation accuracy through cause-effect analysis, topology modeling and intervention regulation, reduces artificial modeling workload and shortens development cycle; and after verification optimization and traceability interpretation, all logic paths are covered and defects are iteratively corrected, a whole-process closed loop is formed, debugging cost and error rate are reduced, and code stability and traceability are enhanced.