8 results about "Event retrieval" patented technology

The invention discloses an operation and maintenance knowledge base analysis system and method based on deep learning, and is applied to unified operation and maintenance management of a hospital information system and medical equipment. The system collects operation and maintenance data such as operation logs, monitoring indexes, alarm events, work orders, maintenance records and the like, constructs a minute-level time window through equipment identification and technology triggering time, and merges multi-source heterogeneous data into operation and maintenance sub-events and operation and maintenance events. Log, index and text feature extraction is performed on each operation and maintenance sub-event, multi-modal feature fusion is performed to generate an operation and maintenance event feature vector, the operation and maintenance event feature vector is stored in a vector index database and is associated with an operation and maintenance knowledge graph, and similar historical event retrieval and root cause and processing scheme recommendation of a new operation and maintenance event are realized. In combination with the adoption and processing results of recommended schemes by operation and maintenance personnel, the system performs hierarchical management on the operation and maintenance knowledge and performs incremental updating on the feature modeling network, so that the operation and maintenance knowledge retrieval accuracy and the fault positioning efficiency are improved.

The invention discloses a bank data analysis processing method and system, particularly relates to the technical field of bank data processing, and is used for solving the problem that multi-source data is difficult to trace uniformly. Multi-service system records are accessed through a unified time reference and a subject identifier, scattered records are recombined into event frames with event types, time and subject sets based on event anchor point rules, conflict resolution is performed on multi-source same-name fields according to authoritative source priorities and delay propagation thresholds, main field values and alternative field values are reserved, and multi-source same-name fields are obtained. On this basis, an event frame table, a state sequence table and a participating entity table are constructed, indexes are established according to subject identifiers, time intervals, event types and state codes, retrieval and statistics around customer events are achieved, data consanguinity among source fields, event frame fields and analysis fields is synchronously established when the analysis fields are generated, and the data consanguinity among the source fields, the event frame fields and the analysis fields is established; and in combination with the version information, tracing of index sources in a rule change and late data scene is supported.

The present application relates to the technical field of intelligent transportation, and more particularly to a traffic event image-text mutual search method and system based on cross-modal retrieval, which comprises the following steps: obtaining query data of a traffic event, structurally processing the query data to obtain query semantic units; performing feature coding on the query semantic units to generate query semantic vectors; constructing a weak common semantic space, and setting an image index channel and a text index channel for the weak common semantic units; selecting an index channel according to the query data, mapping the query semantic vectors to the weak common semantic space for similarity matching, and generating a traffic event preliminary search result set; and performing fine sorting on the traffic event preliminary search result to output a traffic event search result set. Through the present application, the problems of low search efficiency and insufficient search accuracy in large-scale search are effectively solved, the matching deviation caused by modal difference is reduced, and the efficiency and business adaptability of traffic event search are effectively improved.

The application belongs to the technical field of computers and provides a fault diagnosis method and system based on equipment maintenance knowledge base. The method comprises the following steps: constructing an equipment maintenance knowledge base, wherein the equipment maintenance knowledge base comprises phenomenon events, fault causes and the relationship between the phenomenon events and the fault causes, the relationship is a fault scene, and the fault scene comprises a single cause scene and a combined cause scene; acquiring a last node in a fault diagnosis path as a phenomenon event, retrieving a fault scene of the phenomenon event, and if the retrieval result is empty, the phenomenon event is a fault diagnosis result. The single cause scene can realize the reasoning ability of the traditional decision tree rule, the combined cause scene is combined with the single cause scene, the fault combination condition traversal with exponential complexity can be automatically expanded based on a small amount of structured expression, the knowledge representation is simplified, and the complexity and workload of the construction and maintenance of the knowledge base are reduced.

The invention provides a short video false news detection method based on a thinking chain and mixed experts. The short video false news detection method comprises the steps of structured thinking chain construction, mixed verification expert module construction, mixed theme expert module construction, model training and parameter optimization and short video false news detection reasoning. According to the method, a structured thinking chain is designed, a multi-modal large language model is guided to simulate a human rumor recognition thinking process, news element extraction, event retrieval and multi-angle reasoning are completed in sequence, and fine-grained reasoning explanation is generated from three aspects of cross-modal consistency, fact verification and logic verification; meanwhile, a hybrid verification expert module is introduced, multi-angle evidences are integrated through a dynamic routing mechanism, key reasoning features are captured, and the judgment credibility is improved; and in combination with a mixed topic expert module, a discrimination standard is adaptively adjusted according to a short video topic scene, and the generalization ability and discrimination precision of the model under different topics are enhanced, so that interpretable robustness detection of the short video false news is realized.

The present application relates to the technical field of information engineering knowledge graph construction, and particularly relates to a supervision knowledge graph dynamic modeling method based on multi-source heterogeneous data fusion. The method constructs a dynamic evolution and cross-source consistent knowledge network by designing a four-dimensional knowledge graph modeling framework, combining entity alignment of a graph neural network, relationship extraction of remote supervision, and attribute processing of weighted fusion. For a multi-modal data missing scenario, a virtual feature compensation mechanism (historical event retrieval, multi-dimensional feature weighted fusion) is used to generate virtual features and map them to the graph, effectively filling in the data and solving the problem of incomplete knowledge coverage in complex engineering scenarios. A compliance entropy evaluation model is introduced to quantify the compliance state of the supervision knowledge graph from three dimensions of rule coverage, data consistency, and traceability integrity, breaking through the limitations of the traditional artificial compliance check in terms of lag and subjectivity.

The invention discloses a potential evapotranspiration extreme event intelligent identification method for time sequence big data, and relates to the technical field of big data mining, and the method comprises the steps: collecting meteorological data, carrying out the quality control and restoration, calculating the daily potential evapotranspiration amount, writing in a column storage table of time and space partitions, and forming a potential evapotranspiration time sequence data set; obtaining a conditional quantile baseline value and a scale parameter according to the potential evapotranspiration time sequence data set, performing streaming updating, calculating a standardized residual error through the conditional quantile baseline value and the scale parameter, and generating a standardized residual error sequence; according to the standardized residual sequence, identifying continuous high-value time periods and generating event objects, calculating extreme strength indexes and sorting the extreme strength indexes to obtain an event curve and an event list; and constructing an event signature through the event curve and the event list and establishing an inverted index, and performing intelligent identification and result archiving on the potential evapotranspiration extreme event through similar event retrieval. And a comparable, searchable and reusable effect can be achieved for potential evapotranspiration extreme events.

This invention belongs to the field of intelligent video surveillance technology and discloses a factory hazardous area monitoring storage and intrusion behavior recognition and early warning system. By incorporating a distributed hierarchical storage module, it automatically configures differentiated storage duration and multi-copy backup strategies based on the intrusion hazard level and area risk level. The first-level solid-state drive enables real-time data and second-level abnormal event retrieval and playback, the second-level mechanical hard drive stores regular video, and the third-level offline archiving medium archives high-risk event data for a long time. At the same time, it generates an immutable hash verification chain for medium and high-risk events, and automatically cleans up abnormal data in batches on a periodic basis, realizing intelligent management of security data throughout its entire life cycle. This not only significantly reduces storage costs but also ensures the complete retention and secure traceability of critical data. The multi-modal preprocessing module completes the spatiotemporal alignment and adaptive interference filtering of video, infrared thermal imaging, millimeter-wave radar, and vibration sensor data.