54 results about "Constraint analysis" patented technology

The invention relates to the technical field of chip design, in particular to a system-on-chip design method and system and a medium. The method comprises the following steps that a chip design target and basic parameters are collected, a constraint condition set is generated through demand constraint analysis, the basic parameters of a system are subjected to parallel recombination, functional prototype data are generated, the functional prototype data are used for chip behavior design, functional unit groups are generated, a network architecture is reconstructed to form a functional module network, and the functional module network is used for chip behavior design. And carrying out clock domain mapping and competition compensation processing based on the network to obtain a function scheduling sequence, carrying out resource matrix remapping on the basic parameters according to the function scheduling sequence, generating hardware abstract data, carrying out chip layout simulation, and finally generating a simulation chip blueprint. According to the system-on-chip design method, the more efficient system-on-chip design method is achieved.

The invention discloses a reservoir scheduling decision support system for multi-objective optimization, and relates to the technical field of hydraulic engineering scheduling. According to the system, comprehensive optimization of flood control, power generation and ecological targets is realized through cooperative work of the multi-source dynamic constraint analysis module, the collaborative optimization module, the scheduling module and the correction module. A dynamic constraint boundary table is generated by collecting meteorological, hydrological and ecological data in real time. And matching a multi-target coordination rule through a physical constraint double-depth network algorithm, generating a Pareto feasible solution set, and mapping the Pareto feasible solution set into a coordination strategy of flood discharge flow and power generation output. In combination with a hydraulic model, flood control and power generation weights are adjusted, an executable dispatching instruction is generated, it is ensured that the ecological flow reaches the standard, and the influence of a downstream submerged area is optimized. And correcting the dynamic constraint boundary table according to actual monitoring data, and continuously optimizing a scheduling decision. Accurate management and multi-objective optimization of reservoir dispatching are realized, and the utilization efficiency of water resources is improved.

The invention discloses an intelligent course arrangement method based on multi-objective optimization and deep learning, and relates to the technical field of intelligent course arrangement, and the method comprises the steps: collecting multi-source data, and constructing a spatial-temporal feature matrix; mining a historical class schedule conflict mode by using a knowledge graph and constructing an association relationship model; newly-added course constraints are converted into vectors, and a comprehensive constraint model is formed through BERT model analysis. A graph neural network is utilized to capture association of courses, teachers, classrooms and time periods, a comprehensive constraint model is combined to generate candidate schemes meeting hard constraints such as teacher time conflicts and classroom capacity and partial soft constraints such as pre-repair relations and student course selection conflicts, and an optimal course arrangement scheme is obtained through optimization. The problems that an existing system is insufficient in multi-source data application, difficult in association construction, inaccurate in constraint analysis, difficult in complex contact capture, insufficient in multi-target consideration and the like are solved.

The invention relates to the technical field of energy storage scheduling, and discloses a distributed energy storage power system optimization scheduling method and system. The method comprises the following steps: collecting system operation data, performing normalization processing and anomaly detection, and predicting 24-hour load demand and new energy output in combination with standardized data; performing cost calculation and constraint analysis according to the prediction data, and generating a day-ahead scheduling plan; the operation state is updated according to the 15-minute cycle, and the output data is adjusted; performing multi-time-scale hierarchical processing on the power fluctuation; based on this, the energy storage capacity is optimally configured and the life characteristic is evaluated; and parameter optimization and updating are realized through operation index monitoring and analysis. According to the invention, through multi-dimensional performance evaluation and parameter optimization, efficient utilization and service life prolonging of the energy storage system are realized.

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 discloses a territorial space planning stock land management and control early warning system based on multi-dimensional analysis, and relates to the related field of territorial space management, and the system comprises a data association module which is used for collecting territorial space planning constraint data and regional multi-dimensional land data of a target region, and carrying out the spatial association superposition, and forming a stock land database; the partition development module is used for carrying out stock unit identification and regional aggregation analysis to obtain a partition development stock land; the multi-dimensional constraint analysis module is used for constructing a multi-dimensional constraint system, performing multi-dimensional constraint analysis on the partition development stock land, and determining a partition stock land volume threshold value; and the land use management and control early warning module is used for monitoring and obtaining stock land use development planning volume data according to a partition stock land use volume threshold value, and performing land use management and control early warning based on a grading early warning mechanism. The technical problem that existing stock land management and control early warning is insufficient in accuracy and timeliness is solved, and the technical effect of improving the stock land management and control early warning accuracy and timeliness is achieved.

The invention discloses an industrial robot collaborative optimization system, which relates to the technical field of humanoid robots and comprises a joint state acquisition module for acquiring joint torque, angular velocity, acceleration and external disturbance data in real time; the joint first constraint analysis module is used for decomposing a motion task into a high-level task and bottom-level joint control, introducing joint limiting and torque saturation constraint, solving joint optimal torque distribution through QP quadratic programming, and obtaining a first joint torque distribution result; the second joint constraint analysis module is used for designing a multi-objective optimization function based on the first joint torque distribution result and a humanoid robot joint real-time state database, carrying out weight self-adaption according to the humanoid robot joint real-time state database, synthesizing the weight of the multi-objective optimization function according to a dynamic weight, and obtaining a second joint torque distribution result; and solving an optimal joint angle, speed and moment sequence to obtain a joint moment optimal distribution result. Joint abrasion can be effectively reduced, and meanwhile task adaptability and energy efficiency optimization are enhanced.

The invention provides a multi-model score fusion method based on dynamic programming, and relates to the technical field of application of a computer system in financial credit risk control. The method comprises the following steps: in a model correction stage, carrying out standardization processing on scores of each scoring model; in the constraint condition analysis stage, an optimal score mapping value is obtained by constructing and solving a target optimization function; in a dynamic planning optimization stage, constructing an optimal division strategy of scores; in the grading and dividing rule screening stage, an optimal grading and dividing rule is selected by adopting a stability screening method based on matrix logarithm transformation; making a score fusion scheme based on the optimal score division rule; in the index evaluation stage, the stability of the score fusion scheme is evaluated based on the optimal score mapping value. The problems that an existing score fusion method usually adopts a pairwise cross fusion mode to carry out score calculation, score constraint analysis depends on a fixed rule, score division is usually based on an empirical rule and the like are solved.

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 method for analyzing the testability of a marine launched rocket by light thunder equipment, which comprehensively considers factors such as radar performance indexes, tracking target characteristics and the like through radar tracking capability calculation, tracking angle calculation, visibility constraint analysis, testability constraint analysis and testability time determination. And a quantitative analysis result is provided for the testability of the sea-based light thunder equipment of the rocket launched on the sea, and the accuracy of a calculation result is high. The application of the method provided by the invention can assist the analysis of measurement and control support conditions during maritime emission, especially the development of maritime optical and radar equipment station distribution design and the verification of the rationality of a maritime station distribution method, and also facilitates the optimization of a scheme which meets the key measurement and control requirements and has a better overall measurement effect through the testability analysis of different station distribution schemes.

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 relates to the technical field of structure assembly, and discloses an ancient building digital building block education system, method and device, and the system comprises an operation interaction module which is used for receiving operation instructions of a user about the selection, assembly sequence and node connection mode of ancient building components, and converting the operation instructions into assembly action data recognized by the system; the modeling and constraint analysis module is used for constructing a three-dimensional configuration model of the components in a high-dimensional configuration space according to the received splicing action data, extracting tenon-and-mortise node information between the components, and establishing node constraint conditions for a contact position relationship and a rotation direction relationship between nodes; and the stability verification module is used for constructing a mechanical verification model based on the three-dimensional configuration model and node constraint conditions. Through mechanical verification modeling and a visual feedback mechanism, accurate stability evaluation and real-time operation guidance in the splicing process are realized, and teaching interactivity and splicing precision are improved.

The invention discloses an article grabbing and positioning method, system and device and a medium, and relates to the technical field of visual recognition. The method comprises the steps that a two-dimensional image, collected through a monocular camera, of an object to be grabbed is acquired; the types and the number of the to-be-grabbed articles in the two-dimensional image are recognized, and standard physical sizes corresponding to the types are determined; if the object is a single object, the object serves as a target to-be-grabbed object, the top face corner pixel coordinates of the object are detected, and the three-dimensional pose is solved through the PnP algorithm by combining the physical size and the internal reference of the monocular camera; if the number of the objects is multiple, all candidate corner points are detected, quadrilateral corner point sets of all the objects are screened out through clustering and geometric constraint analysis, then candidate three-dimensional poses of all candidate objects to be grabbed are calculated through a PnP algorithm, and then a target object to be grabbed is selected according to the candidate three-dimensional poses; and finally outputting the three-dimensional pose information of the target to-be-grabbed object. According to the invention, accurate positioning of a single article and three-dimensional pose recognition in a multi-article stacking scene are realized only by means of the monocular camera.

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 discloses a hydrogen-lithium hybrid aircraft weight estimation method, and relates to the field of overall design of hydrogen energy aircrafts and hybrid aircrafts, and the method comprises the following steps: carrying out constraint analysis according to aircraft task requirements and existing database information, and obtaining a power-to-weight ratio and a wing load; performing performance analysis on the basis of the power-to-weight ratio, the wing load, the aircraft weight initial value and the flight mission profile to obtain an aircraft power demand and an energy demand; calculating the weight of each part of the airplane based on the airplane power demand, the energy demand, the power mixing degree and the energy mixing degree; and obtaining the total weight of the aircraft based on the weight of each part of the aircraft, judging whether the total weight of the aircraft converges or not, if so, outputting the total weight of the aircraft, and completing the weight estimation of the hydrogen-lithium hybrid aircraft. The method solves the problem that an existing method lacks aircraft weight research on different proportions under the condition that energy is supplied to the lithium hydrogen battery at the same time.

The invention discloses a multi-agent system adaptive target tracking formation control method based on reinforcement learning, and relates to the field of multi-agent system target tracking formation control, and the method comprises the following steps: 1, designing a scene, and making a decision in real time; 2, constructing a constraint analysis model, and constructing a target function and a constraint condition; step 3, designing an intelligent agent strategy; designing an observation space, an action space and a reward function; step 4, experiment design; step 5, intelligent agent training; a two-stage training process is adopted; 6, designing a self-adaptive target tracking formation control framework; an ATTFCF framework is provided. According to the method, target tracking and formation control problems are fused, the MPR-DDPG algorithm is adopted for solving, the problems that multi-agent cooperative target tracking and formation control is low in precision and poor in strategy safety are solved, and the method can be suitable for cooperative target tracking tasks of different numbers of agents.

The application relates to the technical field of intelligent vehicle management, and discloses a vehicle state monitoring method and system based on deep learning, wherein the vehicle state monitoring method based on deep learning comprises the following steps: based on structural mechanics, constraint analysis, vibration anomaly detection, loose positioning and fatigue evaluation monitoring task physical dependency relationship, a task dependency directed graph is generated; a multi-modal monitoring data matrix is input into a shared encoder of a multi-task learning network to generate a unified representation vector; the unified representation vector is respectively input into a vibration anomaly detection decoder, a loose positioning decoder and a fatigue evaluation decoder to output initial prediction results of each task; a conditional probability model between task outputs is established based on a constraint relationship defined by the task dependency directed graph, and a consistent diagnosis result meeting physical constraints is generated through maximum posterior estimation. The application solves the technical problems of one-sided diagnosis results, mutual contradictions and low calculation efficiency.

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.