31 results about "Constraint analysis" patented technology

The invention relates to the technical field of equipment management, in particular to an equipment full-life-cycle digital twinborn decision-making system enabling a large model. Comprising a digital twin modeling unit; a large model enabling analysis unit, wherein the large model enabling analysis unit adopts a hydroelectric equipment multi-modal causal constraint analysis model; a whole-process closed-loop management and control unit; and an intelligent decision output unit. According to the method, the multi-modal causal constraint analysis model adaptive to the working condition of the hydroelectric equipment is constructed, and a causal chain verification backtracking mechanism is introduced, so that the problem that the reasoning result lacks logic verification is effectively solved, the logic consistency of a fault reasoning conclusion is guaranteed, and the reliability of decision output is improved; through a scene adaptation mode of'pre-training + fine tuning 'of a large model, multi-modal feature fusion processing and deep linkage of a workflow engine and a digital twinborn body, full-life-cycle management requirements of equipment are fully covered, and the refinement and intelligence level of hydroelectric equipment management is further improved.

The invention relates to an electric power customer service task processing method and device based on multilevel decomposition and iterative enhancement and a storage medium. The method comprises the following steps: executing intention analysis processing and constraint analysis processing on first user request information to obtain target task structured description information; determining a target subtask sequence according to the target task structured description information; and determining at least one abnormal sub-task in the execution process of the target sub-task, and adjusting and executing the target sub-task sequence according to the at least one abnormal sub-task. According to the method, through organic integration of semantic analysis, task decomposition, dynamic planning and iterative optimization, the problems of semantic understanding deviation, low decomposition accuracy and execution disjunction of complex task processing in electric power customer service are solved, and the task processing success rate and the user experience are remarkably improved.

The invention discloses a lossless evaluation method and device for the repair completion degree of a lining void disease of a hydraulic tunnel. The method comprises the following steps: acquiring a void geometric boundary, and constructing a corrected slurry time-varying electrical constitutive model based on fusion of laboratory test and field embedded sensing data; collecting a ground penetrating radar response sequence in a plurality of curing ages after grouting; performing constraint analysis on the ground penetrating radar sequence by using a multi-physical field coupling model or a physical constraint machine learning model containing slurry seepage, solidification dynamics and electromagnetic wave propagation, and reconstructing a slurry space filling state field; and inferring a receiving state of a far-end node in combination with the void-fracture network topology model, generating a residual void risk quantitative index, and generating a refilling decision. According to the method, through an observation-estimation-prediction-control closed loop mechanism, the problems of model parameter distortion and uncontrollability of a far-end blind area in a traditional method are solved, and accurate quantitative evaluation of the repair quality is realized.

The invention provides a manufacturing decision system-oriented modular functional architecture and a collaborative mechanism thereof, and the structural organization and reusable collaboration of a manufacturing decision process are realized by constructing a multi-layer capability system, a functional module system and a standardized behavior interface. The architecture comprises a data interface module, a decision-making target module, a disturbance response module and a fusion decision-making module, and defines a data flow, a control flow and a trigger relationship among the modules. The system completes target expression, constraint analysis, disturbance identification and collaborative decision generation through standardized access of multi-source data such as orders, productivity, materials and processes, and forms an interpretable and executable decision link. According to the invention, modularization, interpretability and intelligent cooperation of the manufacturing decision-making system are realized, and multi-scene and multi-industry manufacturing decision-making application is supported.

The invention relates to the technical field of electric power Internet of Things communication, and discloses a multi-party computing driven electric power Internet of Things and computing network fusion secure communication method, which comprises the following steps of: firstly, acquiring a node state and a link communication parameter, and constructing a real-time computing network state matrix in combination with topology; performing security-time delay double-constraint analysis on the service request based on the matrix to generate a constraint set; thirdly, determining an optimal strategy through joint optimization of a multi-dimensional path and a threshold strategy; and finally, performing elastic fragmentation and multi-path parallel transmission on the original data. Through global perception and cooperative scheduling of computing network resources, dynamic adjustment of security policies is realized, the problem of uncontrollable time delay caused by resource limitation or network fluctuation in the power Internet of Things is effectively solved, and the real-time performance and robustness of communication are remarkably improved while data privacy security is guaranteed.

This application relates to the field of energy and carbon optimization technology, and provides a method and related equipment for coordinated energy and carbon optimization. The method includes: acquiring energy and carbon state data for each system node in a target energy system; identifying the equipment response characteristics of each system node based on all energy and carbon state data; generating a scheduling strategy parameter matrix and a control strategy switching mechanism for the target energy system through constraint analysis based on all equipment response characteristics; and performing coordinated energy and carbon optimization on the target energy system according to the scheduling strategy parameter matrix and the control strategy switching mechanism. This method can improve the accuracy of coordinated energy and carbon optimization.

This invention relates to the field of knowledge graph technology, and discloses an automated entity relation extraction method and system for knowledge graph construction. The method includes: deep cleaning of heterogeneous data to obtain target text data; contextual structure parsing of the target text data to identify core entities and inter-entity event descriptions; latent constraint analysis of the core entities to obtain a set of deep relations; logical conflict determination between the explicit relation statement set and the deep relation set of the target text data to obtain a set of non-contradictory relations; semantic connection completion of the set of non-contradictory relations to construct a fused relation network; and association reinforcement reconstruction of the fused relation network to confirm the final entity relation set of the target text data. This invention can improve the efficiency of automated entity relation extraction in knowledge graph construction.

The invention relates to the technical field of additive manufacturing and intelligent material design, and discloses an AI coordinated regulation elastic resin DLP forming analysis method and system.The method comprises the steps that firstly, a physical constraint analysis model is constructed and trained, and the physical constraint analysis model comprises a polymerization activity analysis model and a DLP printability analysis model; then, constructing a multi-target generative AI model based on the physical constraint analysis model, and operating the model to generate a candidate molecular structure; and performing dual physical constraint analysis on candidate molecular structures by using the physical constraint analysis model, and filtering to obtain an effective molecular structure set. Then, AI collaborative regulation and control are executed, a material process parameter mapping model is established, and initial DLP forming process parameters are determined for the effective molecular structure set; according to the method, the problems that the material design and the forming process are separated, and the experiment trial and error cost is high are solved, and collaborative design and efficient closed-loop optimization of the material and the process are achieved.

The application discloses a supply chain transaction link intelligent generation method, system, device and storage medium. The scheme receives natural language instructions, business events and external data, assembles context information to generate a task object relying on a PCM service bus; an intent, an entity and constraint analysis are completed through a large model, and a standardized task plan is output. A business topology is built relying on a knowledge base and a knowledge graph, an evaluation function is constructed combining semantic weights, rules and constraints to screen candidate links; a plurality of business systems are linked to check conditions such as resources, budgets and permissions and to prune invalid paths; a plurality of intelligent agent tasks are split to drive a heterogeneous system to complete document landing; after a closed loop, experience, cases, vector and graph data are stratified and deposited. The application realizes the conversion of supply chain transaction links from a fixed process to an intent-driven intelligent generation iteration.

This invention relates to the field of geological exploration technology. It discloses a method, apparatus, equipment, and storage medium for geological exploration path planning. The method includes acquiring basic data of the area to be explored and extracting topological features from the basic data to obtain a regional topology map; performing constraint analysis based on the regional topology map to obtain a constraint quantification lookup table; constructing a node connectivity network based on the constraint quantification lookup table; using the node connectivity network as a search vehicle, performing a multi-objective path search for the exploration target to obtain a set of multi-objective candidate paths; resolving conflicts in the multi-objective candidate path set to obtain a set of compliant paths; scoring all paths in the compliant path set based on preset evaluation criteria, and determining the path with the highest score as the optimal geological exploration path. This invention performs path planning based on regional topology analysis and constraint quantification, generating an optimal geological exploration path that balances target coverage integrity and path compliance.

The application relates to the technical field of aero-engines and discloses an AI-physical model coupling driven engine demand analysis method and system, which fuses natural language demand and industry standards by using RAG (retrieval-augmented generation) technology and a large language model, intelligently analyzes multi-dimensional demand including point performance constraints, physical boundary constraints and task target demand through deep coupling of AI semantic analysis and a physical model, checks the feasibility of a design space based on double physical model verification of constraint analysis and task analysis, ensures that the point performance constraints and the task target demand meet the requirements, and realizes intelligent closed loop from fuzzy text to accurate demand specification book design.

The application provides a dynamic data packet scheduling method and system, and belongs to the field of data processing. The application introduces wireless state perception, real-time flow perception and TSN time constraint analysis mechanism on the DS-TT side, and forms a closed-loop control through dynamic scheduling decision and scheduling execution feedback, so as to realize time perception collaborative scheduling of cross 5G network and time sensitive network TSN. The application can reduce the influence of wireless fluctuation on the time delay of high priority data packet, reduce scheduling conflict and improve resource utilization efficiency, guarantee that the key time sensitive network TSN service is completed within the specified time window, and is suitable for deterministic communication scenes such as intelligent manufacturing, rail transit and power automation.

The invention provides a dynamic data packet scheduling method and system, and belongs to the field of data processing. Wireless state sensing, real-time flow sensing and TSN time constraint analysis mechanisms are introduced to the DS-TT side, closed-loop control is formed through dynamic scheduling decision and scheduling execution feedback, and time sensing cooperative scheduling of the TSN across the 5G network and the time sensitive network is achieved. According to the method, the influence of wireless fluctuation on high-priority data packet time delay can be reduced, scheduling conflicts are reduced, the resource utilization efficiency is improved, it is guaranteed that a key time sensitive network TSN service is forwarded in a specified time window, and the method is suitable for deterministic communication scenes such as intelligent manufacturing, rail transit and electric power automation.

The application discloses a stage lighting and lifting equipment linkage control method based on timing constraint analysis, comprising the following steps: constructing a layered timing constraint matrix of lighting actions, lifting actions and safety checking actions, integrating action start-stop relationship, occupation relationship and interlocking relationship into a unified constraint expression, solving the problems of dispersed cross-equipment action relationship and unclear conflict boundary in the existing linkage control; extracting the precedence edge, parallel edge, mutual exclusion edge and recovery edge, generating an action index sequence and a candidate linkage sequence, improving the sequence and continuity of linkage arrangement; determining the to-be-executed linkage segment according to the link state and forming a linkage control sequence according to the difference priority, reducing the influence of communication fluctuation on key lifting actions; and finally, blocking or rearranging the conflict actions through index backtracking, improving the safety, stability and execution consistency of the linkage control.

The invention provides a time sequence driven chip design cross-stage acceleration system and method, and the system comprises a time sequence prediction module which is used for executing time sequence prediction and evaluation operation on received chip design related input data at the pre-layout stage and / or the pre-wiring stage of chip design; the constraint analysis module is used for analyzing the reasonability of the time sequence constraint by adopting a constraint search method according to the time sequence prediction and evaluation result of the time sequence prediction module and generating the optimal clock period constraint; and the integrated optimization module is used for integrating the optimal clock period constraint to a layout stage and / or wiring stage chip design process, interacting with an electronic design automation tool to complete chip design iteration, and evaluating a design acceleration effect. According to the invention, acceleration of chip design optimization and improvement of design efficiency are realized.

The present application belongs to the technical field of data processing, and particularly relates to an FTO risk analysis method and system based on a feature map. The method comprises: obtaining patent files and to-be-implemented scheme materials; performing constraint analysis on the claim text in the patent files to obtain constraint units with necessity levels; collecting associated evidence according to the constraint units to obtain a candidate evidence set comprising identification candidates and evidence source chains; determining feature influence values according to the uncertainty and necessity levels of the identification candidates; constructing a claim feature map based on the constraint units and the candidate evidence set, and constructing a to-be-implemented feature map based on the to-be-implemented scheme materials; performing constraint satisfaction calculation and consistency correction according to the evidence source chains to obtain a corrected satisfaction degree; and generating an FTO risk analysis result according to the corrected satisfaction degree and the feature influence values. The present application can reduce the misjudgment caused by local similarity and evidence splicing, and improve the explainability and traceability of the risk result.

The invention discloses a near-earth orbit spacecraft implementation layer task planning method and equipment, and belongs to the technical field of spaceflight. The method comprises a core planning process, a constraint processing process, a cooperative work process and a staged iteration process. The core planning process comprises demand submission, event layer planning, instruction layer planning and result confirmation; the constraint processing flow comprises constraint carding, initial arrangement, conflict detection, conflict resolution and planning result implementation; the cooperative workflow comprises resource constraint release, demand submission, plan generation, feedback adjustment, countersigning confirmation and operation control implementation; the staged iteration process comprises pre-planning, monthly planning, weekly planning and real-time re-planning. According to the method, a two-layer planning, multi-party cooperation and staged iteration mode is adopted, complex tasks are adapted, resource configuration is optimized, and cooperation efficiency and real-time response capability are improved.

The application discloses a kind of power line loss analysis system and method based on big data, it is related to power line loss analysis technical field, including real-time acquisition power grid equipment operating state and environmental parameter, constructs power line loss dataset;Each power line loss data and line loss rate's rank correlation coefficient is calculated, and strong correlation factor of line loss is output to be weighted, and the real-time data of each line's strong correlation factor of line loss is analyzed by first line loss constraint, whether to meet constraint is judged, and second line loss constraint analysis command is sent out;The strong correlation factor of line loss of each line is analyzed by exponential smoothing residual error method, and second constraint analysis result is obtained, the first constraint analysis result and the second constraint analysis result are comprehensively, and third constraint analysis result is obtained, to third constraint analysis result is rendered line loss rate distribution in real time.Further analysis residual error, capture line aging, nonlinear factors such as load mutation superimposed influence on line loss, combined with multi-stage constraint analysis improves decision robustness.

The application discloses an Excel data knowledge question and answer method based on table structure semantic cooperation, and belongs to the technical field of intelligent question and answer, and comprises the following steps: defining the hierarchical semantic structure, metadata glossary and task category of a cell, and processing an Excel file into a two-dimensional table; generating a cooperative knowledge body for each cell in the two-dimensional table to construct a composite knowledge base; performing multi-constraint analysis on a user question; generating an evidence set based on constraint-driven hierarchical mixed retrieval; guiding a large language model to generate an answer by using a structured prompt; and checking the answer. The application cooperatively models the semantic and structural relationship of table text to improve the expression capability of the real semantics of Excel data, drives hierarchical mixed retrieval through multi-constraint analysis, improves the retrieval accuracy and integrity under complex problems, introduces an explicit executable reasoning mechanism, improves the accuracy and stability of numerical question and answer, constructs an evidence closed-loop checking and self-correction mechanism, and improves the credibility of the answer.

The application discloses a kind of control system and method of ecological discharge flow intelligent gate of hydropower station, it is related to the technical field of hydropower station intelligent control, including, real-time acquisition ecological hydraulic operation data, pre-processing is carried out, and using LSTM model predicts downstream ecological water demand prediction curve;Ecological water demand prediction curve and the ecological hydraulic operation data after pre-processing are combined, and the rule reasoning of fuzzy PD+I control algorithm is carried out to obtain the preliminary control parameter set of gate;By comparing the safety operation constraint condition of gate through numerical constraint analysis method, boundary mapping and smooth adjustment are carried out, and the constraint compliance gate control parameter set is obtained;Based on constraint compliance gate control parameter set and ecological water demand prediction curve, through digital twinborn model, rapid simulation verification is carried out, and the gate control parameter set that is verified is passed;The application is based on the fusion of prediction curve and real-time data by fuzzy PD+I control algorithm, realizes the efficient unity of ecological protection target and gate operation safety.

The invention discloses a power grid risk assessment method and system based on variable component vectors, and relates to the technical field of risk assessment, and the method comprises the following steps: constructing a power grid state transition logic diagram based on a power grid operation constraint information set; based on the power grid state evolution constraint graph, performing constraint analysis on the target operation scene to generate a state reachability label; quantum coding is carried out on the reachable state of the target operation scene based on the state reachability label, and a parameterized quantum circuit comprising a state constraint sub-circuit is constructed; in the variable component sub-optimization iteration process, an inaccessible intermediate state is cut off, and an adjusted variable component sub-circuit is obtained; and solving a power grid state risk function based on the variable component sub-circuit, and outputting a power grid risk assessment result. According to the method, the state reachability label is generated, the feasible power grid state is subjected to quantum coding, and the unreachable state is dynamically cut off in variable component sub-optimization, so that the power grid risk function is solved in the reachability state sub-space, and the problem of searching for the invalid risk state is effectively avoided.

The application discloses a large language model-based fuzzy test driver generation system, which comprises a constraint analysis module, an initial program generation module, a verification analysis and decision module and a repair iteration module. The application can automatically verify and repair codes and generate a fuzzy test driver for deep learning library testing, improves the success rate of API calling code generated by the large language model, and effectively improves the generation quality of the fuzzy test driver and the success rate of the large model code generation task.

The application provides a kind of offshore wind turbine jacket structure dynamic optimization method driven by virtual work principle, comprising the following steps: parameterization establishes offshore wind turbine jacket structure dynamic analysis model;Design constraint analysis is carried out, and the relationship of design constraint to design variable is deduced based on virtual work principle;With the minimum structure quality as optimization goal, optimization objective function is established;Virtual work principle dynamic optimization framework is established, and optimization algorithm is used to optimize design of offshore wind turbine jacket;The optimal structure is checked for dynamic constraint, and whether it meets the constraint condition is judged, if it meets, optimization is ended, otherwise, the size of structure unit that does not meet the constraint is manually adjusted until the structure unit meets the constraint requirement.The application does not need a large number of samples, reduces the number of time-domain dynamic analysis required for optimization, significantly improves the optimization efficiency, reduces the material consumption and construction cost under the premise of ensuring the safety and dynamic performance of the structure.

The application discloses a kind of urban low-altitude multi-scene take-off and landing facility coordinated layout space-time constraint analysis system, it is related to urban low-altitude traffic infrastructure management technical field, including cloud management center, the cloud management center communication connection has following module: scene coordination perception module, integrated multi-agent reinforcement learning and deep Q network, simulate the space-time demand dynamic of four types of scenes of target area, generate scene coordination degree matrix and peak-shaving scheduling strategy, identify the time period and area of sharable facility and output sharing candidate set.The application can dynamically simulate and identify the demand difference and complementarity of four types of scenes, emergency rescue, logistics distribution, tourism and government inspection in space-time dimension by integrating multi-agent reinforcement learning and deep Q network technology, by constructing scene coordination degree matrix and peak-shaving scheduling strategy, the time period and area of sharable facility can be accurately identified, so as to avoid facility duplication and time conflict.

The invention relates to the technical field of energy-carbon optimization, and provides an energy-carbon collaborative optimization method and related equipment, and the method comprises the steps: obtaining energy-carbon state data of each system node in a target energy system; identifying equipment response characteristics of each system node based on all energy carbon state data; based on all equipment response characteristics, generating a scheduling strategy parameter matrix and a control strategy switching mechanism of the target energy system through constraint analysis; and performing energy-carbon collaborative optimization on the target energy system according to the scheduling strategy parameter matrix and the control strategy switching mechanism. According to the method, the precision of energy-carbon collaborative optimization can be improved.

This invention discloses an intelligent auxiliary method and system for software development, relating to the field of software development technology. The method includes the following steps: parsing insurance rule text to obtain a set of insurance rules; constructing business object relationships based on the insurance rule set to obtain a set of business object relationships; performing rule constraint analysis based on the set of business object relationships to obtain a set of rule constraint expressions; generating executable rule descriptions based on the set of rule constraint expressions to obtain a set of rule descriptions; mapping functional implementations based on the set of rule descriptions to obtain a set of implementation logic; and performing traceable consistency verification based on the set of implementation logic to obtain an implementation consistency result. This invention makes the implicit constraints in insurance rules explicit, establishing a clear correspondence between business object relationships and rule constraints, avoiding the problem of rule semantic offset, improving the traceability and consistency verification capability of the rule implementation process, and enhancing the consistency and reliability of rule implementation in insurance software.

The application discloses a natural language query retrieval system based on deep learning, comprising: a metadata acquisition module for acquiring metadata information of a target structured database; a field semantic generation module for generating a field-level semantic representation; a semantic card generation module for updating the field-level semantic representation and encapsulating the same as a semantic card; a query semantic generation module for generating a query-level semantic representation; a field selection module for generating a target field set; a constraint construction module for constructing a query constraint graph; a sentence generation and verification module for generating a structured query statement based on the query constraint graph and performing semantic consistency verification; and an execution and clarification module for generating an updated structured query statement, which realizes accurate mapping between a natural language query and structured data and improves the accuracy of field selection and constraint analysis in a complex natural language query scenario.

The invention discloses a satellite task planning method and system based on multi-constraint conditions, and relates to the technical field of reinforcement learning, and the method comprises the steps: receiving task parameters of a plurality of observation tasks, carrying out the multi-constraint analysis of each observation task based on the current satellite orbit data and the task parameters, and obtaining a multi-constraint analysis result; obtaining at least one candidate observation scheme corresponding to each task, wherein the multi-constraint analysis at least comprises orbit visibility analysis and meteorological constraint analysis; based on candidate observation schemes of all observation tasks, a collaborative planning scheme integrating multiple constraint conditions is generated through a collaborative decision model, the collaborative decision model takes maximization of overall task return as a target function, and the target function fuses quality scores of the tasks and inter-task resource conflict penalty terms; and based on the collaborative planning scheme, generating a task instruction sequence for driving the satellite to execute. According to the method, the accuracy and the adaptive capability of satellite task planning in a complex scene are remarkably improved.

The invention discloses an intelligent examination arrangement system and method based on multi-stage hybrid optimization, and belongs to the technical field of computer educational administration management, and the system comprises a data loading module which is used for obtaining to-be-arranged examination data, student course selection data, examination room resource data, invigilation resource data, historical examination arrangement data and an unstructured text; the AI constraint analysis module is used for analyzing the unstructured text by using an AI large model and converting the unstructured text into a machine-readable first constraint; the historical mode mining module is used for analyzing historical examination arrangement data, mining time arrangement characteristics of a specific examination and converting the time arrangement characteristics into second machine-readable constraints; the conflict graph construction module is used for constructing a student conflict graph based on the student course selection data; according to the method, the AI large model and historical data mining are introduced, so that the input process of complex constraints is greatly simplified, and the test arrangement result is more suitable for actual requirements and historical habits.