480 results about "Dynamic modelling" patented technology

The invention relates to the technical field of exploration, and discloses a water depth inversion method and system based on a multispectral remote sensing image, which utilizes an independent verification sample set to carry out hierarchical precision evaluation, and analyzes model performance differences according to dimensions such as a water depth range and a substrate type. Error propagation of links such as atmospheric correction and water level correction is quantified through full-chain uncertainty analysis, an uncertainty quantification model of ensemble learning is constructed, and a pixel-level precision distribution diagram is generated. Therefore, according to the technical scheme, a closed-loop dynamic adaptation framework is formed by systematically integrating multi-temporal data dynamic modeling and an uncertainty quantification mechanism, a spatio-temporal variation compensation mechanism is embedded in the whole process from data acquisition to result verification, the interference of spatial-temporal heterogeneity of water optical characteristics on water depth inversion is effectively dealt with, and the accuracy of water depth inversion is improved. The problems of parameter mismatch and precision reduction of the water depth inversion model caused by dynamic change of optical characteristics of a coastal water body in seasonal and tidal scales are solved.

The invention relates to the technical field of data integration, and particularly discloses a perfluoro / polyfluoroalkyl substance multi-source exposure data integration risk assessment system, which is characterized in that environmental exposure data of perfluoro / polyfluoroalkyl substances is acquired through a multi-source environmental sensor array, and a PFAS multi-mode exposure feature database is established; carrying out pollution source isotope fingerprint analysis, and obtaining source contribution rate distribution maps of three pollution sources of industrial emission, surface runoff and atmospheric settlement through a nonlinear source analysis algorithm; constructing a three-dimensional geographic information dynamic migration model according to the source contribution rate distribution map, and generating a multi-medium dynamic migration flux matrix; a composite risk assessment model is established based on the multi-medium dynamic migration flux matrix, probability risk assessment is executed in combination with an ecological toxicity threshold database, and a space gridding risk grade map is output; the method not only fills the blank of the prior art in the aspects of multi-medium dynamic modeling and nonlinear source analysis, but also provides powerful technical support for environmental pollution control and ecological risk prevention and control.

The invention discloses a water electrolysis hydrogen production intelligent control system and method based on artificial intelligence. The system and method are suitable for a large-scale water electrolysis hydrogen production scene under the power supply condition of fluctuating renewable energy sources such as wind power and photovoltaic. The system comprises a data acquisition layer, an edge calculation layer, an intelligent control layer and an execution layer. Dynamic modeling of the running state of the hydrogen production system is achieved through multi-parameter real-time monitoring and feature extraction. The intelligent control layer fuses an LSTM prediction module and a reinforcement learning controller, the LSTM prediction module is used for predicting future renewable energy input and hydrogen demand trends, and the reinforcement learning controller calculates an optimal current density set value based on a prediction result so as to maximize hydrogen production efficiency per unit energy consumption. And meanwhile, a digital twinning technology and a safety protection mechanism are combined, thermoelectric dual regulation and control of the electrolysis process are achieved, and the control response speed and the system stability are improved. According to the invention, energy consumption can be effectively reduced, the service life of the stack is prolonged, and the operation efficiency and reliability of the hydrogen production system under complex load are improved.

A system and method for predictive analysis, scenario simulation, and decision optimization is provided. The system includes a prediction management system executed on a distributed computing infrastructure, and a prediction engine configured to receive input data, including event parameters, user-defined constraints, real-time data feeds, and historical trends. The prediction engine generates predictive models using algorithms trained on historical event outcomes, assigns probability scores and confidence intervals to potential outcomes, and dynamically updates the models based on new input data. Actionable insights are generated and ranked according to predefined success criteria. A non-transitory computer-readable medium is used to store the predictive models, outcome probabilities, and actionable insights for subsequent analysis and reporting. This system facilitates enhanced decision-making by offering real-time insights and continuously refined predictions, thereby optimizing responses to complex events and scenarios.

The invention discloses a public opinion event multi-mode semantic fusion modeling and abstract generation method and system, and relates to the field of natural language processing and social network analysis. Through the multi-mode semantic fusion technology, the short text understanding ability is improved, and the problems of semantic fuzziness and network language diversification are solved. Meanwhile, through a cross-window event cluster matching technology, an event evolution path with time continuity is constructed, and comprehensive capture of event dynamic characteristics is realized. Besides, the structured event abstract is automatically generated by utilizing the generative model, so that the consistency and the information density of the abstract are improved, and the actual application requirements are met. Through the innovations, the defects in the aspects of semantic comprehension, dynamic modeling and abstract generation in the prior art can be effectively overcome, a more efficient and accurate solution is provided for monitoring and analysis of public opinion events, and the method has wide application prospects in the fields of public opinion monitoring, emergency early warning, social media data analysis and the like.

The invention relates to the technical field of data center dynamic modeling, and discloses a twin model dynamic modeling method for a data center DCIM platform, which comprises the following steps: constructing a discrete state space model containing a thermal coupling matrix and a system matrix, collecting real-time power and temperature time sequence data, and calculating a cross-correlation function to lock hot air dynamic transmission lag time; calculating cut-off frequency based on physical attributes of the cabinet and decomposing data into high and low frequency components by using a complementary filter; according to the method, the model parameters are made to return to a physical source through a frequency domain decoupling mechanism, the problem of aliasing of airflow coupling and structural thermal inertia parameters in a traditional single-scale identification method is solved, and the method is suitable for large-scale identification. And the physical authenticity and prediction robustness of the twin model under a complex working condition are improved.

The invention relates to the technical field of power system automation, and particularly discloses a cross-domain heterogeneous computing power real-time calling and unified scheduling system for the power industry, which comprises a computing power resource global sensing unit, a business demand dynamic modeling unit, a cross-domain unified scheduling decision unit and a task execution and feedback control unit, according to the method, heterogeneous computing power states are collected in real time, task requirements are dynamically modeled, a scheduling scheme is generated based on multi-objective optimization, and task execution monitoring and load balancing are realized by means of closed-loop feedback, so that the utilization efficiency of global computing power resources is improved, and the real-time performance and reliability of power business are guaranteed.

The invention relates to the technical field of three-dimensional modeling, in particular to a dynamic modeling method for a geological structure three-dimensional model. The method comprises the steps that multi-source data are acquired and preprocessed, a voxel semantic fusion algorithm based on variational optimization is introduced, after semantic information is extracted from the preprocessed multi-source data, the preprocessed multi-source data are mapped to a target three-dimensional space grid, and an optimal semantic fusion vector is obtained; based on the optimal semantic fusion vector, generating a standard voxel data pool, and constructing a geological structure three-dimensional model; monitoring data change, calculating the position of a newly added data point, combining the standard voxel data pool to obtain a space updating area, and modeling the space updating area to realize model updating; and after the modeling of the space updating region is completed, optimizing the boundary continuity. The problems that multi-source geological data cannot be directly used for structure construction and semantic fusion of a three-dimensional model, dynamic response to newly-added data is lacked, and geometric discontinuity and structural logic discontinuity exist at the boundary are solved.

The invention discloses a high-efficiency time sequence optical flow method, system and device for fusing event and image information and a medium. The method comprises the following steps: constructing a time sequence feature extraction module; constructing a double-branch collaborative iteration optical flow module of the event and the image; constructing a fusion modeling and iterative optimization optical flow module based on cross attention; training a dual-branch collaborative iteration optical flow module of the event and the image and a fusion modeling and iterative optimization optical flow module based on cross attention on a training set of an MVSEC public data set to obtain weights, and testing on a test set of the MVSEC public data set to obtain an optical flow calculation result; the system, the equipment and the medium are used for implementing the method. The method has the advantages of high time resolution, high dynamic modeling capability, fine multi-modal fusion, high estimation precision and the like, the motion sensing performance in a complex scene is remarkably improved, and powerful technical support is provided for an intelligent visual system.

The invention relates to the field of roadway surrounding rock deformation analysis, in particular to a roadway surrounding rock deformation intelligent monitoring method and device based on three-dimensional laser scanning. The method comprises the following steps: performing three-dimensional laser scanning on a roadway to generate equalized laser point cloud data; performing point cloud relative deformation detection and surrounding rock deformation dynamic modeling on the equalized laser point cloud data to construct a dynamic surrounding rock deformation model; performing space grid division on the dynamic surrounding rock deformation model, and performing differential change trend analysis to generate a multi-unit deformation trend; performing nonlinear evolution analysis and deformation situation prediction in a future time period on the multi-unit deformation trend to generate a deformation prediction result; and performing deformation situation interference simulation on the deformation prediction result, and extracting various interference simulation results. According to the invention, surrounding rock deformation data is accurately obtained in real time, and the deformation trend is dynamically analyzed and predicted, so that early warning is performed in advance, the safety risk is effectively reduced, and the safety and monitoring efficiency of underground engineering are improved.

The invention is suitable for the technical field of road construction, and provides a wind power plant road intelligent line selection method based on multi-dimensional constraint dynamic modeling, and the method comprises the steps: obtaining a multi-dimensional constraint condition of wind power plant road line selection, and constructing a constraint model; generating a slope grid map based on a digital elevation model, and fusing the slope grid map with a constraint map layer reflecting geological stability and a land cover type to generate a dynamic cost surface; adopting an improved heuristic search algorithm to perform parallel path search from the road starting point to each target fan position, generating a plurality of initial feasible paths, and calculating a comprehensive cost value; screening a candidate line set according to a comparison result of the comprehensive cost values; receiving real-time updating data in a construction process and environment monitoring, inputting the real-time updating data into the constraint model to trigger model updating, performing iterative optimization on the candidate line set based on the updated constraint model, and outputting a final line selection scheme; and the line selection efficiency, economy and reliability are effectively improved.

The invention relates to the technical field of dynamic modeling and digital twinning, and discloses a digital twinning dynamic modeling method for new material research and development, which comprises the following steps: constructing a low-order linear reference model, calculating a residual signal flow, and extracting residual non-statistical topological characteristics by using a sliding window to retrieve a system matrix correction increment; constructing a parallel shadow verification model to execute virtual deduction, and screening and executing parameter hopping according to a convergence divergence ratio; according to the method, through a closed-loop mechanism of residual form fingerprint identification and shadow deduction verification, the problem of capturing discontinuous physical phase change under low computing power is solved, and real-time high-fidelity synchronization of the model on complex microscopic evolution is realized while environmental noise interference is suppressed.

The invention discloses a polymorphic data asset context analysis and industry chain collaboration method, and particularly relates to the technical field of data intelligence and industry collaboration, comprising the following steps: accessing multi-source data through a heterogeneous adapter group to generate a four-dimensional intermediate representation containing entities, relationships, timestamps and space coordinates; inputting the representation into a space-time diagram neural network to construct a data association dynamic matrix carrying a dynamic attenuation weight; performing space-time interpolation restoration on the matrix missing link to form a global connected data asset topology network; generating a cross-enterprise security collaboration map based on the differential privacy chain architecture hierarchical encryption topology network; activating the four-dimensional relation chain in response to the collaborative request, and outputting a Nash equilibrium strategy through multi-agent reinforcement learning; an asset value report is generated after the strategy is executed, data distribution drift is detected based on KL divergence, and incremental updating of the data association dynamic matrix is triggered. According to the method, the problems of dynamic modeling and security collaboration of multi-source heterogeneous data are solved, and the decision-making accuracy of an industrial chain is remarkably improved.

The invention relates to the technical field of power system safe operation safety and intelligent monitoring, and discloses a vegetation intrusion risk assessment method and system for a power transmission corridor and electronic equipment, and the method comprises the steps: obtaining multi-source remote sensing data and meteorological data of vegetation in a preset range of a target power transmission corridor; performing data fusion on the multi-source remote sensing data to obtain fused data; extracting a vegetation height change sequence based on the fused data, and performing prediction based on the vegetation height change sequence and the meteorological data to obtain a future time sequence height change curve; obtaining engineering parameters of a target power transmission corridor, and calculating a vegetation invasion risk value based on the engineering parameters and a future time sequence height change curve; and performing risk assessment based on the vegetation invasion risk value. The method breaks through the limitation of traditional static monitoring, integrates InSAR time sequence analysis and a machine learning algorithm, realizes vegetation growth dynamic modeling and future trend prediction, realizes risk level quantitative evaluation in combination with power transmission line structure parameters, and can provide technical support for power grid risk early warning.

The invention relates to an intelligent work order automatic creation and circulation system and method based on context awareness, provides dynamic modeling, and relates to the field of systems or methods specially suitable for administrative, commercial, financial, management or supervision purposes. The system comprises a work order analysis device which is used for dynamically modeling a convolutional neural network model, and intelligently analyzing a creation demand and a creation type of a skill work order of each consultation work order according to consultation associated data of each consultation work order in a recent time interval; and the automatic creation device is used for executing automatic creation of a subsequent skill work order based on the intelligent analysis result. In order to solve the technical problem that the skill work order of the tourism e-commerce is difficult to synchronously analyze the creation demand and the creation type, an artificial intelligence model obtained by dynamic modeling of the tourism e-commerce is used, and according to context sensing data of a visitor in each consultation work order, synchronous analysis of the creation demand and the creation type of the subsequent skill work order is realized; therefore, the technical problem is solved.

The invention provides a space-time-causal double-path knowledge migration cross-domain recommendation method, and aims to perform dynamic modeling and fine migration on a complex behavior pattern of a user so as to solve the problems of negative migration and low multi-dimensional feature fusion efficiency caused by insufficient dynamic capture of user interests and lack of causal understanding in existing cross-domain recommendation. The method comprises the following steps: (1) constructing a multi-field interaction data set; (2) inputting and sharing behavior codes; (3) carrying out parallel modeling on a space-time path and a causal path; (4) dynamic gating fusion; and (5) cross-domain knowledge migration and recommendation prediction.

The invention discloses a heterogeneous network embedding method based on dynamic heterogeneous network decomposition and a hierarchical space-time attention mechanism. The method comprises the following steps: dynamic network decomposition: decomposing a heterogeneous network sequence into independent subgraph sets according to edge types; multi-order node attention aggregation: for each sub-graph, fusing information of nodes and different edge types; semantic level attention is embedded, wherein global semantic embedding is generated in combination with global relation type weight and meta-path long-short-term memory network coding; time dynamic modeling: respectively calculating a time attention weight and a Monte Carlo sampling approximate Horkes intensity, and then aggregating and splicing results of the time attention weight and the Monte Carlo sampling approximate Horkes intensity to obtain a final embedding result. According to the method, through cross-granularity semantic modeling and efficient time sequence dependence learning, an innovative solution is provided for node representation of the dynamic heterogeneous network.

The invention provides a multi-mode fusion laser directional energy deposition real-time monitoring method and system, and belongs to the technical field of additive manufacturing. Comprising the steps that real-time data collection is conducted on a molten pool area in the laser directional energy deposition process through synchronous triggering of a multi-source sensor; a distributed clock network is constructed, environment influence parameters are collected in real time in combination with a working condition sensing unit, dynamic modeling and correction are conducted on asymmetric link delay in the clock synchronization process, and space-time alignment of multi-source sensor data is achieved; after the multi-source sensor data subjected to space-time alignment is preprocessed, data-level fusion, feature-level fusion and decision-level fusion are performed in sequence. The state of the molten pool can be accurately monitored in real time, and the quality and stability of the laser directional energy deposition process are improved.

The invention relates to a knowledge graph construction and retrieval optimization method based on water affair data, and the method comprises the steps: taking the unified knowledge expression of a water affair object and an event thereof as a target, determining a water affair business field and business range, and constructing a water affair knowledge graph conceptual model covering a static knowledge ontology and a dynamic event ontology; performing knowledge extraction on the obtained multi-source heterogeneous water affair data, converting the data into a unified triple form, and constructing a data layer of a water affair knowledge graph; and on the basis of the data layer, node, path and region query is performed on the knowledge graph by adopting a graph algorithm, and semantic enhancement retrieval and reasoning are performed in combination with a large language model. By constructing the water affair knowledge graph and introducing knowledge extraction and retrieval enhancement capability of a large language model, dynamic modeling and semantic reasoning of water affair objects, events and relationships thereof are realized, high-risk area identification, emergency path planning and emergency early warning are supported, and the knowledge utilization rate and intelligent decision level in the water affair field are improved.

The invention discloses an elevator operation safety intelligent monitoring method and system, and relates to the technical field of elevator operation safety monitoring, and the method comprises the steps: collecting elevator multi-source operation data in real time through a bus interface, arranging sensors in a lift car, a shaft and a machine room, synchronously sampling various signals, building an operation state machine by combining position information and a door lock state, dividing the operation process into a plurality of working condition sections and organizing original data according to the working condition sections; calculating a multi-dimensional health index in a working condition section, and dynamically modeling an adaptability threshold by combining real-time characteristics and historical statistical data; and multi-dimensional indexes are compared in real time based on threshold values, and abnormal scores are calculated in a fused mode, so that fault sources are recognized, and intelligent monitoring of elevator operation safety is achieved. According to the method, through multi-source data real-time collection, working condition section division, dynamic threshold modeling and multi-index fusion analysis, refined monitoring of the elevator running state is achieved, the accuracy of anomaly detection and the fault positioning capacity are improved, and the safety and reliability of the elevator are improved.

The invention discloses a source load impact and system bearing life evaluation method and system, equipment and a medium, and relates to the technical field of traction power supply, and the method comprises the steps: collecting data to recognize impact, building a source load model, analyzing an impact diffusion path, evaluating system bearing, fusing multi-dimensional damage, calculating the residual life of the equipment, and automatically updating parameters. And generating a closed-loop regulation and control strategy. The system comprises a data analysis and dynamic modeling module, a bearing evaluation and conduction analysis module, a damage coupling and life prediction module and a parameter correction and strategy generation module. The traction impact time domain-frequency domain-topology integrated quantization is realized, and the problem of impact time-space diffusion assessment is solved. A life model is constructed, and multi-dimensional stress damage is accurately mapped. The micro-simulation and on-line filtering technology is utilized to ensure that model parameters are adaptively updated along with working conditions, and the problem of disjunction between evaluation and actual conditions is solved. The method supports side cloud cooperation and closed-loop pre-control, effectively improves the safety margin of the power grid, and delays the aging of equipment.

The invention provides a data-driven saline screw compressor unit dynamic modeling method, and belongs to the technical field of industrial intelligence and predictive maintenance. According to the method, adaptive deep denoising of original data is realized through a composite signal processing flow of fusing variational mode decomposition, permutation entropy criterion and wavelet packet transformation optimal threshold denoising; meanwhile, a sectional sampling strategy is introduced to enhance the diversity of training data. Then, a wavelet multi-scale energy entropy extraction layer is used for constructing a high-information-density feature vector; furthermore, a prediction model formed by multiple layers of stacked long and short-term memory network units is adopted, and the complex time sequence dependency relationship of the system is deeply captured. According to the method, pure and stable system dynamic representation can be extracted from high-noise industrial data, the dynamic characteristics of the system in the full working condition range are accurately described, and it is ensured that the prediction result is self-consistent physically and reliable in engineering, so that the prediction precision and generalization performance of the model are remarkably improved.

The invention relates to the technical field of geological modeling, and discloses an underground reservoir three-dimensional geological model construction method and system. The method comprises the following steps: screening drilling data through a preset precision threshold value, correcting topographic data by using a first precision drilling data set to generate a topographic surface model, reversely calibrating a second precision drilling hole, and establishing a multivariate data fusion mechanism; layering standardization is achieved in combination with sedimentary cycle characteristics, aquifer units are automatically divided, and stratum lithology codes are compiled; a three-dimensional model is generated by adopting an implicit modeling method based on modeling boundary constraints, and the model precision is verified through a profile comparison method and a leave-one-out method; and converting the adjusted geological section into a three-dimensional constraint surface to drive the generation of a virtual drill hole so as to realize incremental updating of the three-dimensional geological model. According to the method, the spatial precision of the terrain surface model is remarkably improved, the problem of non-uniform reference of multivariate heterogeneous data is solved, the reliability and accuracy of model construction are improved, the virtual drilling data set is generated, the data are dynamically modeled, and the dynamic updating of the model is realized.

The invention discloses an ecological restoration dynamic modeling system for a power grid project slash in a high-altitude area of the Tianshan Mountain in Xinjiang, and the system comprises an obtaining module which is used for obtaining historical multi-source heterogeneous ecological data of the power grid project slash in the high-altitude area of the Tianshan Mountain in Xinjiang Mountain, and carrying out the standardization processing of the historical multi-source heterogeneous ecological data to generate an ecological data set; the historical multi-source heterogeneous ecological data comprises climate data, soil data, hydrological data and vegetation data; the model construction module is used for constructing a climate-soil-hydrology-vegetation four-dimensional coupling ecological restoration dynamic prediction model based on the ecological data set; the dynamic simulation module is used for presetting an ecological restoration scene; inputting a preset ecological restoration scene into the four-dimensional coupling ecological restoration dynamic prediction model for dynamic simulation prediction, and when a dynamic simulation prediction result meets requirements, obtaining an ecological restoration dynamic prediction model of the power grid project slash in the high-altitude area of Tianshan Mountain in Xinjiang; and carrying out accurate dynamic simulation and prediction on the ecological system of the project slash, and formulating and adjusting a scientific full-period management and protection scheme.

The invention provides an intelligent construction site safety monitoring and early warning method and system, and the method comprises the steps: fusing the multi-source data, such as personnel positioning, equipment state and environment parameters, and building a structured time sequence feature set with spatial semantic annotation; constructing a symbolized rule base in combination with a field safety specification; applying a dynamic graph attention network, dynamically modeling personnel, equipment, environment nodes and an interaction relationship thereof, and deducing potential causal association strength between spatial entities; through path semantic distillation and anti-fact intervention, a compliant and accurate multi-stage causal reasoning path is formed, and key risk nodes and propagation channels are visualized in a three-dimensional BIM scene; and finally, dynamic early warning event generation and natural language interpretation are realized, model closed-loop optimization based on field feedback is supported, and the timeliness, interpretation and adaptive capability of construction site safety risk discovery are improved.

The invention discloses a dam body stability analysis method based on three-dimensional dynamic modeling, and belongs to the technical field of dam stability analysis. The method comprises the following steps: step 100, constructing a dam body three-dimensional dynamic evolution model; step 200, obtaining dam body monitoring data, processing the dam body monitoring data, and updating and driving the three-dimensional dynamic evolution model to evolve in real time based on the processed dam body monitoring data; and step 300, analyzing and predicting the stability of the dam body of the dam. By introducing the three-dimensional dynamic modeling technology and combining the efficient fusion and real-time updating mechanism of multi-source data, the accuracy and efficiency of dam stability analysis are improved, and therefore the requirement of modern water conservancy projects for safe operation of dams is better met.

The invention discloses an intelligent data question and answer method and system based on index semantic enhancement, and relates to the technical field of intelligent data question and answer. The method comprises the following steps: acquiring a data analysis demand in a natural language form; the method comprises the following steps: performing information extraction on a data analysis demand by utilizing an index semantic enhanced large language model, generating a query statement according to the extracted information, recalling and rearranging most relevant indexes and elements in an index semantic knowledge base by utilizing a knowledge retrieval enhancement layer according to the data analysis demand and a historical session on the basis of mixed retrieval, and generating a query statement according to the query statement. Generating a prompt template according to a retrieval result of the index semantic injection layer by utilizing the index semantic injection layer, and performing semantic understanding reasoning according to the prompt template to obtain corresponding information; and checking compliance according to the query statement, and returning a query result. The method aims at achieving efficient mapping, dynamic modeling, intelligent SQL generation and optimization, authority safety management and control and multi-round intelligent interaction of natural languages and enterprise-level index assets.

The invention discloses a multi-parameter shallow gas detection method which comprises the following steps: S1, self-adaptive sensing network layout and multi-parameter synchronous acquisition: monitoring nodes synchronously acquire underground electrical parameters, soil gas composition and concentration, soil mechanics and pore pressure parameters and an environmental noise background value; accessing a hydro-meteorological data stream of the target area in real time; s2, multi-source interference signal identification and data purification: obtaining a purified multi-parameter data sequence reflecting real geological anomalies; s3, water-gas-force coupling dynamic modeling and migration simulation: outputting a shallow gas migration risk thermodynamic diagram; s4, intelligent diagnosis based on transfer learning and causal discovery: performing time sequence analysis on the purified multi-parameter data sequence, and identifying a gas reservoir core area, a dominant migration channel and dominant control factors influencing gas aggregation and migration; and S5, dynamic risk assessment and graded early warning: quantitatively calculating the risk probability.

The invention provides an intelligent optimization method and system for a mine water deep reinjection horizon, and the method comprises the steps: 1, constructing a three-dimensional geological twin body of a mining area, which comprises the definition of a multi-objective function: maximizing a safety objective f1; minimizing the economical efficiency target f2; the environmental protection target f3 is maximized; 2, multi-objective optimization and Pareto optimal solution set generation are carried out; step 3, intelligent decision and optimal horizon determination: step 301, selecting an optimal horizon; 302, chemical components of mine water are determined, and all buried layer positions are marked as preliminary screening layer positions; and step 303, obtaining comprehensive compatibility indexes of the preliminary screening horizon, and marking the preliminary screening horizon with the maximum value in the comprehensive compatibility indexes as a preferred horizon. According to the method, the horizon optimization efficiency is improved through dynamic modeling and multi-objective optimization, the optimization accuracy is ensured by means of multi-dimensional compatibility evaluation and multi-field coupling analysis, and a technical path with high efficiency and reliability is provided for scientific decision-making of the mine water reinjection horizon.

The invention discloses a strip mine slope weak plane dynamic modeling system based on real-time working condition feedback, and belongs to the technical field of geographic model 3D dynamic modeling of computer graphics. Comprising a data acquisition module, a data preprocessing module, a modeling and updating module, a working condition feedback calculation module and a working condition feedback adjustment module which are connected in sequence, the method comprises the following steps: calculating an overlapping region and an adjacent overlapping region of a working condition operation influence region and a strip mine slope three-dimensional model, and intensity data of coordinate points of a three-dimensional weak surface under the influence of industrial and mining operation, and labeling and dynamically updating to generate inspection information of the overlapping region and the adjacent overlapping region; meanwhile, the three-dimensional model of the strip mine slope with more accurate dangerous area form is reconstructed and updated, the accuracy and timeliness of weak plane stability analysis and early warning are improved, and the problems that in the prior art, serious defects exist in the aspect of associating real-time mining working conditions, weak plane stability analysis lags behind, accuracy is low, and early warning accuracy and timeliness are low are solved.