22results about How to "Improve exploration efficiency" patented technology

The invention discloses a layered planning method for indoor autonomous exploration of a mobile robot based on dynamic prediction, and the method comprises the steps: S1, dividing an exploration environment where the robot is located into a plurality of local subspaces, and marking the state of each subspace as an unexplored state, an explored state, an explored state or a semi-explored state according to the data of a sensor and a semantic detection result; s2, establishing a global topological graph connected with each subspace, and planning a global access path by using a traveling salesman problem model; s3, integrating a pedestrian trajectory prediction model in the selected current subspace, and evaluating and selecting a local viewpoint capable of maximizing information gain and minimizing dynamic collision risk based on a prediction result; the method has the beneficial effects that a semi-exploration state and a self-adaptive revisit mechanism are introduced, and when the door is possibly opened, the check is turned back, so that the dead angle of a map is eliminated, and the exploration completeness is improved; pedestrian trajectory prediction is incorporated into viewpoint scoring, and people deadlock is avoided.

This invention discloses a well placement method, equipment, and storage medium for bioherm-type carbonate reservoirs. The well placement method includes the following steps: S1. Obtaining the amplitude spectrum data of the reservoir to be well placed; S2. Classifying the bioherm type and reservoir category, and determining the connectivity of the reservoir; S3. Determining the optimal well placement area and optimal well placement pattern based on the determined bioherm type, reservoir category, and reservoir connectivity; S4. Performing economic calculations on the wells to be placed within the optimal well placement area, establishing an economic model, and determining the optimal well spacing d. i And the optimal number of oil and gas wells N i This method, tailored to the characteristics of bioherm-type carbonate reservoirs, classifies them into different types, distribution ranges, and connectivity, providing a reliable reference for well placement. It designs optimal well placement areas and patterns, effectively improving well development efficiency. An economic model is established through economic accounting to optimize well spacing and the number of oil and gas wells, thereby increasing reservoir development utilization and economic benefits.

This application provides a method and related apparatus for processing landmarks in a map. The method includes: a first object inputting and confirming a first landmark code; a terminal obtaining import information of a first landmark set exported by a second object corresponding to the first landmark code; the first object confirming the import information; and the terminal importing the first landmark set into the application map page based on the import information. Based on this, the first object does not need to manually mark each landmark in the application map to gradually explore the application map. Instead, it obtains the import information of the first landmark set exported by the second object by inputting and confirming the first landmark code corresponding to the first landmark set exported by the second object. After confirming the import information, the first landmark set exported by the second object is successfully imported into the application map page. This method is relatively simple to operate, consumes less time, improves the efficiency of landmark marking in the map, and thus improves the map exploration efficiency.

The application discloses a method and system for predicting deep hydrocarbon source rock organic matter enrichment law based on glowworm algorithm optimized GBDT, and belongs to the field of hydrocarbon source rock TOC prediction. Specifically, the method comprises the following steps: obtaining logging curve data; preprocessing the obtained logging curve data; based on the preprocessed data, establishing a machine learning model (the core lies in the hyperparameter optimization of the model, and the glowworm algorithm is particularly used in the optimization process); and using the established TOC prediction model to predict the total organic carbon content. The application combines machine learning and intelligent optimization algorithm and predicts the organic matter enrichment law of deep hydrocarbon source rock, which can greatly improve the prediction accuracy and precision, and reduce the exploration cost and risk.

The application provides a relative velocity analysis well-free constraint relative wave impedance inversion method, which comprises the following steps: step 1, velocity analysis is carried out by using seismic data to establish the time-depth relationship of a study area; step 2, seismic facies analysis is carried out on the seismic data to determine the geological model of the study area; step 3, velocity correlation analysis is carried out to determine the velocity migration amount; step 4, the velocity curve of a seismic trace is extracted; step 5, relative velocity normalization analysis of different lithologies at the same depth point is carried out; step 6, relative velocity wave impedance inversion processing is carried out; and step 7, lithology and reservoir identification, tracking and description are carried out. The relative velocity analysis well-free constraint relative wave impedance inversion method breaks through the limitation of conventional inversion which can only be used for single target layer and time domain wave impedance inversion, eliminates the influence of different velocities in time domain and depth domain of multiple target layers, and provides relative wave impedance inversion data for lithology or reservoir prediction under complex conditions in a well-free exploration area.

The invention relates to the technical field of computer and big data management, and discloses a data interaction method of a handheld tablet personal computer, which comprises the following steps: receiving a big data query instruction, and obtaining a high-dimensional data set; a three-dimensional point cloud is generated through graph neural network dimension reduction embedding; constructing and rendering a dynamic interactive three-dimensional topological mapping model; responding to the multi-finger gesture operation to adjust the spatial layout and the clustering structure of the data entity in real time; and dynamically calculating and visually coding the association strength between the data. The system comprises a query receiving module, a data acquisition module, a dimension reduction processing module, a model construction module, a rendering module, a gesture recognition module, an association calculation module, a visual coding module and the like. According to the method, through three-dimensional space mapping and multi-finger gesture interaction, the information density and association visibility are remarkably improved, local detail expansion, spatial memory anchor points and multi-user cooperation are supported, and efficient and visual big data exploration is achieved.

Embodiments of the present application provide a kind of air-based equipment cluster cooperative control method, electronic equipment, storage medium and program product, related to air-based equipment cluster control technical field, the method comprises: according to the unexplored unit on exploration map, select the target exploration position to be distributed to multiple air-based equipment, according to the cost of each air-based equipment to reach each target exploration position, obtain the distribution exploration position distributed to each air-based equipment in multiple target exploration positions, after the unit of exploration map is all explored, according to multiple user equipment positions on exploration map, select the target coverage position to be distributed to multiple air-based equipment, according to the cost of each air-based equipment to reach each target coverage position, obtain the distribution coverage position distributed to each air-based equipment in multiple target coverage positions, control air-based equipment moves to distribution coverage position.The embodiments of the present application avoid repeated exploration or omission exploration, and improve the pertinence of communication signal coverage.

The invention discloses a high-dimensional continuous control method and device based on RNA evolution adaptive maximum entropy. According to the method, through the population global search capability of RNA evolution, the exploration efficiency of high-dimensional continuous control tasks is improved, and the risk of falling into local optimum is reduced; through an adaptive entropy coefficient updating mechanism, the entropy coefficient is dynamically adjusted along with the training stage and the task dimension, and manual parameter adjustment is reduced; evolutionary search and maximum entropy gradient updating are cooperatively coupled, global optimization and local fine optimization are considered, and the convergence speed and stability are improved; and premature convergence is inhibited through diversity monitoring and a noise injection mechanism, so that the strategy robustness and generalization ability are improved.

This invention discloses a method, apparatus, electronic device, and storage medium for determining sweet spots in coal formations. The method involves acquiring the target well's logging curves for the target formation under at least one detection parameter; determining the logging reference parameters for each detection parameter based on the coal sampling information and the logging curves for the target formation; identifying at least one coal formation when the logging reference parameters and the logging curve parameters to be used in the logging curves meet a first preset condition; determining the organic carbon content information of the coal formation for each coal formation based on the organic carbon content determination function, the logging reference parameters, and the logging curve parameters to be used; and identifying sweet spots in coal formations based on the organic carbon content information of at least one coal formation. This method ensures the accuracy of identifying sweet spots in the target formation, provides guidance for the exploration and development of the target formation, and helps improve exploration efficiency and benefits.

This invention provides a method for predicting deep solid potash deposits using brine hydrogeochemical simulation. The core of this method lies in using brine to locate potash. By simultaneously collecting surface saline spring water and deep borehole brine from the same target area, the method calculates and compares the brine characteristic coefficients of both. Based on this invention, a quantitative relationship of brine coefficient thresholds is proposed to determine the brine's formation, evaporation rate, and the influence of atmospheric precipitation. Brine samples indicating favorable deep potash-forming environments are screened. Then, a hydrogeochemical model is constructed and corrected, and the simulated evaporation mineral precipitation sequence is performed on the screened samples. The saturation indices of target minerals such as halite and carnallite are calculated. The core criterion is that the SI value of halite approaches -0.5 to +0.5, combined with the fact that halite is unsaturated and carnallite is nearly saturated. The exploration target area is delineated using a comprehensive multi-indicator approach. This invention aims to significantly improve the prediction accuracy and exploration efficiency of deep solid potash deposits under complex geological conditions.

This invention relates to the field of rock and soil exploration technology, and discloses a rock and soil drilling exploration device for engineering construction. The device includes a main body with a support frame rotatably connected inside. A drill rod is mounted on the side wall of the support frame. When two arc-shaped plates form a cross shape around the outer surface of the drill rod, the invention enhances the rotational force of the motor, causing the motor to drive the drill rod to rotate and disengage from a jammed state via a threaded shaft. After disengagement, the hydraulic rod retracts to its output end. As the hydraulic rod retracts, it resets the two arc-shaped plates, allowing them to fit onto the outer surface of the drill rod, enabling normal operation. This reduces the likelihood of the drill rod jamming when encountering harder rock layers during rotation within the rock and soil strata, thus improving the exploration quality of the device.

This invention provides a method and apparatus for predicting in-situ stress and selecting fracturing zones in volcanic rock reservoirs, belonging to the fields of geological engineering and petroleum exploration technology. The method for predicting in-situ stress in volcanic rock reservoirs includes: acquiring pre-stack seismic data and well logging data of the area to be tested; performing joint inversion using the pre-stack seismic data and well logging data; obtaining a first rock mechanical parameter based on the inversion results; calculating a second rock mechanical parameter based on the first rock mechanical parameter; performing azimuth anisotropy inversion using the pre-stack seismic data to obtain a third rock mechanical parameter; and calculating the in-situ stress of the area to be tested based on the first, second, and third rock mechanical parameters. This invention improves the accuracy of in-situ stress prediction in volcanic rock reservoirs by comprehensively considering various formation characteristics, which helps optimize the selection of fracturing zones and improve the efficiency and economic benefits of petroleum exploration and development.

The invention discloses a robot exploration mapping method, related equipment and a computer program product, a traditional RRT algorithm is improved in the robot exploration mapping process, the size of a random number action range is dynamically updated according to a map explored by a robot at the current moment, and the robot exploration mapping efficiency is improved. In other words, the action range of the random number is expanded along with expansion of the explored map, so that sampling points can be randomly generated in the relatively small action range of the random number at the initial stage of exploration, and the expansion direction of the rapid exploration random tree is guided to generate candidate exploration points, so that the randomly generated sampling points are closer to the current position of the robot; that is, the sampling point is more meaningful to the RRT algorithm, and effective exploration points can be generated through a small number of iterations, so that the map exploration efficiency is improved. And moreover, the action range of the random number is expanded along with the expansion of the explored map, so that the global coverage of exploration can also be ensured.

The application provides a prospecting method for a brine type lithium-potassium-boron deposit in a fault basin tectonic conversion area, and the method comprises the following steps: determining a target layer type; obtaining gravity and magnetic data and seismic data of the fault basin, identifying faults and / or identifying deep faults and / or identifying volcanic rock bodies, and determining a first favorable ore-forming tectonic unit; for the first favorable ore-forming tectonic unit, analyzing differential subsidence of adjacent tectonic units, and determining a second favorable ore-forming tectonic unit; for the second favorable ore-forming tectonic unit, determining a first favorable exploration area, and further determining a second favorable exploration area; and for the determined second favorable exploration area, drilling verification is carried out, and physical parameters of the brine type lithium-potassium-boron deposit are detected. The application can quickly and accurately delineate the brine type lithium-potassium-boron deposit in the fault basin, and is especially suitable for rapid positioning and evaluation of the brine type lithium-potassium-boron resource in the continental fault basin.

The present application relates to a kind of green prediction method and device of mineral based on cross-domain self-adaptation and multimodal fusion, belong to mineral resources exploration and remote sensing technical field.Multiple-source data is obtained, input transfer learning module learns transferable model parameters, the data obtained is fused through modal gating network, then through graph construction model constructs line mapping, and is enhanced using graph convolution network;After enhancement, multimodal data is input into the Transformer encoder of embedded graph convolution network, extract the spatial topology structure and spectral feature of mining area, obtain the potential probability graph of ore body through the prediction head prediction;Environmental risk assessment model is built, " maximum of ore body prediction accuracy " and " minimum of environmental risk " are realized through multi-objective optimization, and the accuracy of ore body prediction is verified through drilling activity.This method not only improves the regional generalization ability of model, but also realizes green mineral exploration and the interpretability of result, improves the precision, efficiency and sustainability of mineral exploration.

This invention relates to the field of geological exploration technology, specifically disclosing a coal mine hydrogeological exploration device and method. The device includes a drill rod having a first rod section and a second rod section, the diameter of the first rod section being larger than the diameter of the second rod section; an exploration component including a first ring and a second ring sleeved on the second rod section, the outer diameter of the first ring and the second ring being the same as the diameter of the first rod section, and the first ring and the second ring being connected by a folding curtain, the folding curtain having through holes; by setting the exploration component on the drill rod, when drilling reaches the exploration position, the first ring and the second ring are controlled to move away from each other along the second rod section, causing the folding curtain to unfold. The folding curtain contacts the inner wall of the borehole, providing a supporting effect and forming an exploration space within the borehole, avoiding the problem of borehole wall collapse leading to the inability to conduct normal exploration.

The present application relates to the oil and gas geophysical exploration seismic data processing technical field, specifically relates to a kind of seismic data low-frequency signal inversion method and system, method includes: obtaining initial seismic data;According to initial seismic data, determine the highest frequency that needs to be carried out low frequency recovery;Based on initial seismic data, estimated seismic wavelet is obtained;The gradient corresponding to different directions in initial seismic data is calculated, and the structure tensor is obtained according to the gradient;The structure tensor is carried out eigenvalue decomposition, and the eigenvector corresponding to the minimum eigenvalue is obtained;According to seismic wavelet, the objective function about reflection coefficient is established, the objective function is numerically solved, and the reflection coefficient is obtained;The reflection coefficient is carried out low frequency filtering with high cut frequency as the highest frequency, and the recovered low-frequency signal is obtained.The method effectively improves the quality and resolution of seismic data.

According to the high-performance alloy design method with the small sample extrapolation capability, component parameters, process parameters and performance indexes of an alloy are collected, and an original data set is constructed; a table variational auto-encoder is used for learning the distribution rule of material features in a potential space, and generation and characterization of complex components and process combinations are achieved; multi-scale features are extracted and optimized through feature engineering, and an extended feature set containing chemical composition, heat treatment parameters and microstructure information is formed; on the basis, a performance prediction agent model fusing a neural network model and a gradient boosting tree model is constructed, and high-precision performance prediction is achieved; through an active learning strategy based on uncertainty measurement, dynamic sampling and iterative optimization are carried out on a model prediction result, and the extrapolation capability and material discovery efficiency of the model are continuously improved. According to the method, automatic recommendation and optimization of the high-performance aluminum alloy material under the small sample condition are achieved, and the intelligence and the high-throughput level of material design are improved.

The application discloses a kind of based on double coupling agent network's multiple sub-target task implementation method, comprising: the state of current environment and desired target of real-time acquisition are input into trained double coupling agent network, obtain corresponding multiple sub-target task implementation strategy;Double coupling agent network includes: using agent: main network A obtains corresponding action and its evaluation value and external reward according to current environment state and target, and is stored to experience playback area;Target network A completes the optimization of main network A according to evaluation value;Exploration agent: main network B is randomly obtained from experience playback area sample, predicts the next state of this sample state, and calculates internal reward, and target network B is optimized to main network B according to predicted state and internal reward, coupling network generates action and its external reward according to current environment state and target, and external reward is used to update using agent, and completes the optimization of main network B according to internal reward.

The utility model relates to an exploration device for investigating mineral resources, which comprises an outer shell, an exploration mechanism is arranged inside the outer shell, a portable sample storage mechanism is movably mounted outside the outer shell, a sample storage hollow cylinder is sleeved outside the outer shell, and a sample storage mechanism is movably mounted outside the sample storage hollow cylinder. And six storage inner cavities are formed in the sample storage hollow cylinder. According to the exploration device for mineral resource investigation, the sample portable storage mechanism realizes integrated sample storage; the outer portion of the outer shell is sleeved with a sample storage hollow cylinder, six storage inner cavities in the outer shell are all provided with anti-collision protection linings, sample storage pipes can be inserted into the anti-collision protection linings, a top connecting ring and a limiting ring are in threaded connection through two fixing screws, and six containing cavities in the bottom of a rubber protection ring are matched with the tops of the sample storage pipes, so that sample storage is integrated into a device body. Extra storage equipment is not needed, the burden of exploration personnel is relieved, the anti-collision structure protects the safety of samples, disorder caused by decentralized management is avoided, and the exploration efficiency is improved.

The invention provides an SSA-LSSVM reservoir lithology fine prediction method constructed based on lithology granularity factors. The method comprises the following steps: obtaining a multi-attribute logging curve of known lithology and granularity classification in a research area; calculating texture features and gradient features to construct a high-dimensional engineering feature pool; adopting a filtering method-wrapping method mixed strategy based on a binary sparrow search algorithm to screen an optimal attribute feature combination sensitive to lithologic granularity level identification from the high-dimensional engineering feature pool; constructing a lithology granularity factor model, and optimizing the weight of the lithology granularity factor model by adopting a sparrow search algorithm to obtain an optimal lithology granularity factor model by taking maximization of the distinction degree between lithology of different granularity fractions as a target; an SSA-LSSVM lithologic granularity identification model is constructed, the model is trained based on a sparrow search algorithm, and the trained SSA-LSSVM lithologic granularity identification model is obtained; according to the method, nonlinear features and redundant information in the logging data can be effectively captured, and the classification precision is improved.

A method and apparatus for evaluating reservoir oil-bearing potential based on gas logging data, the method comprising: acquiring raw total hydrocarbon data and gas logging influencing factor data of a target formation in a study area; determining correction coefficients for each gas logging influencing factor, and obtaining corrected total hydrocarbon data based on the correction coefficients and raw total hydrocarbon data; calculating a similarity factor and a saturation factor using the corrected total hydrocarbon data; and determining the oil-bearing potential of the target formation based on the similarity factor and the saturation factor, and using a pre-established gas logging data interpretation chart in the study area.