141 results about "Reasoning algorithm" patented technology

The invention relates to a brilliance large-scale model culture knowledge generation and retrieval method combined with a knowledge graph. The method comprises the steps that entity extraction, relation modeling and dynamic completion processing are carried out according to multi-modal culture data, a culture knowledge graph is constructed in combination with a fuzzy time reasoning algorithm and a cross-modal alignment mechanism, and the multi-modal culture data comprises an unstructured culture text, image data and space-time metadata; the space-time metadata comprises text space-time metadata and image moment metadata; performing semantic alignment processing by combining a large model through a user query statement and a culture knowledge graph to obtain joint representation of cross-modal semantic alignment; and performing multi-modal retrieval-generation joint optimization based on the joint representation to obtain cultural knowledge description and associated multi-modal content. According to the method, through multi-modal data fusion, dynamic knowledge completion and fuzzy time reasoning, culture knowledge description can be accurately generated, associated multi-modal content can be retrieved, and the accuracy and the intelligent level of culture knowledge service are improved.

The invention discloses a pharmaceutical quality traceability decision-making method and system based on a dynamic knowledge graph, and relates to the technical field of pharmaceutical quality control. The method comprises the following steps: defining a dynamic knowledge graph containing quality-related entities, wherein the dynamic knowledge graph at least comprises a quality event entity, a production element entity and a time sequence causal relationship between the quality event entity and the production element entity; capturing production data related to quality in real time, and generating event data with a quality rule identifier based on a preset triggering condition; and carrying out dynamic state modeling on the production element entity to generate a state vector containing a time sequence attenuation characteristic and a quality influence weight. By constructing the dynamic knowledge graph and designing the time sequence causal reasoning algorithm, real-time capture, dynamic modeling and causal relationship analysis of production data are achieved, the defects of the traditional technology in real-time performance and relevance are overcome, and an efficient and accurate intelligent solution is provided for pharmaceutical quality traceability.

The invention discloses a dispatching automation fault intelligent processing system based on knowledge reasoning navigation. The dispatching automation fault intelligent processing system comprises a dispatching automation system environment sensing module which obtains machine room environment multi-source data in real time to serve as a data source; the intelligent analysis module performs preprocessing and labeling on the image type data and the text type data, then performs knowledge extraction to obtain entities and relationships among the entities, performs data fusion on the obtained entities, constructs an automatic duty knowledge graph, forms a scheduling automation system fault research and judgment mechanism reasoning model, and performs fault diagnosis on the scheduling automation system. A fault alarm event is generated, a knowledge reasoning environment is constructed, and a fault research and judgment conclusion is obtained through reasoning of a knowledge reasoning algorithm; an auxiliary decision-making module performs reasoning matching on the fault alarm event and the automatic duty knowledge graph through an RE2 model to obtain a fault handling auxiliary decision-making opinion; and constructing a fault exception handling flow navigation chart, and generating a fault handling coping strategy. The operation and maintenance management level of the dispatching automation machine room is improved.

The invention relates to a higher education personalized hierarchical teaching method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-dimensional learning data, carrying out the time-space alignment processing, and generating a synchronous multi-dimensional data set; performing spatial-temporal feature fusion and time sequence modeling on the data set by using a deep neural network, and constructing a dynamic student portrait; analyzing knowledge mastery degree features in the portrait through a semantic analysis model, and generating a personalized resource recommendation sequence in combination with the knowledge graph; based on the sequence and the portrait, planning a personalized learning path by using a path reasoning algorithm; and carrying out teaching hierarchy binding on the personalized resource recommendation sequence and the learning path to form a hierarchical teaching scheme. Accurate teaching support is provided for individual differences of students, and the teaching effect and learning experience are effectively improved.

The invention belongs to the technical field of aviation energy design, and particularly provides an aviation energy power device fault intelligent inquiry system and method based on a knowledge graph. The method comprises the following steps: firstly, showing a complex data structure and a knowledge relationship to a user in a visual and dynamic manner through knowledge extraction, knowledge graph database construction, knowledge graph visualization and other methods, and constructing a knowledge graph basic framework; according to the method, the depth information of the target object can be extracted from the images from different angles, the fault characteristics of the equipment can be intuitively displayed, and the fault state of the equipment can be visually represented by extracting the depth information of the target object from the images from different angles. The evolution of the equipment state can be tracked through the change of the three-dimensional space; a conversion algorithm of the knowledge graph and the Bayesian network is realized through the Bayesian network, and rapid reasoning of fault causes is realized in combination with a Bayesian network reasoning algorithm; and finally, based on an intelligent inquiry system of a large model, a deep learning technology and a large-scale training model are utilized, and previous fault data and professional knowledge of the aviation energy power device are combined to provide intelligent auxiliary diagnosis support.

The invention discloses an intelligent security and protection management system based on an AI large model, and relates to the technical field of security and protection management, the intelligent security and protection management system comprises a modal large model and a chained reasoning module, the chained reasoning module comprises a distributed chained reasoning sub-module, and a preset chained reasoning algorithm process is adopted to execute the following steps: scene segmentation and target detection; quantifying risk factors; constructing a causal relationship; threat scene simulation; and risk assessment calculation. According to the method, the multi-modal large model and the chain reasoning module are designed, multi-modal information is extracted and fused in real time to form the risk state matrix, the risk situation is deduced step by step, the problem that the multi-causal relationship and dynamic change in a complex scene are difficult to capture in traditional single-layer anomaly detection is solved, the risk identification accuracy is remarkably improved, and the risk identification efficiency is improved. The false alarm and the missing alarm are reduced, and the accurate prejudgment of the global security situation is provided for the system.

The invention provides an intelligent early warning system fusing Internet of Things sensing data and a domain knowledge graph, aiming at the problems of data islands, high false alarm rate, response lag and the like of a traditional park early warning system, and is suitable for park safety prevention and control in industries such as chemical industry, logistics, manufacturing and the like. The knowledge graph is an ideal tool for modeling connection between objective objects in the real world, the data island problem can be effectively solved by constructing the knowledge graph oriented to the smart park safety management field and fusing an intelligent reasoning algorithm, and the accuracy and timeliness of park risk early warning are remarkably improved. Specifically, a whole set of pre-warning system is designed from bottom to top in three aspects of multi-modal knowledge graph modeling, a three-level pre-warning inference engine and a self-adaptive optimization mechanism, and the park pre-warning requirements which meet current intellectualization and manpower cost saving are constructed. The multi-modal knowledge graph relates to six types of ontology concepts, comprises different data types, and comprises an equipment topological relation, environmental parameter association, an emergency plan, risk analysis, attack behavior simulation and an asset attribute model. The third-level early warning reasoning comprises rule reasoning, sub-graph matching reasoning and link prediction reasoning. The self-adaptive optimization technology aims at constructing a feedback learning mechanism, incorporating each early warning processing result into a knowledge graph, and continuously optimizing the object relation weight. In an early warning analog simulation experiment, the scheme of the invention realizes the effects of reducing the false alarm rate by 42% and improving the emergency response speed by 60%, and the feasibility and effectiveness of the scheme are proved.

The invention relates to the technical field of intelligent decision making of production equipment, and discloses a decision making method and system based on a knowledge graph, and the decision making method based on the knowledge graph comprises the following steps: processing input data through a multi-granularity knowledge graph construction system, and obtaining a multi-scale knowledge graph; processing the real-time sensor data and the multi-scale knowledge graph through a time sequence knowledge dual representation learning framework to obtain a dynamic representation model; processing the dynamic representation model and the multi-scale knowledge graph through a time-varying causal propagation network decision system to obtain a decision analysis result; processing the decision analysis result and the multi-scale knowledge graph through a multi-hypothesis reasoning algorithm to obtain a root cause analysis report; a multi-scale knowledge graph is constructed by fusing multi-source heterogeneous data, and intelligent decision with high accuracy and strong interpretation is realized by combining time sequence knowledge dual representation learning and time-varying causal propagation network analysis.

The invention relates to the technical field of photovoltaic systems, and particularly discloses a fault identification method and system for a photovoltaic system, and the method comprises the steps: collecting data in real time through environment, electrical parameters and an equipment state monitoring sensor, carrying out the cleaning and standardization, extracting time domain, frequency domain and time frequency features, and screening a feature subset through a correlation analysis and feature importance sorting algorithm; detecting and classifying faults by using a hybrid model composed of an isolated forest algorithm and a random forest classifier; positioning a fault subsystem and analyzing a root cause by means of a hierarchical diagnosis strategy, a graph neural network and a Bayesian reasoning algorithm; and periodically updating the model based on the new data. The method can quickly and accurately identify and position faults, adapts to a complex environment, reduces the operation and maintenance cost, and improves the operation reliability and operation and maintenance efficiency of a photovoltaic system.

The invention discloses a drilling and blasting method tunnel construction safety risk evolution analysis method based on a knowledge graph, and the method comprises the steps: obtaining and preprocessing the text data of a drilling and blasting method tunnel construction accident case, and obtaining a safety risk corpus; a mixed deep learning model is constructed, the safety risk corpus is trained through the mixed deep learning model, and the risk factors and the incidence relation of drilling and blasting method tunnel safety are obtained; constructing a drilling and blasting method tunnel construction safety risk coupling evolution analysis knowledge graph based on the risk factors and the incidence relation, and obtaining a key accident chain and a key edge in combination with a knowledge query and path reasoning algorithm; and quantitatively evaluating independent values and coupling effect intensity of the risk factors by using an interaction matrix algorithm, calculating comprehensive importance scores of the risk factors, and determining key risk points according to the comprehensive importance scores of the risk factors. The problems that in a traditional method, attention to the risk factor coupling effect is insufficient, and accident case data are not fully utilized are effectively solved.

The invention relates to the technical field of mine industry management, in particular to an industrial management system with an industrial configuration picture presentation function, which comprises a heterogeneous data acquisition module which is composed of a plurality of sensors and realizes data fusion through a multi-protocol converter; the three-dimensional dynamic configuration engine covers the geological model construction unit, the real-time data mapping unit and the visual rendering unit; the intelligent scheduling decision module is provided with a transportation path optimization sub-module, a resource allocation sub-module and an emergency response sub-module; and a man-machine interaction terminal. An edge computing node of the heterogeneous data acquisition module adopts an improved LSTM missing data interpolation model; the transportation path optimization sub-module uses an improved ant colony algorithm, the resource allocation sub-module uses an improved multi-objective optimization model, and the emergency response sub-module uses an improved rule reasoning algorithm. The problems of data fusion, path optimization, resource allocation, emergency response, visualization and the like in mine industry management are effectively solved, and the efficiency, the safety and the intelligent level of mine management are improved.

The invention relates to a water conservancy facility operation and maintenance method based on multi-source data, and the method comprises the steps: collecting multi-source heterogeneous data of a water conservancy facility, and obtaining a standardized multi-dimensional data matrix; multi-modal features of the standardized multi-dimensional data matrix are extracted through a graph neural network fusion algorithm, and a fusion feature vector is obtained; performing fault diagnosis on the fusion feature vector through a knowledge graph reasoning algorithm automatically constructed by a domain ontology to obtain fault diagnosis information; based on the fault diagnosis information, performing facility state evolution analysis through a space-time coupling degradation prediction model to obtain a space-time degradation prediction result; according to the space-time degradation prediction result, state evaluation of the water conservancy facilities is carried out through a dual-objective optimized dynamic weight distribution algorithm, and an operation and maintenance state evaluation result is obtained; and on the basis of the operation and maintenance state evaluation result, performing cross-basin cascade fault prevention and control on the water conservancy facilities to obtain a cascade fault prevention and control scheme. The method improves the prediction precision of the degradation of the water conservancy facilities, and enhances the anti-risk capability of the water conservancy infrastructure.

The invention discloses a multi-dimensional digital twinning modeling method. The method comprises the following steps: S1, performing dynamic fusion and real-time modeling on multi-source heterogeneous data in a complex scene; and high-precision multi-dimensional data expression is formed. S2, based on a cross-domain knowledge graph and a semantic reasoning algorithm, establishing semantic association among multi-dimensional data; and the cross-domain data fusion problem is solved. And S3, performing real-time evaluation and early warning on the system state through an anomaly detection and trend prediction technology. The defects of a traditional digital twin system in multi-scene data fusion and predictive analysis are overcome.

The invention relates to a fault judgment system, method and device based on a knowledge graph and a medium, and belongs to the technical field of reliability analysis, and the system comprises a data collection module which is used for obtaining to-be-diagnosed information, the to-be-diagnosed information comprises a device type, a working condition and real-time monitoring data of a target device, the real-time monitoring data comprises operation parameters and state information; the fault diagnosis reasoning module is used for inputting the to-be-diagnosed information into a pre-constructed knowledge graph and performing fault diagnosis on the to-be-diagnosed information by adopting a graph reasoning algorithm to obtain a reasoning result of the target equipment, and the reasoning result comprises a fault type, a fault reason and an influence factor of the target equipment; and the diagnosis result analysis module is used for evaluating the reasoning result to obtain an evaluation result, displaying the reasoning result and the evaluation result based on a visual tool, and generating a fault diagnosis report. According to the method, the accuracy of fault judgment is effectively improved, and misjudgment caused by insufficient experience in a traditional method is reduced.

The invention discloses a mesh belt furnace multi-equipment collaborative material tracking and graphical monitoring method, and relates to the technical field of industrial Internet of Things, and the method comprises the steps: carrying out the dynamic feature extraction of material block data based on a dynamic twin database, carrying out the analysis through combining a causal reasoning algorithm, and generating an enhanced data set with a causal label; on the basis of the enhanced data set with the causal label, when material tracking abnormity is detected, constructing a dynamic causal graph, calculating the contribution degree of equipment operation parameters to the abnormity through a Bayesian network, and generating a repair instruction set; and the repair instruction set is issued to the PLC to adjust the equipment operation parameters, the state change of the material blocks is monitored in real time, and when the deviation exceeds the tolerance range, the causal graph is triggered to update and a regulation and control instruction is generated to regulate and control the equipment operation parameters. According to the method, the causal network node weight is updated in real time by adopting the dynamic time warping algorithm, and accurate positioning of an abnormal source and intelligent generation of a repair strategy are realized.

The invention belongs to the technical field of disaster information early warning, and particularly relates to a disaster information fusion and semantic reasoning algorithm based on a cross-modal diagram attention mechanism, which comprises a data preprocessing module, a cross-modal diagram construction module, a cross-modal diagram attention mechanism module, a semantic reasoning module and a result output module. According to the method, data sources from multiple modals can be effectively fused through a cross-modal graph attention mechanism, so that more comprehensive disaster information is provided, and meanwhile, the graph attention mechanism is realized through the graph attention mechanism, so that the model can dynamically and adaptively adjust the weight according to the correlation of different modal data, and the accuracy of information fusion is improved. The graph neural network is adopted for semantic reasoning of disaster events, the types and influences of disasters can be accurately predicted, decision support can be provided, disaster emergency management personnel can be helped to respond quickly, various different types of disaster data can be processed, and the method has high adaptability to data sources and formats.

The invention discloses a knowledge graph question-answer interaction method and system based on artificial intelligence, and the method comprises the steps: receiving a natural language question input by a user, carrying out the hierarchical analysis of entity words and intention words in the question through a dynamic weight distribution mechanism, and generating an enhanced semantic vector containing entity association strength and intention probability distribution; traversing the knowledge graph based on the enhanced semantic vector, and constructing a hierarchical query sub-graph comprising a core entity, an associated entity and an implicit relationship; performing reasoning calculation on the hierarchical query subgraph by adopting a multi-path collaborative reasoning algorithm to generate a candidate answer set; and performing semantic consistency verification and redundancy elimination on the candidate answer set, generating an adaptive answer and an association explanation path in combination with historical interaction preference of the user, and synchronously feeding back a newly discovered entity association relationship to the knowledge graph for incremental updating to form a question and answer interaction closed loop. By utilizing the embodiment of the invention, the accuracy and interpretability of questions and answers can be improved, and the user interaction experience and the information acquisition efficiency are improved.

The invention discloses a version knowledge graph reasoning method and system based on large language model enhancement, and relates to the technical field of dynamic knowledge graphs, and the method comprises the steps: employing a semantic drift detection and compensation mechanism, comparing context coding differences of same entities of different versions in an initial knowledge graph, recognizing drift, and dynamically adjusting entity embedding vectors, outputting a compensation update atlas; an LLM enhanced multi-hop reasoning algorithm is adopted, multi-hop reasoning is carried out on the compensation updating map, symbol reasoning, vector reasoning and context reasoning are carried out in parallel in each hop, and an entity relation reasoning result is obtained through dynamic weight fusion; and superposing an entity relationship reasoning result to the compensation updating graph through a cloud collaborative node, adding an entity edge and automatically maintaining a version history log, and obtaining a reasoning fusion version knowledge graph. Through a multi-hop reasoning algorithm enhanced by a large language model, the deep semantic mining capability and reasoning accuracy of a cross-version entity relationship are effectively improved.

The invention provides an index transaction attribution analysis method, electronic equipment, a storage medium and a product, and belongs to the technical field of artificial intelligence. The method comprises the following steps: in response to an attribution analysis request for transaction of a target index, querying target data related to the target index; calling a plurality of agents, and analyzing the target data from a plurality of dimensions to obtain abnormal data under the plurality of dimensions; inputting the abnormal data under the plurality of dimensions into an attribution reasoning model, and outputting a plurality of candidate root causes causing the transaction of the target index; the multiple candidate root causes, the abnormal data under the multiple dimensions and data of a preset knowledge base are input into an attribution analysis model, an attribution analysis result of the target index transaction is output, and the preset knowledge base is used for storing description data of different indexes and dimensions. The method does not depend on a traditional reasoning algorithm, and the attribution analysis result reasoned by means of the model is more comprehensive and more accurate.

The invention relates to a method for improving the reasoning efficiency of a multi-machine algorithm based on cloud edge collaboration, and the method comprises the following steps: a cloud end issues a reasoning task instruction to an edge end, and the reasoning task instruction comprises at least one reasoning algorithm and at least one video stream address; the edge end responds to the reasoning task instruction, carries out stream pulling scheduling on the video stream address by adopting a time wheel mechanism, and decodes the video stream to obtain image data; the edge end writes the image data into a shared memory area through a shared memory mechanism so as to realize cross-process sharing; the edge end reads the image data from the shared memory area and executes a reasoning algorithm specified in the reasoning task instruction to generate a reasoning result; and the edge end reports the reasoning result to the cloud end. The method has the advantage of efficiently coordinating the strong computing power of the cloud end and the low delay characteristic of the edge end.

The invention discloses an SVG valve hall cooling efficiency evaluation method and system based on a probabilistic graph model, and the method comprises the steps: obtaining original time sequence data which is obtained through the collection of a cooling system multi-parameter monitoring sensor group disposed in an SVG valve hall in continuous T sampling periods; preprocessing the original time series data to obtain a credible time series data set; constructing a Bayesian network topological structure comprising three-level nodes of an environment layer, a component layer and an efficiency layer and causal dependence edges, and optimizing parameters of the Bayesian network topological structure by adopting a maximum likelihood estimation method to form a dynamic Bayesian network model after parameter calibration; and the credible time sequence data set is used as an evidence variable to be input into the Bayesian network model after parameter calibration, calculation is carried out through a belief propagation reasoning algorithm, a final control instruction set is generated through probability weighted scoring processing, the final control instruction set is fed back to a valve group monitoring system, and early warning and automatic load reduction are achieved. The problems of large evaluation deviation and early warning lag in the prior art are solved.

The invention discloses a power grid equipment fault detailed rule generation method and system based on a dynamic knowledge graph. The method comprises the following steps: establishing a knowledge graph by utilizing power grid equipment fault text data; extracting features from a power grid equipment fault text through a fault self-diagnosis module by applying an NLP technology, performing fault type diagnosis through text classification, deep learning and an inference algorithm, and continuously optimizing a diagnosis result in combination with operation and maintenance feedback; and according to a fault diagnosis result, automatically generating related supervision rules by utilizing causal reasoning of a knowledge graph. Intelligent diagnosis of power grid equipment faults can be realized, technical supervision suggestions and operation and maintenance measures can be generated, the understanding and operation efficiency of operation and maintenance personnel on the power grid equipment faults is improved, and intelligentization and high efficiency of power grid equipment management are realized.

The invention provides a user digitization level-based digitization transformation service recommendation method and a user digitization level-based digitization transformation service recommendation system. The method comprises the following steps: constructing a five-dimensional evaluation model; acquiring multi-source data in real time; inputting the multi-source data into a five-dimensional evaluation model, and evaluating the digital level of the enterprise based on a preset evaluation algorithm and weight; constructing an industry knowledge graph comprising enterprises, service providers, solutions and technical standard nodes; the digital service is matched based on a mixed recommendation algorithm, the mixed recommendation algorithm comprises a collaborative filtering algorithm, a content matching algorithm and a knowledge graph reasoning algorithm, and a recommendation result is generated through a vectorization retrieval and optimization algorithm; and constructing a capability matrix including technical capability, implementation experience and service capability dimensions, and recording historical performance data of the service provider through a block chain technology to dynamically evaluate the capability of the service provider. The actual digital level of an enterprise can be accurately evaluated, and the accuracy of service provider matching is improved.

The invention relates to the technical field of medical informatization, and discloses a team construction method and system for whole course case management, and the method comprises the steps: integrating medical data and non-medical Internet of Things data through a cross-domain data fusion engine to generate a patient behavior map, carrying out the reverse deduction of wearable equipment data as an inducement based on a reverse reasoning algorithm, and triggering hierarchical intervention, detachable combination and dynamic pricing of points are realized by using a block chain smart contract; according to the system, the data island limitation of traditional medical monitoring is avoided, the risk inducement can be reversely locked before the physiological indexes are abnormal through the implicit association between the smart home power utilization frequency and the medicine use record, accurate early warning is achieved, and the safety of the system is improved through multi-modal data collaborative analysis and deep coupling of sandbox rehearsal decision and the smart contract. And the technical jump from passive treatment to active health management is realized.

The invention discloses a cross-regional rescue force distribution method based on a dynamic resource game, and the method comprises the following steps: S1, collecting disaster data, carrying out the standardization processing, and extracting a feature set; s2, constructing a disaster scene graph based on the feature set, and generating a preliminary disaster scene model; s3, defining a game model, and generating a preliminary resource allocation model; s4, introducing an anti-factual reasoning algorithm, and optimizing the resource allocation model; s5, constructing a resource excitation module, and optimizing an excitation strategy; s6, predicting a resource demand based on the excitation strategy, and generating a scheduling scheme; and S7, in combination with the feedback information, generating a cross-regional rescue distribution scheme. Through the optimization algorithm based on the dynamic resource game, efficient, fair and dynamic resource allocation of cross-regional rescue force is realized, and the disaster rescue response speed and the resource utilization efficiency are remarkably improved.

The invention relates to the field of power system automation, and discloses an intelligent power distribution network feed automation control system based on multi-source information fusion, which comprises a multi-source sensor module used for collecting current data in a power distribution network in real time, and an information fusion module used for receiving the preprocessed current data and sending the preprocessed current data to the power distribution network. The multi-source fusion module is used for carrying out multi-source fusion processing on the current data by adopting a Bayesian reasoning algorithm, the fault positioning module is used for receiving a fusion result, and the optimal control module is used for receiving a grounding fault occurrence position and adopting an optimal control theory and a dynamic planning algorithm; the invention further discloses an intelligent power distribution network feed automatic control method based on multi-source information fusion. The method comprises the following steps of an acquisition stage, a fusion stage, a positioning and decision-making stage and an execution and self-healing stage. According to the invention, by constructing the Bayesian fusion and dynamic programming control mechanism, the purposes of more accurate ground fault positioning, more efficient strategy removal and more adaptive response process are achieved.

The invention relates to the technical field of data processing and risk monitoring, and discloses a performance risk intelligent monitoring and collaborative response method and system, and the method comprises the steps: an edge sensing node calculates the compression characteristics of performance flow data through employing a mean aggregation algorithm according to a current sliding window parameter, and transmits the compression characteristics to a cloud end; the cloud decision-making platform executes data standardization management, calculates performance risk probability by using a model reasoning algorithm, and calculates feature uncertainty indexes by using a game theory interpretation method and a variance statistical method; a feedback control strategy module generates a self-adaptive sampling control instruction according to the index, drives an edge node to dynamically update a sliding window parameter, and constructs an acquisition feedback closed loop; and the collaborative response module calculates a response priority by utilizing multi-dimensional weighted logic, calculates desensitized data by utilizing a differential privacy algorithm and then executes response. According to the invention, adaptive adjustment of data acquisition granularity and safe cooperative processing of risk events are realized.

The invention belongs to the technical field of intelligent medical treatment, and discloses an ophthalmology department hospital guide knowledge graph construction system based on improved gradient lifting. Comprising the steps of collecting multi-source ophthalmology original data; performing word segmentation processing on the multi-source ophthalmology original data to obtain ophthalmology medical vocabularies, and dividing the ophthalmology medical vocabularies into disease vocabularies and disease vocabularies; identifying professional vocabularies in the disease vocabularies, and constructing a rough reasoning algorithm and a fine reasoning algorithm; according to the disease vocabularies, the specialized vocabularies and a predefined disease weight set, an ophthalmology entity relation network is constructed, a rough reasoning algorithm and a fine reasoning algorithm are fused, and an ophthalmology hospital guide knowledge graph is constructed; according to the invention, complex symptoms can be dynamically analyzed, the diagnosis accuracy is improved, the medical resource configuration is optimized, and the self-evaluation requirement of the patient is met, so that the rapid response and accurate recommendation of the user symptoms are realized, and the medical treatment efficiency and the medical service quality of the patient are improved.

The invention discloses a multi-scale target detection method based on a YOLOv8-SAHI model, which optimizes and improves the network structure of the YOLOv8 based on the YOLOv8. Firstly, in order to cope with target scale and category diversity challenges, variable kernel convolution is adopted to replace a common convolution in a backbone network; then, a C2f-TA module is designed, so that the model is focused on a target area, and interference of irrelevant information is effectively suppressed. Finally, in order to solve the problem of low detection precision of the small target, a slice-assisted super reasoning algorithm is introduced, and more detailed feature information is obtained by enhancing pixel representation of the small target. According to the method, the adaptability of the model to targets with different scales in complex backgrounds such as a construction site is improved, the small target detection capability is particularly enhanced, the phenomena of false detection and missing detection are effectively reduced, the detection precision is improved, and on-site monitoring and safety management are enhanced.

The invention discloses a traditional Chinese medicine whole industry chain big data construction method and system, and relates to the field of data links, and the method comprises the steps: generating a standardized metadata template for seven links of a traditional Chinese medicine industry chain; block chain nodes are deployed in each link of an industrial chain, timestamp fingerprints are generated for key process data, and the key process data are automatically chained for evidence storage; constructing a data fusion model based on a graph neural network, and generating cross-link associated data; constructing a traditional Chinese medicine ontology knowledge graph, and mapping the association relationship between the cross-link associated data in the graph; full-chain digital tracing from implantation to clinic is realized through a path reasoning algorithm; based on the hybrid collaborative storage architecture, access management is carried out through dynamic fragmentation strategy data points; and constructing a process prediction model, detecting process parameter deviation, and carrying out risk early warning. The method has the advantages that through block chain evidence storage and combination of the graph neural network and the knowledge graph, full-chain digital tracing and risk early warning from implantation to clinic are realized.