50 results about "Energy dynamics" patented technology

The invention relates to the technical field of network public opinion monitoring, and discloses an emotion energy dynamic change-based public opinion inversion recognition method, which comprises the following steps of: constructing a weighted emotion potential energy field, and calculating effective emotion potential energy of nodes; and then calculating semantic field topological stress representing the viewpoint structure tearing degree and emotional potential energy field system entropy representing the energy distribution disorder degree, on this basis, constructing an entropy-stress two-phase state space and a dynamic hysteresis loop model, quantifying the social damping characteristics of the system, monitoring the system state in real time, and determining the state of the system. When the two-phase space yield judgment condition and the damping failure judgment condition are met at the same time, a public opinion inversion early warning signal and a risk probability index are output, the critical characteristics of public opinion system instability can be accurately recognized from the dimensions of structural mechanics and dynamics, and the false alarm rate of inversion early warning is effectively reduced.

The invention relates to the technical field of artificial intelligence and energy system operation control, and discloses an artificial intelligence-based energy dynamic aid decision-making method and system, and the method comprises the steps: collecting multi-source heterogeneous data of an energy system, and constructing an observation vector; decoupling the observation vector into an environmental causal factor and an environmental noise factor of a potential space by using a variational structured causal characterization model; pruning a causal factor based on an accumulated anti-fact regret value gradient to obtain effective decision information, generating an anti-fact trajectory by applying intervention to a noise factor to construct an expanded belief state set, and updating a control strategy by using a continuous anti-fact regret minimization algorithm on this basis; and finally, performing physical constraint verification on the sampling action and executing the physical constraint verification. Through causal decoupling and an anti-factual reasoning mechanism, decision robustness, interpretability and physical safety of an energy system in a dynamic non-stationary environment are effectively improved.

The present application relates to a kind of complex water level geological conditions under booster station jacket foundation construction method, belong to marine wind power engineering technical field.The method solves in deep water, torrent, hard seabed environment, booster station jacket foundation construction is prone to structural tilt due to environmental load, and the problem that stability is insufficient and construction precision is difficult to control.The method includes: stress analysis to jacket, calculate total overturning moment and carry out stability judgment;Submarine pre-investigation and leveling;Using GPS and cable wind rope cooperation, complete accurate installation and pre-leveling of jacket;According to specific order, steel pipe pile is inserted and sunk, and pile body stress real-time monitoring and energy dynamic control are implemented during sinking process;Jacket leveling is fixed, annular space grouting and pile top cutting are carried out.The method is mainly used for the safe, efficient construction of offshore wind farm booster station jacket foundation under complex hydrogeological conditions.

The invention discloses an energy supply side optimization management system and an implementation method, and relates to the technical field of energy management. The method comprises the following steps: acquiring source end data of an energy supply side, analyzing the source end data through a multi-level protocol adaptation engine and a dynamic rule matching engine, and extracting various energy features and energy supply side GIS (Geographic Information System) information; constructing a multi-energy flow topology constraint matrix based on energy features, performing fine-grained geographic grid division based on energy supply side GIS information, and determining grid-level energy dynamic parameters; performing dynamic trend adjustment on the grid-level energy dynamic parameters in combination with the multi-energy-flow topology constraint matrix, and determining a grid-level energy dynamic prediction map; searching an optimal tradeoff solution set through a population aggregation and dispersion search algorithm; and carrying out constraint solution on the optimal tradeoff solution set in combination with the safe operation boundary, and determining an energy scheduling optimal solution. According to the invention, the accuracy and foresight of energy management are improved, the loss link is accurately traced, and the system is driven to continuously evolve towards the low-carbon and high-efficiency direction.

The present application belongs to the technical field of data processing, and particularly relates to a method and system for dynamic monitoring of energy carbon based on multi-source data fusion, which comprises the following steps: collecting high-frequency energy data and low-frequency emission data of a target area, performing frequency-raising processing on the low-frequency emission data by using an interpolation algorithm, and performing standardization processing on the two types of data to obtain energy sequences and emission sequences with unified time resolution; calculating the causal fluctuation confidence of the energy sequences at each time, and constructing a cumulative distance matrix in combination with the causal fluctuation confidence; searching for an optimal alignment path of the energy sequences and the emission sequences by using a dynamic time warping algorithm, and calculating a local slope index at each path point; dynamically adjusting the observation noise covariance of Kalman filtering according to the local slope index, performing smoothing correction on the aligned emission data, and outputting the energy carbon monitoring result. The present application solves the problem of distortion in calculation of carbon emission intensity caused by phase misplacement of multi-source data.

The invention relates to a hydrogen energy dynamic scheduling method and system considering source load uncertainty in an industrial park, and belongs to the technical field of energy scheduling, and the method comprises the steps: obtaining wind-solar power generation and hydrogen load historical data, and constructing a data set; obtaining an intrinsic mode function component through variational mode decomposition; constructing a modal feature matrix based on the components, and performing K-medoids clustering by adopting a dynamic time warping distance to generate a typical scene and probability; establishing a day-ahead scheduling optimization model, and making a hydrogen production and energy storage plan by taking total cost minimization as a target; and in actual operation, a real-time scheduling instruction is generated by minimizing the plan deviation and the equipment variation through the intra-day rolling optimization model. Through multi-scale optimization and scene generation, the scheduling precision, economical efficiency and real-time adaptability are improved, and the method is suitable for an electric-hydrogen coupling system in an industrial park.

This invention discloses an energy regulation method and system for virtual power plants based on a predictive model, relating to the field of virtual power plant operation control. The method includes the following steps: collecting energy data from the virtual power plant; integrating wind power data and load data, and extracting features from the integrated data; constructing a preliminary energy dynamic balance prediction model based on predicted generation data of distributed energy sources and predicted demand data of load-side resources; using the preliminary energy dynamic balance prediction model to predict day-ahead load demand and distributed energy; adjusting the operating status of each distributed energy source and load-side resource in real time using the niche gray wolf optimization algorithm according to the preliminary dispatch strategy; dynamically adjusting and optimizing the energy dynamic balance prediction model; and re-predicting load demand and distributed energy using the optimized energy dynamic balance prediction model. This invention can respond to changes in the power system in advance, achieving more efficient energy dispatch.

The invention belongs to the technical field of energy regulation and control, and provides an energy storage regulation and control decision method and system based on energy dynamic balance, and the method comprises the steps: constructing an energy storage equipment feature set, carrying out the numerical conversion of state features in the energy storage equipment feature set, and obtaining key energy storage features; the method comprises the following steps: constructing an equivalent state-of-charge model, an intra-day time-phased capacity measurement and calculation model and a system power model of an energy storage system, setting balance conditions of the energy storage system, and carrying out quantitative solution on key energy storage characteristics to obtain quantitative data of energy storage nodes; according to the quantized data and an energy balance grading rule, an alternative regulation and control scheme is determined through a multi-energy storage system regulation and control model; and setting regulation and control constraint conditions, constructing a regulation and control cost model of the energy storage system participating in the power grid, and screening the alternative regulation and control schemes to obtain an energy storage regulation and control decision scheme. According to the scheme, the regulation and control precision is effectively improved, the energy dynamic balance of the energy storage node network is realized, and the regulation and control effect is better.

The invention provides a multi-objective optimal compromise robust optimization scheduling method for an integrated energy system, and the method comprises the steps: constructing an electric-thermal-hydrogen integrated energy system frame, sequentially constructing a hydrogen energy dynamic utilization model, a green hydrogen certificate transaction method and a chance constraint distribution robustness model, and carrying out the calculation of the optimal compromise robust optimization scheduling of the integrated energy system based on the ordering of approximate ideal solutions and a multi-objective decision theory. Establishing a multi-target optimal compromise distributed robust model of the integrated energy system; and finally, solving the multi-target optimal compromise distributed robust model of the integrated energy system by adopting a non-dominated sorting genetic algorithm based on local search. After the hydrogen energy dynamic utilization link is introduced, the operation characteristics of hydrogen energy can be effectively reflected, and the wind power absorption level and the energy utilization efficiency can be effectively improved; the multi-target optimal compromise robust low-carbon optimization not only considers the low carbon, economy and robustness of the system, but also significantly improves the solving efficiency and optimization result of the system.

The invention provides a network construction type equipment electromagnetic dynamic modeling method and system considering a network energy storage state, and belongs to the technical field of power system dynamic modeling. The method comprises the following steps: establishing a circuit link electromagnetic scale dynamic model which simultaneously considers instantaneous power transmission and instantaneous energy dynamic states of inductance and capacitance elements according to a grid-connected system topology of a network construction converter; establishing a control link electromagnetic scale dynamic model of the network construction converter; the circuit link electromagnetic scale dynamic model and the control link electromagnetic scale dynamic model are combined, and a network construction converter electromagnetic scale dynamic nonlinear model representing system network energy dynamics is constructed; and carrying out linearization processing on the nonlinear model at a steady-state working point to obtain the electromagnetic scale dynamic small signal model of the network construction converter. According to the method, the coupling effect of network energy dynamic and converter control under the electromagnetic time scale is depicted, and the defects of a traditional steady-state model in broadband oscillation analysis are overcome.

The application discloses a rape whole growth cycle monitoring method and system based on three-dimensional point cloud reconstruction, relates to the field of intelligent agriculture and computer vision technology, collects three-dimensional point cloud data at multiple key growth periods of rape, constructs a semantic growth graph after pretreatment; the graph is input into a pre-trained time sequence model, the energy flow among source nodes, library nodes and channel nodes is simulated based on the energy dynamic balance principle, the future growth state of the plant is deduced, and growth monitoring data is finally output. The application realizes nondestructive monitoring of rape from the bud stage to the silique stage through three-dimensional point cloud technology, and overcomes the problems of low efficiency and strong destructiveness of traditional methods. Through the construction of the semantic growth graph, the organ recognition and segmentation problem is solved. Further, a calculable model is constructed by combining the source-library theory, photosynthetic product flow and organ competition are simulated, the biomass is accurately predicted before the silique forms, and the phenomenon of abortion is recognized, so that the leap from morphological monitoring to growth mechanism deduction is realized.

The application discloses an economic-environment and energy dynamic pricing double-layer game micro energy network capacity optimization planning method, which comprises the following steps: framework design of the economic-environment and energy dynamic pricing double-layer game micro energy network capacity optimization planning method, establishment of an upper-layer environment-economy evolution game planning model, establishment of a lower-layer energy dynamic pricing strategy master-slave game model and establishment of a multi-energy transformation coupling hub model. The method can consider the conflict relationship between economic benefits and environmental benefits in the planning problem, consider the dynamic characteristics of energy prices, balance the conflicting interests between micro energy network operators and users, improve the planning efficiency and economic benefits of the economic-environment and energy dynamic pricing double-layer game micro energy network capacity optimization planning, effectively reduce the total planning cost, balance the peak-valley difference of user load, improve the energy utilization efficiency, reduce the emission of greenhouse gas CO2 and air pollutants NO x , and realize the environmental value. The method has the advantages of scientific rationality, strong applicability and good effect.

The invention discloses an industrial park multi-energy dynamic complementary scheduling method and system based on multi-source data, and belongs to the technical field of energy scheduling. The method comprises the following steps: an acquisition module, a first determination module, a second determination module, a constraint determination module and a scheduling scheme determination module. According to the method, the power change rate is predicted in real time and quantified into the dynamic stability margin, then the dynamic stability margin is used as the rigid constraint to be embedded into the economic dispatching optimization model, the standby capacity is reserved in advance in the dispatching decision-making stage to prevent risks, the balance of economy and safety is converted into economic optimization within the safety boundary, and the economic optimization efficiency is improved. According to the method, the safety requirement is improved from a soft target to a hard constraint, active beforehand defense of an electric energy quality problem is realized, economic optimal scheduling is realized while the system stability is guaranteed, and the problem that voltage sag and frequency fluctuation are difficult to effectively suppress in renewable energy fluctuation processing in the prior art is solved.

The invention relates to the technical field of intelligent control of an energy system, and discloses an AI energy dynamic optimization control system and method based on an intelligent agent. The method comprises the following steps: extracting energy network equipment attributes and topology, and constructing a multi-dimensional data cube; and real-time sensing data and a flexible constraint rule are synchronously injected. And driving the intelligent agent to carry out incremental semantic annotation on the data stream, generating an energy event with a space-time mark, and compiling the energy event into a source load feature portrait. And according to the portrait, activating a matched regulation and control mode sub-graph in the strategy knowledge graph, generating a dynamic control parameter, and loading the dynamic control parameter to an actuator. According to the method, real-time deep semantic understanding of the operation state of the energy system and dynamic self-adaptive accurate regulation and control based on historical experience are realized, and the intelligent level and the operation efficiency of complex energy network control are improved.

The invention discloses a conflict demand-oriented thermal management energy dynamic allocation method and system. The method comprises the following steps of: constructing a thermal dynamic model based on state sensing data; judging whether the total demand energy of all the heat management units is greater than the total supply energy of the vehicle; if the energy is less than or equal to the total supply energy of the vehicle, performing energy distribution according to the demand of each thermal management unit; if the energy is greater than the total supply energy of the vehicle, using a model prediction control method to construct a rolling time domain optimization problem objective function of each thermal management unit, and applying constraint conditions to the rolling time domain optimization problem objective function; and solving the objective function based on a numerical optimization algorithm and the thermodynamic model to obtain an energy distribution scheme sequence of each thermal management unit in the prediction time domain. Based on the data processing flow, the limited total energy budget is dynamically distributed to each thermal management unit in real time, and the overall thermal management efficiency of the system is optimal on the premise that the security hard constraint of the system is met.

The application discloses an energy supply side optimization management system and an implementation method, and relates to the technical field of energy management. The method comprises the following steps: collecting source end data of an energy supply side, analyzing the source end data through a multi-level protocol adaptation engine and a dynamic rule matching engine, and extracting various energy characteristics and GIS information of the energy supply side; constructing a multi-energy flow topology constraint matrix based on the energy characteristics, performing fine-grained geographical grid division based on the GIS information of the energy supply side, and determining grid-level energy dynamic parameters; combining the multi-energy flow topology constraint matrix to perform dynamic trend adjustment on the grid-level energy dynamic parameters, and determining a grid-level energy dynamic prediction atlas; finding a best trade-off solution set through a population aggregation and dispersion search algorithm; combining a safe operation boundary to perform constraint solving on the best trade-off solution set, and determining an optimal solution of energy dispatching. The application improves the accuracy and foresight of energy management, accurately traces loss links, and drives the system to continuously evolve in the direction of low carbon and high efficiency.

The invention relates to the technical field of power system optimization scheduling, and discloses a multi-energy complementary coordinated scheduling method and system for an energy base in a green power market, and the method comprises the steps: building a multi-dimensional dynamic penalty coefficient system based on the linkage of a carbon price and a green certificate price; the reference transaction volume is locked through the medium-and-long-term green electricity contract, and the output deviation is adjusted through the real-time market, so that a collaborative operation mechanism of the green electricity market and the real-time market is formed; a multi-energy complementary coordinated scheduling model is constructed in combination with multi-dimensional correlation factors, and the maximization of the total income of an operation cycle is taken as a target and is incorporated into physical operation constraints of water, electricity and gas electricity; and realizing multi-energy dynamic coordinated scheduling through the cooperation of the multi-dimensional dynamic penalty coefficient system, the collaborative operation mechanism and the multi-energy complementary coordinated scheduling model. According to the invention, load demand complementation and clean energy multi-energy complementation can be efficiently realized, and resource sharing can be flexibly adjusted.

The invention relates to a fuel cell energy dynamic management method and system based on a neural network. The method comprises the following steps: U1, generating an optimal control label of fuel cell energy; u2, constructing and training a model; and U3, based on the trained model, inputting complete line data, and dynamically adjusting and planning the energy of the fuel cell to obtain data information of the output power of the optimized fuel cell. According to the method, the whole process can be planned before departure, continuous correction is carried out on the way according to the actual state, it is ensured that the follow-up strategy at any moment is the optimal solution for the current state, and real dynamic global optimization is achieved.

The application provides a fluctuation coordination distributed intelligent healing power distribution network method and system, the method comprises the following steps: collecting power distribution network operation data and distributed energy dynamic characteristic data in real time; calculating distributed energy real-time output power and line voltage deviation based on a distributed energy dynamic output model; triggering a multi-agent negotiation mechanism if the line voltage deviation exceeds a threshold; each agent selects the optimal coordination strategy through message passing and strategy updating; and performing self-healing control operation. By collecting distributed energy dynamic characteristic data in real time and establishing an accurate dynamic output model, the power output of intermittent power sources can be accurately predicted, and the problem of insufficient consideration of the fluctuation of distributed energy in the prior art is effectively improved.

The invention discloses a wide-speed-range aircraft multi-source energy dynamic configuration method, and belongs to the technical field of calculation, reckoning or counting. Aiming at the characteristics of strong nonlinearity, high dynamism and multi-peak distribution in a TBCC mode conversion stage, the method comprises the following steps: firstly, constructing an environment vector e containing a flight condition and an engine state; secondly, establishing a strong coupling nonlinear mechanism model of the TBCC engine and the multi-source electric energy generation system; further constructing a multi-target optimization model taking the thrust loss delta FN and the unit thrust oil consumption increment delta SFC as targets; a model-driven environment perception EA-NSGA-II algorithm is adopted, the environment is monitored jointly through environment vectors and fitness changes, and the Pareto frontier is dynamically tracked through local restart, population memory and environment prediction mechanisms; and finally mapping the solution set into a multi-source power distribution interval. According to the method, the optimal energy configuration scheme meeting the safety constraint can be output in a wide speed range, especially in a dynamic mode conversion working condition.

This invention provides a smart integrated energy dynamic optimization method for virtual power plants, belonging to the field of energy dynamic optimization technology. The method first constructs a standardized spatiotemporal coupled dataset through multimodal, multi-dimensional data perception and preprocessing. Then, it establishes a precise dynamic coupling model of multiple energy flows (electricity, heat, cooling, and gas) considering dynamic efficiency, time-varying losses, and equipment lifespan degradation. Next, it builds a three-timescale closed-loop optimization framework (day-ahead, intraday, and real-time), constructing a five-dimensional dynamic collaborative optimization objective function that integrates operating costs, carbon emissions, grid safety margin, user energy comfort, and equipment lifespan degradation. A hybrid mechanism combining improved federated reinforcement learning and adaptive scene trees is used to handle energy system uncertainties. The model is solved quickly using a hierarchical distributed alternating direction multiplier method, and finally, optimization commands are issued and closed-loop feedback corrections are completed.

The invention relates to the technical field of heat pump systems, and discloses a recoverable energy dynamic scheduling method and device for a heat pump system, and the method comprises the steps: obtaining heat energy use parameters of a user in each heat use scene; target operation parameters of the heat pump system are determined, and recoverable energy parameters corresponding to the heat pump system are analyzed according to the heat energy use parameters in all the heat use scenes and the target operation parameters of the heat pump system; and according to the recoverable energy parameters corresponding to the heat pump system, scheduling operation is conducted on recoverable energy corresponding to the heat pump system. It can be seen that the recoverable energy parameters corresponding to the heat pump system can be analyzed intelligently, the recoverable energy corresponding to the heat pump system can be scheduled, in this way, accurate matching between the recoverable energy and the heat using scene is improved, the utilization rate of the recoverable energy can be improved, the heat energy waste situation can be reduced, and the energy utilization rate of the heat pump system can be increased. And therefore, the energy scheduling suitability and the energy-saving potential of the heat pump system can be improved.

The invention belongs to the technical field of carbon dioxide flooding recovery efficiency improvement in oil and gas field development engineering, and particularly relates to a method for expanding the swept volume of carbon dioxide flooding through nitrogen foam flooding. Injection allocation is dynamically optimized based on formation energy, all gas injection wells run, the injection speed is designed according to a development scheme, carbon dioxide is continuously injected, and the flowing bottomhole pressure of a gas-seeing oil well is adjusted to saturation pressure; a nitrogen foam slug injection scheme is designed based on oil reservoir heterogeneity, and a high-permeability channel is plugged by utilizing the advantages that foam blocks large but not small, defoams when encountering oil and is stable when encountering water and the like; the injection time sequence is dynamically monitored and optimized through the carbon dioxide displacement front; through well pattern function reconstruction and crack-matrix synergistic displacement, carbon dioxide gas channeling is inhibited, and the swept volume is enlarged; finally, a gas-oil ratio-foam stability combined feedback mechanism is established, and dynamic plugging is achieved.

The present application relates to the technical field of mining engineering and rock mechanics, and discloses a method for analyzing the failure and earthquake-inducing mechanism of a thick interbedded sandstone roof, which comprises the following steps: first, obtaining real-time microseismic data, and quantitatively constructing a four-dimensional space-time damage field representing the internal damage evolution of the rock mass according to the real-time microseismic data; then, establishing an interlayer time-lag sliding model driven by the damage field, so as to realize the dynamic coupling of the damage state and the mechanical parameters; further, performing dynamic simulation by using the model, identifying potential failure and outputting graded early warning by using a multi-stage early warning mechanism based on a rate index and an energy dynamic instability criterion; finally, introducing a closed-loop feedback correction mechanism, and using the deviation between the actual seismic source inversion parameters and the prediction results to iteratively correct the model by using an optimization algorithm. The present application closely combines damage evolution and mechanical behavior, has self-adaptive correction capability, can reveal the earthquake-inducing physical mechanism, and improves the accuracy and reliability of analysis and prediction.

The application discloses an energy equalization method for an electrically driven vehicle line control chassis multi-energy braking system, comprising the following steps: step one: first, obtaining the through vehicle speed, braking demand and residual power data of different vehicles at different times under the same road environment by using a vehicle-road cloud platform, and fusing historical data and real-time vehicle data; step two: based on the demand braking energy prediction value obtained in step one, designing a braking energy dynamic equalization algorithm, and real-time calculating the energy recovery ratio of an EMB system and an electric drive system by using an optimization algorithm; and step three: on the basis of step two, integrating a sensor network and cloud data analysis, real-time collecting and processing the energy recovery data in the braking process, and combining the real-time data with historical data. The application realizes accurate prediction of the energy demand of a vehicle under different braking conditions, and improves the foresight and adaptability of a prediction model through a self-updating energy demand prediction method.

The invention discloses a post-fire slope optimization method, which belongs to the field of slope rockfall blocking, and comprises the following steps: S1, constructing a multi-source data set; s2, determining a rockfall propagation range; s3, constructing a disaster-exposure-fragility three-dimensional risk assessment model, and outputting a slope risk grade map and a risk score; s4, based on a risk assessment result, a fused interaction matrix and a weight calculation method, introducing a risk-barrier energy dynamic adaptation model, constructing a site selection adaptability-protection energy-structure parameter dynamic optimization system, and outputting a flexible barrier site selection scheme and barrier parameters; and S5, based on a real-time monitoring result, evaluating a flexible barrier site selection scheme and barrier parameters, and dynamically optimizing the step S4 according to an evaluation result. By adopting the post-fire slope optimization method, through integration of multi-source monitoring data and a dynamic simulation technology, the limitation that a traditional method is insufficient in adaptation to special working conditions after the fire is broken through, and the dual goals of safety protection and ecological restoration are achieved.

The application discloses a water, wind and light combined intelligent control system based on multi-energy coordinated control, belongs to the technical field of multi-energy coordinated control, and first collects environment and operation parameters of wind power, photovoltaic and hydroelectric equipment, pre-processes to form a structured data set with time and space tags and dynamic sampling identification; then, time and space correlation analysis is carried out on the data set, a wind power fluctuation and hydroelectric regulation margin real-time response matrix is constructed, a photovoltaic output attenuation and hydroelectric storage compensation time sequence correlation model is constructed, and multi-energy dynamic complementary degree is quantified through a two-dimensional coupling algorithm and hierarchical data reliability is classified; subsequently, a water, wind and light combined power supply capacity prediction model containing an energy characteristic complementary compensation coefficient is constructed in combination with a historical meteorological scene correlation rule base, a hierarchical regulation and control strategy with time and space priorities is generated, and finally, the regulation and control strategy is executed to complete parameter adjustment, continuous monitoring is carried out through closed-loop feedback, multi-energy coordinated complementary regulation is triggered when single energy regulation capacity is saturated, and efficient water, wind and light coordinated control is realized.

The application discloses a kind of reduced-order model driven distributed parameter system reaction physical field control method and system, comprising: the process mechanism of reaction physical field in industrial process is sparsely identified, and process mechanism is embedded neural network, to construct the time-space reduced-order prediction model of reaction physical field;Then energy response metric quantifies the dynamic response of spatial point of distributed parameter system, from which several key spatial points are screened with significant energy dynamic response;Again, the optimal reaction physical field set value of key spatial point is used as control target, the reaction physical field of key spatial point is predicted through time-space reduced-order prediction model, and the input of control system is optimized rolling, to realize the regulation of reaction physical field.The application can significantly improve the control precision of distributed parameter system reaction physical field and process index.

This invention discloses a cognitive decision-making method and system based on multidimensional situation coding, belonging to the field of artificial intelligence and cognitive computing technology. The method includes: acquiring input data and converting it into an event sequence, thereby generating a six-dimensional external polar field; driving a six-dimensional energy vector to converge from an initial neutral state to a stable state under the action of an energy evolution equation; binarizing the converged energy vector and matching it with a situation coding library to determine the current situation type; generating a structured description and action suggestions for the current situation; selecting a target situation type and planning an evolution path based on situation value weights; and outputting a cognitive decision report. This invention uses events as the native cognitive unit, which can be characters, words, phrases, sentences, or any combination thereof. Through a six-dimensional complete situation space and an energy dynamics emergence mechanism, it provides cognitive systems with endogenous interpretability, a unified situation coding framework, autonomous planning capabilities based on intrinsic value, and online self-evolution capabilities. It can be widely applied to intelligent decision support, complex system situation assessment, and other scenarios.

A tunnel dual-energy clustering routing method based on multi-objective optimization, comprising: constructing a three-dimensional space model of a single-tube tunnel for representing the geometric structure of the tunnel; proposing a multi-objective clustering routing algorithm (TCR) and a dual-energy dynamic supply algorithm (DETCR); on the basis of a competition of tribes and cooperation of members algorithm (CTCM), proposing a multi-objective competition of tribes and cooperation of members algorithm (MOCTCM), comprehensively considering the residual energy, communication distance, energy consumption and relay node (RN) election frequency of nodes, and providing a more adaptive and globally optimized decision basis for the selection of optimal cluster heads (Optima-CH) and optimal relay nodes (Optima-RN). The application effectively solves the problems of uneven node energy consumption and limited energy supply in a dynamic and energy-limited tunnel environment, significantly prolongs the network life cycle and improves the energy efficiency level.