1636 results about "Controllability" patented technology

The invention relates to a cross-domain intelligent reasoning method based on knowledge graph enhancement, and the method achieves the precise reasoning in a complex scene through the construction of a hierarchical knowledge expression framework and a dynamic optimization mechanism. A multi-source heterogeneous data fusion technology is adopted, subject fine-grained knowledge units are generated through multi-modal feature extraction, and a three-dimensional knowledge graph structure comprising a core common concept layer, a subject feature ontology layer and a dynamic semantic mapping layer is established; based on a path exploration algorithm driven by reinforcement learning, cross-domain implicit association is mined while subject independence is reserved, and controllability and interpretability of the reasoning process are achieved in combination with an attention fusion mechanism of a large language model. According to the method, the limitation of traditional unified ontology modeling is broken through, the problems of concept drift and path deviation existing in reasoning in the cross fields of medicine-finance, engineering-law and the like are effectively solved, and the accuracy and knowledge traceability of complex decision tasks are remarkably improved.

The invention relates to a coal mine safety risk intelligent management and control method, device and equipment and a medium. The method comprises the following steps: constructing a multi-source heterogeneous coal mine safety data system according to received data of a target coal mine park; the multi-source heterogeneous coal mine safety data system comprises static structure data, dynamic environment data, personnel behavior data and management data; constructing a coal mine three-dimensional space model according to the static structure data and the dynamic environment data; risk indexes in the dynamic environment data are extracted based on multi-algorithm fusion for evaluation, and a risk level is obtained; and if the risk level reaches a preset threshold value, triggering a corresponding linkage response mechanism, and forming a visual result in the coal mine three-dimensional space model. By the adoption of the method, closed-loop logic from sensing, evaluation to linkage treatment can be achieved, and the real-time performance, predictability and controllability of coal mine safety management are effectively improved through algorithm support of each stage and fine design of implementation details.

The invention relates to the technical field of vehicle scheduling, in particular to a multi-terminal vehicle scheduling system based on reinforcement learning. The system comprises a heterogeneous data fusion module, a resource allocation module, a hierarchical reinforcement learning module, an optimization feedback module and a man-machine cooperative control module. Data of vehicle operation, operation tasks, environment monitoring and the like are collected and uniformly packaged into a structured data set, an upper-layer manager model generates a global scheduling instruction set based on a PPO algorithm, and a lower-layer worker model outputs a specific vehicle control instruction based on multi-agent reinforcement learning. The system also evaluates and optimizes a historical scheduling execution effect through an NSGA-II algorithm, selects a Pareto optimal solution set, and realizes continuous iteration of a scheduling strategy. The man-machine cooperative control module supports visual display and manual intervention operation, and improves the adaptability and controllability of the system in a complex operation scene.

The invention relates to the technical field of collaborative optimization, in particular to a virtual power plant power generation-utilization-price collaborative optimization system based on an AI large model, and the system comprises a load confidence matching module, a resource stability mapping module, a source-load capacity coupling module, an electricity price interval adjustment module and a comprehensive regulation and control linkage module. According to the method, the confidence interval prediction of the load demand is realized based on the hybrid neural network modeling of the load behavior data and the equipment temperature control characteristic sequence, and the scheduling matching confidence is measured according to the boundary overlapping condition of the prediction interval and the power generation response characteristic; a stability screening mechanism for adjusting resources is constructed in combination with the output fluctuation ratio and the equipment inertia characteristic, the controllability of load adjustment and the real-time performance of source side response are improved, the price adjustment rhythm is corrected through an electricity price response delay factor, dynamic closed-loop linkage between load adjustment and price guidance is achieved, and the load adjustment efficiency is improved. The execution priority is dynamically updated under the condition that multiple response conditions are matched, and the certainty of resource scheduling and the sensitivity of response are improved.

The invention discloses a vehicle-mounted cross-protocol data time sequence cooperative processing method, which comprises the following steps: establishing a unified time reference, and providing an accurate space-time coordinate system for heterogeneous network nodes by electing a global master clock and constructing a clock relation model and a periodic correction mechanism; systematic cognition and management of communication resources are realized, and a verified protocol capability table is generated by automatically identifying protocol types and variants; constructing an automatic mapping system from demand to execution, performing formalized packaging on a target scene by introducing a unified intermediate model, generating a cross-protocol mapping rule based on the model and a protocol capability table, and then forming a global scheduling table in combination with a unified time reference; the cooperative communication of all nodes is finally realized by using the global scheduling table, and the end-to-end delay is constrained within the preset range, so that the problem that the end-to-end time sequence in the vehicle-mounted heterogeneous network is uncontrollable is solved, and the controllability and reliability of the time sequence of the vehicle-mounted network are improved.

The invention discloses an improved hybrid A star path planning method for an automatic driving vehicle, and relates to the technical field of automatic driving, laser radar sensing and unmanned vehicle navigation. According to the method, on the basis of inheriting the advantages of high global search efficiency, strong kinematics feasibility and the like of a hybrid A star algorithm, a multi-factor cost function is designed, a trajectory smooth constraint is introduced, and the feasibility and execution quality of a path are improved from two aspects of cost evaluation and path construction. In order to further enhance the smoothness and the dynamic controllability of the local path, the initial path is optimized and adjusted by fusing a time elastic band (TEB) algorithm in the method, so that the continuity and the stability of the path in a complex dynamic environment are improved. The method is suitable for a high-precision path planning task in an automatic driving system, and the comprehensive requirements of a path generation result in the aspects of reliability and environmental adaptability can be well met.

The invention belongs to the field of combination of a large model and a knowledge graph, and particularly relates to a large model and knowledge graph combined reasoning method for false news detection, which breaks through the limitation of a traditional detection method in the aspects of knowledge instantaneity and reasoning controllability through heterogeneous architecture design. Based on multi-dimensional features such as timeliness, propagation mode and information density of news propagation, a double-engine-driven intelligent reasoning system is constructed, on one hand, important entity extraction and semantic understanding are carried out through a large language model, and on the other hand, structured fact verification is provided through a dynamic knowledge graph. And the inherent factual illusion problem of a large language model is effectively solved. Compared with a traditional knowledge iteration mode depending on single model parameter updating, the method supports an incremental updating strategy based on knowledge graph nodes.

The invention discloses a virtual power plant optimization system and method, and the system comprises a virtualization platform module which is used for integrating distributed energy, energy storage equipment, an electric vehicle, a flexible load and other resources, and achieves the virtualization and centralized management of the resources; the dynamic resource aggregation module is used for dynamically matching resource characteristics with power system requirements; the market transaction module is used for optimizing the earnings of the virtual power plant; and the core algorithm module is used for optimizing the load prediction precision. The market transaction module implements two-stage market bidding based on a multi-agent game model, analyzes the uncertainty of quotation of competitors through a multi-scene simulation technology, optimizes a dynamic transaction strategy, and achieves the maximization of spot market benefits and the controllability of risks. Through cooperative work of a virtualization platform module, a dynamic resource aggregation module, a market transaction module, a core algorithm module, a security management module and the like, efficient management of resources and maximization of market benefits are realized.

The invention discloses a cargo full-process tracking method for logistics transportation, and relates to the technical field of logistics transportation tracking, and the method comprises the following steps: collecting cargo state data and transportation route data in real time through a sensor and a camera which are installed on a transportation vehicle and a cargo; multi-type sensors and cameras are deployed on transport vehicles and goods, real-time acquisition of goods state and transport route data is realized, a multi-dimensional prediction model is constructed through a long-short-term memory network and a convolutional neural network, and anomaly identification is performed, so that the real-time prediction of the goods state and the transport route data is realized. According to the invention, accurate detection of goods theft, deviation of a vehicle from a predetermined route, abnormal temperature and humidity, abnormal pressure and the like is realized, automatic alarm and intelligent processing of abnormal conditions are realized through a preset hierarchical response mechanism, the problems of untimely monitoring, poor abnormal identification capability and the like in logistics transportation are effectively solved, and the logistics transportation safety is improved. And the safety, controllability and management efficiency of cargo transportation are obviously improved.

The invention discloses a large-section long-footage mechanical blasting mixed excavation method suitable for a subway station, and relates to the technical field of underground rail transit engineering, and the method comprises the following steps: obtaining a tunnel section contour, a geologic structure and blasting parameters, building a three-dimensional blasting wave propagation inversion model, and combining geology and material heterogeneity to obtain a three-dimensional blasting wave propagation inversion model; and identifying a closed space region forming reflection interference, and predicting a space point location where blasting wave energy is abnormally accumulated. Based on the three-dimensional blasting wave propagation inversion model, the abnormal wave energy accumulation area is predicted in combination with geological heterogeneity, the high-risk blasting section is recognized, parameters are reconstructed, vibration real-time monitoring and model feedback correction are assisted, a closed-loop control system is constructed, local wave crest abnormity is effectively restrained, and the safety and controllability of large-section tunnel blasting construction are improved.

The invention relates to the technical field of integrated circuit manufacturing, in particular to a precise electroplating control system and method for an electroplated layer of a double-layer circuit board, and the method comprises the steps: building a multi-physical field coupling simulation model, and simulating the internal current density and ion concentration distribution of a via hole; constructing a mapping relation between the current density and the side wall electroplating thickness based on a simulation result, and predicting the electroplating thickness; through thickness prediction feedback, a time sequence adjustable pulse current control strategy is designed, the pulse amplitude, the duty ratio and the on-off period are dynamically adjusted, and current distribution optimization is guided; the state of the electroplating process is sensed in combination with real-time monitoring and analysis of multi-source parameters of electrode potential, conductivity and fluid velocity; and based on the prediction model and the real-time parameters, adjusting the pulse waveform and the time sequence through closed-loop compensation. According to the method, the uniformity and controllability of the thickness of the electroplated layer on the side wall of the via hole can be remarkably improved.

The invention relates to the technical field of computers, and discloses an integrated information system for clinical test archives and a data processing method thereof, and the method comprises the steps: constructing a standardized data model covering a whole process; multi-source data dynamic structured intake and integrity verification are carried out; performing multi-dimensional semantic association analysis based on a Bayesian network and a correlation coefficient; block chain type version tracing and difference comparison are carried out; performing role-sensitivity-operation-context four-dimensional access control; and task-driven collaborative sharing and auditing log records. The system comprises a unified modeling module, a data intake module, a semantic analysis module, a version tracing module, an access control module and a collaborative auditing module. The problems of data islands, semantic segmentation, poor dynamic adaptability, weak safety protection and the like in the prior art are solved, intelligent management, semantic interconnection, safety controllability and efficient collaboration of the whole life cycle of clinical test files are achieved, and data quality, audit compliance and multi-role collaboration efficiency are remarkably improved.

The invention discloses a BIM-based digital intelligent construction management method and system, and belongs to the technical field of construction management. A BIM collaborative management mechanism based on the combination of difference comparison, conflict detection, influence evaluation, approval control, version locking and recheck management is constructed; the problems that in the prior art, due to version control defects, multi-party model modification conflicts are not recognized, merging errors exist, construction errors exist and the like are solved, differences are automatically compared before the models are submitted, conflict areas are recognized, the influence of the conflict areas on the construction progress and space coordination is evaluated, merging can be achieved after approval of an approval system, and therefore the construction progress and space coordination accuracy is improved. According to the BIM collaborative construction method and system, a key area is constructed, a re-checking identifier is set for the key area, and meanwhile, related responsible person and operation information are recorded in the whole process to form a traceable data chain, so that the safety, accuracy and construction risk controllability of BIM collaboration are remarkably improved.

The invention provides a multi-scene-oriented intelligent light adaptive control method and system, and the method comprises the steps: recognizing the current behavior state of a user based on visual sensor data, predicting the behavior state of the user, and outputting a light control behavior tag; combining the behavior label with a scene type and ambient light intensity to generate a target illumination intention vector; according to the target illumination intention vector, in combination with an illumination control conflict situation, real-time lamp parameters and user space state data, calculating an illumination weight of each lamp to a user, distributing brightness and color temperature parameters, and in combination with the target dimming process duration, generating a lamp control parameter set; and converting the lamp control parameter set into a dimming instruction sequence, performing time synchronization and delay compensation, and issuing the dimming instruction sequence to each lamp for execution. According to the method, accurate mapping from behavior intention to physical illumination is realized, and the method has extremely high universality, deployability and engineering controllability.

The invention discloses a method for simulating a mold temperature field and curing deformation through finite elements and a mold optimization design method based on the method. The finite element analogue simulation method comprises the following steps: firstly, modeling and simulating a mold grid; secondly, calculating equivalent thermal and mechanical parameters of the composite material by adopting a multi-scale homogenization method; then, a heat conduction model of the mold and the composite material blade is constructed and coupled with the curing dynamic model through temperature, and mold temperature distribution is simulated; and finally, establishing a mold thermal expansion and curing stress coupling model, and predicting thermal deformation and stress change of the mold. By identifying the weak area of the mold in the simulation process, the mold is optimally designed. By improving a mold temperature field distribution simulation method, optimizing a grid modeling process and introducing an accurate calculation method of equivalent material parameters, the precision of mold design and the controllability of a curing process are effectively improved, so that the problem of inaccurate mold temperature field distribution and curing deformation prediction is solved.

The invention relates to the technical field of soil remediation management, in particular to a method and system for soil remediation and digital management, and the method comprises the steps: data construction: fusing multi-source monitoring soil environment parameters with space-time background data such as a GIS layer and a meteorological time sequence to form a data set; state quantification: outputting a comprehensive state index for quantifying restoration urgency and complexity through the soil health assessment model; scheme generation: matching the restoration target library and the resource library, and generating a digital restoration scheme containing an expected state trajectory through an optimization algorithm; closed-loop control is carried out, feedback data comparison tracks are continuously collected, if the deviation exceeds a threshold value, the index is recalculated, the scheme is dynamically adjusted, and the system comprises a data construction module, a state quantification module, a scheme generation module and a closed-loop control module and executes the method. According to the method, the data reliability and evaluation scientificity are improved, the scheme adaptability is guaranteed, dynamic and controllable remediation is realized, and digital and efficient management of soil remediation is promoted.

The invention relates to the field of intelligent automatic driving, in particular to a multi-vehicle cooperative controllable confrontation test method based on a diffusion model. Comprising the following steps: S1, a diffusion model training stage: training a diffusion model based on real driving data, and learning vehicle behavior distribution through forward noise addition and reverse denoising to obtain fixed model parameters; s2, scene and multi-vehicle space-time modeling: fusing the road map, the lane topology and the vehicle state information, and constructing a space-time scene representation of multi-vehicle interaction as input of diffusion generation; s3, a diffusion generation mechanism based on adversarial guidance; s4, performing partial diffusion control and diversified confrontation generation; and S5, evaluating the authenticity and controllability of the multi-agent confrontation scene. Compared with the prior art, the method has the advantage that the authenticity, controllability and closed-loop consistency of multi-vehicle cooperative confrontation scene generation are obviously improved.

The invention provides an intelligent question and answer method based on a preset multi-dimensional knowledge base and a large language model, and the method comprises the steps: receiving a natural language query, converting the natural language query into a query semantic vector, and recognizing a corresponding intention type and a first confidence coefficient; if the first confidence coefficient is higher than an intention threshold value and the highest similarity between the query semantic vector and a standard question semantic vector in a preset multi-dimensional knowledge base is higher than a matching threshold value, returning a standard answer; otherwise, retrieving a generated context based on the query semantic vector, fusing the user query and the generated context, inputting the fused user query and generated context into a large language model to generate a preliminary answer, checking fact consistency of the preliminary answer and a standard answer in the generated context to generate a credibility score, and obtaining a target answer when the credibility score exceeds a credibility threshold; and dynamically optimizing the system based on feedback data of the user to the target answer. The intelligent customer service system with controllability, flexibility and self-optimization capability is realized, and the semantic understanding depth and response accuracy of the system are improved.

The invention discloses an interactive data insight system based on a large language model. The interactive data insight system comprises an intention recognition and classification module, an entity extraction module, an entity and business term alignment module, an SQL generation module and a structured query verification module. The key technical problems that an existing natural language data analysis system is insufficient in semantic understanding, inaccurate in entity alignment, weak in multi-round dialogue support, unstable in SQL generation, lack of safety and the like can be solved, semantic understanding and natural language interaction of complex business data are achieved, the data analysis threshold is greatly reduced, and the data analysis efficiency is improved. And the query efficiency and the result controllability are improved.

The invention discloses a high-pressure jet grouting pile construction optimization control method and system, and belongs to the technical field of pile foundation construction. According to the method, the pile body strength is predicted based on the XGBoost regression model, the HSIO hybrid swarm intelligent optimization algorithm is combined with the artificial bee colony algorithm and the seathead optimization algorithm, the construction parameter combination is dynamically adjusted, and optimal matching of the target strength and the real-time construction state is achieved. The method solves the problems of parameter setting deviation and non-uniform pile forming quality caused by the fact that traditional construction depends on artificial experience, is particularly suitable for high-precision construction requirements under complex geological conditions (such as karst areas and deep and thick sludge layers), remarkably improves the pile body strength stability, construction efficiency and quality controllability, and reduces the construction cost. And meanwhile, material waste and personal error risks are reduced.

The invention relates to the technical field of large model interaction, in particular to a long-term and short-term memory processing system based on a large model and a vector database, and provides a scheme that short-term context memory and long-term stable memory are subjected to hierarchical management and joint retrieval by performing semantic analysis and structured modeling on user interaction contents. Under the condition that historical interaction content exists, a theme segment division and intention stack mechanism is introduced, ordered organization and step-by-step alignment of intention states in multiple rounds of conversations are achieved, and matched memory entries are obtained from a cache database and a long-term database based on query semantic representation. Furthermore, by performing normalization, situational rewriting and conflict suppression on memory entries, controllable reasoning prompt information is constructed and input to a large model to generate target output, so that the consistency, stability and controllability of a generated result are improved in a multi-round interaction and cross-session scene.

The invention discloses a vehicle torque control method and device, a computer readable storage medium and a vehicle, and relates to the field of vehicle control. The scheme comprises the steps that first torque is generated according to a vehicle torque request, and second torque is generated through filtering; generating a base torque gradient according to the vehicle state and the second torque; obtaining a corrected torque gradient according to the dynamic correction coefficient; generating a dynamic zero-crossing region threshold according to the vehicle state and the corrected torque gradient; and performing torque control on the zero-crossing region according to the dynamic zero-crossing region threshold. The threshold is dynamically adjusted based on the vehicle state, and it is ensured that the zero-crossing range meets the actual working condition requirement. The dynamic property and the smoothness are balanced through correction coefficient and gradient control, the dynamic response is guaranteed, meanwhile, precise control over the zero-crossing area is achieved, the problems of vehicle structural collision abnormal sound and the like are solved, meanwhile, the controllability of an accelerator pedal is enhanced, and the driving experience feeling of a user is improved.

The invention relates to the technical field of energy efficiency management of a super data center, and discloses a dynamic energy efficiency optimization and carbon traceability system for a super data center, which comprises the following steps of: acquiring energy consumption, an energy supply chain and environmental data of the super data center through an acquisition module, and performing fusion and time sequence processing to provide basic data for an analysis module; and the analysis module establishes a carbon footprint prediction model based on the time sequence data, calculates layered carbon emission, and constructs a multi-objective optimization model to generate an energy efficiency optimization strategy. And the central control module adjusts the operation of the equipment according to the strategy, compares the adjusted data with the prediction result, judges whether to correct the strategy, and realizes closed-loop optimization. And the display module visually presents the time sequence data and the optimization strategy, so that a closed-loop intelligent management mechanism of data acquisition, analysis and prediction, strategy generation and execution correction is realized, and the energy efficiency management precision and carbon emission controllability of the data center are effectively improved.

The invention discloses a vehicle stability control method, system and device, a medium and a program product, and relates to the technical field of vehicle dynamic control, and the method comprises the steps: calculating a vehicle speed, a pavement friction coefficient, a slip rate, a load transfer rate and a preview path curvature; the vehicle speed, the road friction coefficient, the load transfer rate and the preview path curvature serve as input, the yaw velocity deviation and the slip rate serve as reward signals, and a yaw velocity dynamic threshold value is generated; according to the actual yaw velocity, the expected yaw velocity and the yaw velocity dynamic threshold, obtaining a judgment result that the current vehicle is under-steering or over-steering, and generating a control strategy in combination with a vehicle driving form; and the optimal braking torque adjustment amount is calculated with the minimum yaw velocity deviation and the slip rate as the optimization target, and the front and rear shaft driving torque is obtained according to the deviation between the actual side slip angle and the expected side slip angle. The self-adaptive dynamic environment and the differentiated adaptive driving form are realized, and the safety and the controllability under the complex working condition are improved.

The invention discloses a digital research and development demand automatic disassembling method based on knowledge reasoning. The method comprises the following steps: gathering multi-source demands, uniformly de-duplicating, and establishing a version baseline; a semantic graph is generated according to the standardized requirements, a support graph and a constraint set are constructed, and a resource portrait, historical measurement and a template library are formed; performing retrieval and sub-graph aggregation by using an improved GraphRAG model, completing alignment reasoning and outputting a fact set, a conflict list and a deletion list; the method comprises the following steps: generating a hierarchical task, dependency and priority by using an improved Reflexion algorithm, and performing shadow scheduling and acyclic verification to obtain an execution sequence table; generating a design / interface / test / verification / acceptance workpiece, and solving resources, milestones and key paths according to the design / interface / test / verification / acceptance workpiece; and a tracing link and an early warning mechanism are established, and rearrangement and resource redistribution are carried out when the time exceeds the limit. According to the method, the schedule forming time is shortened, the reworking and delay risks are reduced, and the workpiece coverage rate, the critical path controllability and the delivery traceability are improved.

The invention discloses an online order full-process automatic processing system and method. The system comprises an order state analysis module, a cross-platform collaborative adaptation module, a resource scheduling planning module, an environment load evaluation module and a task scheduling optimization module. According to the invention, by analyzing the trend of the key time sequence change of the order life cycle, the circulation bottleneck can be identified and the time section exceeding the threshold value can be generated, so that the task sequence is matched with the system load, the scheduling coordination and the response precision are enhanced, the execution capability is dynamically compared to form the optimal distribution instruction, and the cooperation efficiency and the transmission continuity are improved. Based on a utilization rate change identification trend, resource distribution is balanced, scheduling flexibility and controllability are enhanced, resource and energy consumption cooperative adjustment is realized through environment load evaluation, operation stability and environment adaptability are improved, high efficiency and continuous optimization of an order flow are realized, and processing capability and operation toughness of a system under high concurrency are enhanced.

The invention discloses a data processing system and method based on a federated architecture interface and related equipment, and relates to the technical field of computer software, in particular to a federated architecture distributed system formed based on a central coordination node and a plurality of regional autonomous nodes. The central coordination node is used for performing global strategy making, metadata block chain evidence storage, conflict detection and federated monitoring and self-healing to realize global management and control, and the regional autonomous node adapts to a physical machine heterogeneous environment to perform local strategy execution, heterogeneous protocol conversion and resource autonomous regulation and control so as to realize flexible regulation of a regional strategy. According to the scheme, through a federated and intelligent treatment system, inherent contradictions between centralized management and control and department autonomy, standardization and rapid iteration and global safety and local flexibility are solved, technical support is provided for an enterprise to construct a standardized, high-reliability and easy-to-expand API ecological system, digital transformation is assisted, and the enterprise safety is improved. And global controllability and local autonomy of enterprise-level API ecology are realized.

The invention discloses a novel pulse reinforcement learning driven unmanned system brain-like intelligent multivariate decision-making method. The method comprises the following steps: respectively establishing a state space, an action space and a reward function of detection identification, material delivery and flight maneuvering tasks; designing a pulse event triggering mechanism according to task characteristics, establishing a pulse frequency adjustment model of various tasks, and constructing a scheduling strategy of time division multiplexing and elastic borrowing; establishing a detection identification model, a material delivery model and a flight maneuvering model; a multi-modal variance index is introduced to dynamically adjust a control signal source by fusing a data strategy and a decision-making mechanism of a prior model; a sliding window state matrix is constructed, task priorities are dynamically calculated, and a unified control instruction is generated in a weighted fusion mode. According to the method, the response capability of the system to the multi-source task is improved, the time sequence independence and the scheduling efficiency of the task are guaranteed, and the stability and the controllability of the unmanned system in a complex environment are enhanced.

The invention discloses a real-time early warning method and system for precise mold production and processing, and relates to the technical field of mold processing, and the method comprises the steps: when a residual pressure disturbance energy aggregation function F1 exceeds a disturbance threshold Q1, the system automatically switches to a trend tracking mode, further calculates and outputs a flow velocity abrupt change rate variation factor F2 and a composite migration pressure difference index F3, and then outputs a flow velocity abrupt change rate variation factor F3; through second-order flow velocity derivative analysis and particle-heat flow-pressure difference coupling behavior modeling, a complex blocking trend caused by particle deposition, heat stagnation accumulation and countercurrent disturbance is effectively identified. And calculating a blockage risk index Irisk by combining the two indexes, and setting a first blockage threshold W1 and a second blockage threshold W2 to realize automatic evaluation and classified response of different blockage levels. Compared with an existing mode of only judging a single parameter of pressure, the method can analyze a blockage evolution path in multiple dimensions and output a grading result, and the risk controllability and the strategy adjustment elasticity are enhanced.

The invention relates to an owned task planning method and system based on search guided by a large language model. The method comprises the steps that in the preprocessing stage, a simulation module based on a large language model is used for generating a precondition function and an effect function corresponding to each action type according to environment meta-information; in a task analysis stage, a translation module based on a large language model is used for translating task description in a natural language form into a structured landing predicate set; in the exploration stage, an exploration module based on a large language model is used for guiding the intelligent agent to explore the environment until all objects related to tasks are observed; in the searching stage, a searching and screening module based on a large language model is used for executing a searching algorithm from the current state, an action space during searching is compressed, a complete task plan is generated, and the task plan is executed by an intelligent agent in the environment. According to the method, the interpretability and controllability of planning can be improved, so that the search process is feasible and efficient in a real task environment.