18 results about "Computability" patented technology

The invention discloses a geographic space entity vectorization method and a geographic space entity vectorization system based on multi-modal fusion and metric learning, which are used for solving the problems that the traditional geographic information representation is single and multi-source data cannot be effectively fused. The method comprises the following steps: firstly, carrying out multi-modal feature extraction on text and coordinate information of a geographic space entity; then, through a neural network model with an independent coding stream and a deep fusion layer, intelligently fusing the multi-modal features and projecting the multi-modal features to a unified embedded vector space; and finally, optimizing the model by adopting a metric learning normal form through a hybrid triple sampling strategy combining semantic and spatial constraints. The invention also provides a system for realizing the method. The system comprises a feature engineering module, a training set generation module, a model training module and a reasoning application module. The system can automatically complete the whole process from data preprocessing, model training to vectorization reasoning. Compared with the prior art, the method has the advantages that the embedded vector with high semantic distinction degree and accurate spatial perception capability can be generated, and the computability and intelligence of geographic spatial data are effectively improved.

The invention discloses a complex medical quality management and control index automatic calculation method based on an intelligent agent. According to the index semantic model construction method provided by the invention, the automatic conversion of the medical quality control indexes from a natural language to structured semantics is realized, so that the index definition has computability and mobility, the dependence of manual analysis and script configuration is eliminated, and the standardization, generalization and reuse efficiency of the index definition is remarkably improved. Through multi-source data semantic packaging and an MCP service abstraction mechanism, semantic unification and interface standardization of multi-source heterogeneous data such as electronic medical records, inspection information, disease course records and medical advice management are achieved, and a semantic data layer capable of achieving cross-system access is constructed; the problems of data dispersion, field isomerism and interface incompatibility in a traditional system are effectively solved.

The invention provides an express behavior risk early warning method based on high-dimensional information, and belongs to the field of artificial intelligence and express logistics, and the method comprises the following steps: 1, collecting multi-source data and constructing a knowledge graph; and step 2, GNN-based courier behavior feature extraction: comparing historical behavior features to calculate a courier behavior deviation degree so as to identify a potential risk. Step 3, time sequence risk dynamic modeling; step 4, reinforcement learning-driven risk early warning decision making; and step 5, risk assessment and visual output. According to the method, through multi-source data acquisition and knowledge graph construction, dynamic time embedding, reinforcement learning optimization and multi-scale risk assessment, efficient extraction and accurate risk early warning of courier behavior characteristics are realized, and the method has strong innovativeness, computability and data consistency and is suitable for complex risk detection requirements of express logistics scenes.

The invention provides a metadata structure, a data processing method and a storage medium. The metadata structure is a structured data object on a computer readable medium, can be read and written by a computer program, and adopts a framework of three parts: a basic index part, which is used for uniquely identifying educational resources and supporting retrieval, and at least comprises a unique identification field and a resource classification / retrieval attribute field; the core attainment vector part is used for quantitatively representing an educational resource childbearing target for intelligent calculation and comprises an N-dimensional attainment vector, the dimension key is c01 to cN, and the value of each dimension is a real number in a preset numerical value interval; and the association application part is used for constructing an inter-resource relationship and learning evidence agreement which at least comprises a field for describing a resource dependency relationship and a rule field which is executable by a computer and is used for extracting evidence from learning interaction data. According to the scheme, structuralization and computability of the attestation information are realized.

According to the electric reactor state prediction method and device, the storage medium and the computer equipment provided by the invention, the actual operation state of the electric reactor can be reflected more comprehensively by acquiring the multi-dimensional fusion features of the target electric reactor. And then inputting the multi-dimensional fusion features into a pre-trained fault diagnosis model, and realizing accurate identification of the operation state and the fault position of the reactor on the premise of not influencing other equipment or the reactor. And on the basis, further determining a finite element simulation model corresponding to the target reactor, applying a fault simulation equation matched with the running state to a position corresponding to the fault position in the finite element simulation model, and then solving the finite element simulation model so as to realize calculation and reproducibility of the fault evolution process. Therefore, the running state characteristics of the target reactor in a future period of time can be predicted, the potential fault development trend of the reactor can be predicted in advance, and the reliability of the operation and maintenance decision of the reactor can be effectively improved.

The invention relates to the technical field of pre-diagnosis and health management, in particular to a liver, gall, pancreas and spleen nursing decision-making system based on a knowledge graph, which comprises a term collection module, a semantic recognition module, a path positioning module, a node verification module and a decision-making reasoning module. According to the method, structured extraction of symptom expression and nursing behaviors is achieved by collecting nursing record content and constructing a semantic input sequence, a term pair list is generated through combined fragment recognition to improve the term expression standardization level, and a semantic contrast mechanism is formed through keyword cross mapping to enhance matching association between terms and path nodes. Node pairs which do not conform to a logic sequence are eliminated through path semantic structure verification to guarantee the closing performance of a path structure, accurate butt joint between a nursing scene and knowledge reasoning is achieved through path entry marking and context mapping, nursing information expression is systematic and normative, the path generation process is made to have continuity and logicality, and the path generation efficiency is improved. And the decision record has traceability and computability.

This invention discloses a hotel room control method and system based on PLC communication technology, belonging to the field of building automation control technology. The method includes collecting PLC detection messages in the guest room and generating a guest room PLC link fingerprint set, constructing a guest room electrical topology twin graph. Based on the guest room PLC link fingerprint set and the guest room electrical topology twin graph, the method calculates the guest room PLC link volatility, and generates a scene action breakdown set based on the guest room PLC link volatility and the guest room electrical topology twin graph. Based on the scene action breakdown set, a scene-level control sequence is generated, and feedback correction update results are generated based on the execution feedback. This invention, with link feature acquisition, graph data modeling, state variable calculation, action set splitting, queue arrangement, and feedback update as the main lines, forms a digital processing closed loop for guest room end-point control, transforming the control process from fixed template execution to an adaptive processing flow based on state data, enhancing the computability, associativity, and updability of the control data.

The invention belongs to the technical field of intelligence analysis, and particularly discloses an artificial intelligence-based military open source intelligence analysis method, which comprises the following steps of: constructing a multi-source channel of a spatial dimension, a time dimension and a behavior dimension to form a to-be-checked data set; performing multi-dimensional fact checking and screening on the data set to obtain a fact element set; key entities are identified, background information is expanded, and an event background logic chain is constructed; performing quantification processing on the logic chain nodes, obtaining key parameters and constructing an index system; inputting the index system into a hybrid prediction model dynamically updated along with the change of the event to output an event prediction conclusion; the method has the following advantages that the information credibility is improved through multi-source data fusion and cross validation, information calculation and traceability are achieved through node quantization and parameter extraction, the event trend is dynamically deduced through the hybrid prediction model, the prediction precision and adaptability are improved, and efficient and reliable information support is provided for military decision making.

The invention relates to the technical field of gas pipe network safety management, in particular to a standard knowledge-driven automatic gas hidden danger troubleshooting method. The method comprises the following steps: carrying out knowledge atomization processing on a fuel gas safety standard text, decomposing the fuel gas safety standard text into knowledge atoms which cannot be divided and mapping the knowledge atoms to a pre-established ontology category; the knowledge atoms are converted into machine-readable condition-action rules through rule templating; identifying scene objects from the multi-source data, extracting elements of the scene objects, and classifying the elements into a unified ontology system; a space-time cube coordinate system fusing time, space and semantic dimensions is constructed, and multi-dimensional alignment matching of scene elements and standard knowledge atoms is achieved; and automatically triggering a hidden danger checking task based on a matching result, generating a task list after multi-source cross validation, and assigning and executing the task list. According to the method, the computability and the dynamic recombination of the standard knowledge are realized, the automation degree and the judgment accuracy of hidden danger checking are improved, and the technical problem that multi-source data fusion and rule execution depend on manpower is effectively solved.

The invention discloses an ecological system and service function assessment knowledge graph completion method based on small samples, and belongs to the technical field of ecological environment management, and the method comprises the steps: automatically extracting an entity-relationship-entity triple through a GraphRAG tool, and constructing an initial knowledge graph; performing topology analysis on the initial knowledge graph, and evaluating structural integrity; for isolated nodes and weak association regions in the initial knowledge graph, obtaining candidate association nodes by adopting semantic vector retrieval; constructing a Prompt example based on a Few-Shot strategy, verifying the rationality of the candidate relationship through an LoRA low-rank fine-tuning large model, and supplementing missing edges; and fusing the new relationship which passes the verification into the knowledge graph. According to the method, a knowledge graph completion technology is applied to an integration scene of ecological environment standard knowledge, the limitation that traditional text knowledge is scattered in expression and insufficient in semantic association is broken through, a sustainable maintenance ecological knowledge graph system is created, and the organizational degree and the computability of ecological assessment knowledge are improved.

The present application relates to a kind of equipment combat system intelligent level evaluation method, belong to combat system capability evaluation technical field, solve the rough evaluation method in prior art, inaccurate, high uncertainty, the problems such as complex evaluation process and poor interpretability.The evaluation method of the present application is from the intelligent degree of the model used in the introduced intelligent technology itself as the starting point, the bottom layer index of the minimum decomposable layer is decomposed to the minimum decomposable layer of equipment combat system layer by layer, the measurement factors of bottom layer index are analyzed from multiple aspects, the evaluation result is accurate, and have good interpretability.The evaluation model used in the evaluation bottom layer index value of the present application, step is simple, simple to realize, easy to integrate into simulation system or evaluation software, have good computability and scalability.

The invention relates to the technical field of oil refining simulation, and provides a reaction device scheduling method, device and equipment for molecular-level oil refining simulation, and the method comprises the following steps: constructing a reaction device set for molecular-level oil refining simulation into a target data structure; performing path accessibility detection on the reaction devices in the target data structure, and sequentially writing the reaction devices meeting the path accessibility into a linear operation sequence table; performing distributed optimization on the reaction devices in the linear operation sequence table to obtain a scheduling sequence directed graph; carrying out computability detection on the reaction devices in the scheduling sequence directed graph according to the scheduling sequence; and carrying out dynamic equilibrium simulation of molecular-grade oil refining on the reaction device meeting the computability. According to the embodiment of the invention, the robustness and simulation efficiency in the molecular-level oil refining simulation process can be improved.

The invention relates to the technical field of edge state synchronization, in particular to a multi-access point edge computing service state synchronization method and system based on a deep Q network. The method comprises the following steps: acquiring access point data; constructing an EDPT index based on the obtained access point data; constructing an MH-D3QN network model, wherein MDP modeling is carried out based on the constructed EDPT indexes; calculating an access point Q value based on an MH-D3QN algorithm; according to the Q value of the access point, carrying out revenue sorting; carrying out model training on the basis of the constructed MH-D3QN network model; and solving difficulty caused by combination explosion is effectively avoided, so that the updating strategy still has computability and expandability in a multi-access-point scene.

The invention belongs to the technical field of coal mine intellectualization, and particularly relates to a coal mine intellectualization system function hierarchy division method and system based on multi-dimensional quantification, and the method comprises the steps: carrying out the quantitative scoring based on three basic attributes of a function data dependence range, a decision influence range and a decision generation mode, and determining an initial ideal deployment hierarchy; for functions with contradictions among attributes, the functions are decoupled into a decision generation unit and a decision execution unit, ideal hierarchies are divided for the decision generation unit and the decision execution unit respectively, and a primary architecture of cloud edge collaboration is formed; all cross-level collaborative links generated after decoupling are identified, and the risk level of each link is quantified by using a risk assessment matrix from two dimensions of link failure consequence level and link real-time performance and reliability requirements; and calculating an overall collaborative risk index and risk density of the scheme, and outputting a final landing deployment scheme through a deployment decision matrix in combination with system function cohesion analysis. And standardization and computability of the division process are realized.

The invention provides a water and electricity knowledge storage and access method and system based on graph calculation and reasoning optimization, and relates to the field of data storage and access. Comprising the following steps: acquiring first water and electricity knowledge data which is an original information set in a specific field; decomposing the first water and electricity knowledge data according to graph calculation parameters to obtain second water and electricity knowledge data; performing fitting calculation on the second hydroelectric knowledge data and a preset forward reasoning model to obtain to-be-stored access data; inputting the access data to be stored into a storage container, and activating a linkage strategy of an access port and the storage container; the method solves the problem that a traditional method can only return a static result and cannot provide intelligent support, realizes the crossing of knowledge from static storage to dynamic reasoning access, improves the structured degree and computability of knowledge data, improves the high efficiency and controllability of data access, and improves the user experience. Therefore, reliable and safe storage and access are realized when the data volume is huge and diversified.

The present application relates to a hydrogen-doped natural gas pipeline failure risk evolution probability calculation method, belonging to the field of pipeline reliability and safety evaluation. The present application is composed of two parts of causal inference and probability machine learning, and quantitatively analyzes the failure accident evolution of hydrogen-doped natural gas pipeline. Based on the existing research reports, relevant literature and the combination of expert scoring method and fuzzy mathematics method, the prior probability of each node of the Bayesian network is obtained, and the overall prior probability distribution is obtained. The latest collected data of one or more variables is used as evidence information, which is input into the Bayesian network to update to obtain the posterior probability distribution of the failure accident evolution of the hydrogen-doped natural gas pipeline. Based on the analysis of pipeline failure data, the present application quantitatively analyzes the accident evolution probability of hydrogen-doped pipeline, provides support for the final quantitative risk evaluation of hydrogen-doped natural gas pipeline, enhances the accuracy of risk evaluation, and has the advantages of strong interpretability, good scalability and strong computability.

This invention discloses a method and system for analyzing and computable input of outcome indicators in meta-analysis RCTs, relating to the fields of medical statistics and literature data processing. The method first organizes a set of statistical elements with outcome indicators, detection time points, and comparison relationships as their scopes; then, it abstracts the meta-analysis formula into statistical element slot templates and performs slot matching within the outcome scope; when a target slot is missing, it is filled in based on preset substitution rules; after computability verification, a standardized meta-analysis input structure is generated. This invention solves the problems of existing technologies where meta-analysis data preparation is highly dependent on manual processes, computability verification is difficult to automate, and missing elements cannot be automatically filled in. It transforms manual judgment and calculation into a reusable structured process, significantly improving the efficiency, consistency, and data utilization of large-scale RCT outcome data processing. The output standardized data can be directly used by existing meta-analysis software.

The present application relates to the technical field of geological disaster evaluation, and discloses a ground subsidence and ground fissure disaster evaluation method and system based on a knowledge graph and a graph neural network, comprising the following steps: S1, self-learning ground subsidence and ground fissure coupling danger evaluation knowledge graph construction and rule initialization; S2, knowledge-driven multi-source original data acquisition; S3, evaluation unit construction and unit-level feature matrix generation; S4, original graph structure construction; S5, multi-relation graph structure expansion and feature fusion based on the self-learning knowledge graph; and S6, knowledge-driven combined graph convolution network-based coupling disaster danger evaluation.The present application constructs a self-learning knowledge graph and rule base for ground subsidence and ground fissure coupling danger evaluation, stores domain mechanism knowledge, expert experience and prior constraints in a structured manner, realizes the computability and continuous self-learning update of knowledge, and overcomes the defects of strong subjectivity and static solidification of traditional knowledge-driven models.