43results about How to "Improve exploration efficiency" patented technology

The invention relates to a construction method of a cooperative combat decision-making agent for ground unmanned equipment, and aims to solve the problem of space dimension disaster of a complex cooperative combat task. The DRL solution space is effectively compressed, the exploration efficiency is improved, and meanwhile intelligent decision making of tasks with different granularities in the whole combat process is achieved; aiming at the problem of generating a'task layer 'combat command action strategy, a cooperative combat decision-making agent network model is constructed to realize the unified coordination control from the effective representation of complex state input to the combat action; by constructing a distributed training framework, the sampling and training efficiency is improved, and high-throughput asynchronous parallel training of a cooperative combat decision-making agent network model is realized; by establishing a combat decision agent learning algorithm based on deep reinforcement learning and solving the combat decision agent learning algorithm, the learning efficiency and final performance of an agent network model are effectively improved.

The invention discloses a multi-unmanned aerial vehicle target exploration and tracking method and system based on multi-agent ant colony reinforcement learning. The method comprises the following steps: dividing an actual environment for target tracking of unmanned aerial vehicles into grid units; performing target state initialization based on the grid units to obtain an initial observation position of the unmanned aerial vehicle; based on an ant colony algorithm and the initial observation position of the unmanned aerial vehicle, the transition probability of the unmanned aerial vehicle among the grid units is calculated; establishing a reward function based on the transition probability and the grid unit; based on the reward function and the existing exploration information and tracking information, performing centralized training on the multiple unmanned aerial vehicles by using a multi-agent near-end strategy optimization algorithm to obtain an optimal joint decision; and based on the optimal joint decision, enabling the unmanned aerial vehicle to complete exploration and tracking of multiple targets to be explored. According to the technical scheme, the possibility that the unmanned aerial vehicle finds the target in the unknown area is increased, and the target can be effectively followed.

The invention provides a sandstone type uranium ore prospecting method based on a tectonic unit boundary, and the method comprises the steps: obtaining the geological data of a sedimentary basin, determining the spatial distribution and the tectonic and stratum characteristics of tectonic units in the sedimentary basin, and determining the types of beneficial metallogenic tectonic units according to the spatial distribution and the tectonic and stratum characteristics of the tectonic units; and determining a first-level exploration target region of the sandstone type uranium mine by combining the gravity data and / or seismic data and / or oil and gas bearing conditions of the favorable metallogenic structure units for different types of favorable metallogenic structure units. According to the method, on the basis of deep analysis of uranium-controlled metallogenic characteristics of the tectonic unit boundary, the upheaval and fracture of the tectonic unit boundary are taken as important marks for prospecting, and geological and geophysical prospecting methods are combined, so that a novel efficient and low-cost prospecting method is formed, an optimal target of a sandstone type uranium ore prospecting area can be accurately locked, and the prospecting efficiency of the sandstone type uranium ore prospecting area is improved. The blindness in the prospecting process can be obviously reduced, and the exploration efficiency is improved.

The invention relates to the technical field of prospecting, in particular to an intelligent prospecting method based on multi-source information fusion and physical-data dual-drive, and the method comprises the steps: obtaining a target multi-source information database based on the granularity and reliability of multi-source information data in the target multi-source information database; performing complementary fusion on the multi-source information data according to the matched fusion level to obtain a multi-source information complementary fusion result; extracting mineral resource distribution information of the target area according to a multi-source information complementary fusion result, and generating an earth crust mineral resource refined distribution diagram of the target area by using the mineral resource distribution information; and based on the target multi-source information database and the earth crust mineral resource refined distribution map, generating a mineral resource information inference result of the target area by using a target identification system. According to the method, the target identification system can be utilized to perform deep mining on the earth crust mineral resource refined distribution map drawn based on the multi-source information, the mineral resource information inference result is obtained, the earth crust mineral resource exploration efficiency can be effectively improved, the resource exploration cost can be reduced, and strategic mineral resources can be accurately locked.

The invention relates to the technical field of reinforcement learning, in particular to a self-learning method and device for a non-antagonistic task, equipment and a medium, and the method comprises the steps: 1, initializing a decision neural network of the non-antagonistic task; step 2, executing an MCTS state search algorithm based on the current decision neural network, obtaining a third-party efficiency score and recording path information; step 3, performing training optimization on a decision neural network according to the third-party efficiency score and the path information; and step 4, repeatedly executing the steps 2-3 in parallel until the decision neural network meets a trigger termination condition. The self-learning method for the non-antagonistic tasks provided by the invention is not limited to task scenes, can obtain better results for all the non-antagonistic tasks, and can perform adaptive modification according to the task scenes; the shortest solution can be quickly and gradually explored from 0 for different non-antagonistic tasks, repeated calculation is avoided to the greatest extent, and the exploration efficiency is improved.

The invention discloses a comprehensive method for analyzing compressibility of a shale reservoir based on machine learning, and relates to the technical field of oil and gas field development, the comprehensive method for analyzing compressibility of the shale reservoir based on machine learning mainly comprises the following steps: obtaining a training data set according to core experiment data and corresponding logging data; constructing a three-parameter prediction model by using a random forest regression model; training the three-parameter prediction model by using the training data set to obtain a trained three-parameter prediction model, and predicting the to-be-predicted logging data to obtain three parameters; and calculating a compressibility coefficient based on the three parameters so as to obtain compressibility classification evaluation of the shale reservoir in the research area. By implementing the comprehensive method for analyzing the compressibility of the shale reservoir based on machine learning provided by the invention, the compressibility of the shale reservoir can be efficiently, accurately and universally evaluated.

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.

The invention discloses a mobile robot autonomous exploration method based on a topological graph, and relates to the field of intelligent robots. According to the method, a framework topological structure of an environment is introduced, exploration behaviors are guided by utilizing topology of the environment, and the method comprises incremental framework extraction, framework topological graph updating and exploration strategy finite-state machine. According to the method, the BFMs phenomenon is relieved to a great extent, complex TSP problem solving is avoided, and efficient exploration incremental skeleton extraction is achieved with extremely low calculation consumption; the exploration behavior of the robot is effectively guided, invalid movement is remarkably reduced, information acquisition is maximized, and resource consumption is minimized.

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 invention relates to the technical field of reinforcement learning, and provides a multi-agent collaboration method and device based on priority experience playback. The method comprises the following steps: completing multi-round strategy training by updating a data set; wherein the strategy training of each round comprises the following steps: acquiring a data set through interaction between an intelligent agent and an environment; based on sample quality, constructing an optimal subspace according to the data set; key points are obtained through screening in the optimal subspace, and multi-agent training is conducted through the key points. According to the method, data sampling is performed based on sample quality, and optimal subspace and key point screening is performed, so that the problem of difficult exploration caused by sparse rewards in multi-agent reinforcement learning is relieved, and the exploration efficiency is improved.

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.

The present invention discloses a method for prospecting hot water jet sedimentary cobalt ore, comprising the following steps: Step 1: compile a hot water sedimentary rock stratum distribution map, and preliminarily determine a favorable mineralization area; Step 2: compile a cobalt element anomaly map according to the results of the water system sediment measurement, and compile a target area comprehensive prediction map in combination with the stratum distribution map, and optimize the target area using the results of the water system sediment measurement; Step 3: carry out a special investigation, establish a marker layer, and then use the induced polarization profile and / or geochemical profile to conduct a preliminary verification of the target area and delineate the ore body; Step 4: use drilling for deep verification, and use borehole electrical logging to determine the deep ore layer, and use the comparison of lithology and rock layer thickness between different boreholes to comprehensively analyze the change law of the stratum and ore-bearing layer, and carry out a comprehensive evaluation of the cobalt ore in the area. The present invention has the characteristics of quickly identifying the cobalt-containing stratum, providing an implementation basis for trenching and drilling deployment, and can effectively improve the exploration efficiency and shorten the exploration cycle.

The invention belongs to the technical field of uranium resource exploration, and particularly relates to a uranium-rich granite deep contact zone burial depth calculation method which comprises the following steps: step 1, carrying out water system deposition sampling in a granite development range of a research area; 2, the uranium element content of the sample is counted, and a uranium-rich granite development area is delineated; 3, gravity measurement is carried out in the delineated uranium-rich granite development area, and Bouguer gravity anomaly and elevation grid data are obtained; 4, solving residual gravity anomaly to form a residual gravity anomaly grid file; 5, broadband magnetotelluric profile measurement is carried out, and a boundary between the deep part of the rock mass and the stratum is calibrated through an inversion result; 6, digitizing each section boundary, and making a constraint grid file; 7, establishing a model to carry out interface inversion, and obtaining elevation grid data of the deep contact zone of the granite rock mass; and 8, calculating the burial depth of the deep contact zone of the uranium-rich granite. According to the method, the deep contact zone burial depth of the uranium-rich granite body can be accurately positioned and calculated.

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 provides an intelligent precise exploration system and method for the surrounding environment of pile foundation engineering drilling. The system comprises a main control system, a sensor unit, a positioning unit, a data processing unit and an analysis and decision unit. After a control center generates control instructions, an instruction sending and executing module of the master control system is responsible for sending the instructions to corresponding exploration equipment or executing mechanisms, the instructions comprise exploration depth adjustment, exploration direction change and exploration equipment starting or stopping, and the instruction sending and executing module needs to ensure accurate transmission and execution of the instructions. Therefore, accurate control over the exploration system is achieved, the operation state of the exploration system is monitored in real time, an alarm signal is sent out in time when abnormity or faults occur, potential problems can be found and processed in time, and safe and stable operation of the exploration system is ensured. According to the method, the accuracy and reliability of exploration data are improved, a scientific basis is provided for construction decision making, meanwhile, the construction risk and cost can be reduced, and the construction efficiency and quality are improved.

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.

The embodiment of the present application provides a method, device, electronic device and storage medium for evaluating the scale of porphyry copper deposits, and relates to the field of ore deposit scale exploration. Rock samples are collected from the area to be explored, and zircon monominerals are sorted out from the rock samples; elemental analysis is performed on the zircon monominerals and rock samples, and an element database is established; the element database is screened according to preset indicators to obtain target element data; the ore deposit scale index of the area to be explored is calculated based on the target element data, wherein the ore deposit scale index includes the V / Sc of the rock and the Eu / Eu* of the zircon; the porphyry copper deposit scale of the area to be explored is evaluated according to the ore deposit scale index, and representative ore-forming rock body data can be obtained by substituting the ore deposit scale index into the ore deposit scale index to more accurately evaluate the porphyry copper deposit mineralization potential and scale, thereby reducing the input cost, improving the exploration efficiency and reducing the exploration risk.

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.

The invention relates to the technical field of artificial intelligence, can be applied to the field of financial science and technology, and discloses a federated learning-based feature exploration method, device and equipment and a storage medium, and the method comprises the steps: obtaining suspicious samples and local model parameters uploaded by all participants; aggregating the plurality of local model parameters to obtain global model parameters; updating original parameters of a preset first global model into global model parameters; selecting a first global model using the global model parameters as a second global model; performing feature extraction on the suspicious sample to obtain a plurality of feature vectors, and obtaining a prediction result corresponding to each feature vector output by the second global model; and calculating a loss value between a prediction result corresponding to each feature vector and a real result, and exploring an optimal feature vector of the suspicious sample from the plurality of feature vectors based on a preset feature exploration mode and the loss value. The method is beneficial for improving the exploration efficiency of the optimal feature vector of the suspicious sample.

The present invention discloses a Rayleigh wave dispersion curve automatic extraction method based on an improved U-net neural network, including constructing a dispersion curve training set; constructing a dispersion curve test set; cutting the image data in the dispersion curve test set and the dispersion curve training set, and binarizing the theoretical dispersion curve in the dispersion curve training set; geometrically transforming the image data in the dispersion curve training set to obtain an expanded dispersion curve training set; constructing an improved U-net neural network model, and using the expanded dispersion curve training set for model training; and extracting multi-order Rayleigh wave dispersion curves from the dispersion curve test set using the trained improved U-net neural network model. The present invention makes full use of the image segmentation capability of deep learning, can quickly and accurately classify the dispersion energy map of field surface wave data, improves the efficiency of extracting Rayleigh wave dispersion curves, and further improves the efficiency of surface wave exploration.

The invention discloses an anisotropic elastic medium seismic exploration wave field separation method and device, and relates to the technical field of seismic exploration. Comprising the following steps: acquiring anisotropic elastic medium related model parameters in seismic exploration; according to a time domain VTI elastic medium seismic wave equation, the time domain VTI elastic medium seismic wave equation is converted into a wave number domain, and P wave and S wave polarization attributes are obtained by solving a Christoffel equation; constructing an anisotropic wave field separation operator according to the polarization attribute, and performing approximate processing on the anisotropic wave field separation operator by combining polynomial expansion and optimization strategies to obtain an approximate wave field separation operator; acting the approximate wave field separation operator on an original elastic wave field to obtain decoupled P wave and S wave data; and performing seismic exploration processing and interpretation based on the decoupled P wave and S wave data to obtain an exploration result. According to the method, the precision and the efficiency in an existing anisotropic elastic medium wave field separation method can be effectively balanced, and the precision and the efficiency are improved in the development of a seismic exploration technology.

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 utility model relates to the technical field of mine tunnel exploration equipment, in particular to an automatic exploration device for the interior of a mine tunnel, which comprises a cart, exploration reaming equipment and an adjusting mechanism, a push plate is fixedly arranged at one end of the top of the cart, the adjusting mechanism is fixedly arranged at the other end of the top of the cart, and the exploration reaming equipment is mounted in the adjusting mechanism. In the embodiment of the utility model, the cart is provided with the adjusting mechanism, the exploration reaming equipment is mounted on the mounting rack in the adjusting mechanism, and the rotating effect of the mounting rack can be realized by arranging the linear module, the limiting frame, the first mounting plate and the second mounting plate; after a first positioning part and a second positioning part are mounted on the protection plate and the push plate to be connected with a rotating mounting frame, the mounting frame and the exploration reaming equipment can be adjusted and switched between a vertical state and a horizontal state, so that the device adapts to exploration work of different mine tunnels through adjustment; and the efficiency of mine exploration is greatly improved.