155results about How to "Reduce collection costs" patented technology

The invention provides a multi-mode intelligent robot control method and device, and the method comprises the steps: obtaining a current mode instruction which at least comprises one of a video instruction, a text instruction, a picture instruction and an audio instruction; on the basis of a pre-trained multi-modal large model, according to the current modal instruction, obtaining post-collapse characterization; on the basis of a pre-trained robot imitation learning network, according to the representation after collapse and current environment observation, predicting an output target action; the multi-mode intelligent robot with the body is controlled to operate according to the target action; wherein the robot imitation learning network is obtained by performing training optimization according to a training sample set formed by single-modal data samples, corresponding environment observation data and real actions of the robot. According to the method, the post-collapse representation is obtained by pre-training the multi-modal large model, and the post-collapse representation is utilized to predict the target action, so that the requirement on multi-modal manual annotation data when a training robot simulates a learning network is avoided in the process, the effect of multi-modal reasoning can be achieved only according to single-modal data training, and the efficiency of multi-modal reasoning is improved. Not only is data collection and labeling cost reduced, but also the reasoning ability of the model is improved.

The invention discloses a physics-based visual tactile sensor multi-mode contact simulation system and method, and the system comprises an elastic body contact simulation unit which is used for carrying out the particle processing of an elastic body of a visual tactile sensor, and obtaining the three-dimensional coordinates of particles on the surface of an object through the contact simulation of the elastic body; the coordinate system conversion unit is used for calculating the coordinates of the mark points on the pixel coordinate system through the coordinate system conversion relation to obtain a mark point simulation image; the tactile image simulation unit is used for processing the three-dimensional coordinates of the particles to generate a tactile simulation image; and the tactile mark point joint simulation unit is used for combining the mark point simulation image and the tactile simulation image to obtain a tactile mark point joint simulation image. According to the method, simulation of the visual tactile sensor tactile image, the mark point image and the tactile mark point combined image in the motion states of pressing, sliding, rotating and the like can be efficiently completed.

The invention belongs to the technical field of mine transportation, and discloses a belt conveyor roller anomaly detection system based on optical fiber auscultation, which comprises a data acquisition unit, a preprocessing unit and a prediction model which are connected in sequence, and an anomaly detection unit and a linear layer which are connected with the prediction model. The data acquisition unit acquires audio data in the running process of the carrier roller, converts the audio data into vibration data and outputs the vibration data to the preprocessing unit, the preprocessing unit segments the input vibration data, the segmented vibration data are input into the trained prediction model, and the prediction model is used for predicting the carrier roller. And the prediction model outputs the characteristics of the potential space corresponding to the current vibration data to an anomaly detection unit, the distance between the characteristics of the current potential space and the characteristic center of the potential space when the carrier roller normally operates is calculated, and if the distance is larger than a set distance threshold value, the current operation of the corresponding section of carrier roller is abnormal. According to the method, automatic detection of the abnormity of the carrier roller is realized, the information contained in the potential spatial features output by the prediction model is richer, and the abnormity detection precision of the carrier roller is improved.

The embodiment of the invention provides an end-to-end automatic driving model training method and device, computer equipment and a medium, and relates to the technical field of automatic driving, and the method comprises the steps: obtaining the driving behavior data of a driver and the first self-vehicle planning data of an end-to-end model, and when the difference value of the two kinds of data exceeds a first preset threshold value, determining the driving behavior of the driver; triggering a data recording mechanism, obtaining preliminary screening data of a period of time before and after the current moment, obtaining second self-vehicle planning data based on the preliminary screening data and an end-to-end model, and carrying out comparative learning on the second self-vehicle planning data and the driving behavior data to obtain a similarity score; screening corresponding data fragments when the similarity score is lower than a threshold value in the primarily screened data, obtaining a virtual driving scene according to the data fragments, and inputting the virtual driving scene into the VLA model to obtain driving related data; and training an end-to-end model by taking the driving-related data as training data. According to the scheme, the quality of training data and the efficiency and precision of model training are improved.

The invention discloses a thromboembolism occurrence risk assessment method and assessment system. The method comprises the following steps: acquiring a blood vessel image map of a to-be-assessed patient; inputting the blood vessel image map of the patient to be assessed into a pre-trained risk assessment model; wherein the pre-training of the risk assessment model is based on a ResNet-50 model, and self-supervised contrast learning iteration is carried out by inputting positive and negative samples so as to update model parameters; extracting a feature embedding vector in the blood vessel image; according to the feature embedding vector, calculating probability distribution of embolism occurrence risk levels through a model classification head; and according to the probability distribution, selecting a risk level label and outputting the risk level label as an evaluation result. According to the method based on the time axis symmetry, the image data of the patient is collected before and after the embolism occurrence time point, the positive and negative sample pairs are generated, the problem that the embolism sample is difficult to obtain is solved, and meanwhile the sample collection cost is reduced through self-supervised learning.

The invention provides a satellite solar short-wave radiation inversion method based on knowledge distillation, which relates to the field of solar short-wave radiation inversion, and comprises the following steps: collecting polar orbit satellite data, stationary satellite data, geographic information data and ground observation data; preprocessing the data to obtain input features and labels for training the teacher model and the student model; sorting the importance of the input features by using a random forest model, selecting key features, and removing non-key features; constructing a deep learning model by using the key features, and generating a teacher model of solar short-wave radiation inversion; and taking an output result of the teacher model as a soft label, introducing ground observation data as a hard label, and carrying out knowledge distillation to obtain a student model of solar short-wave radiation inversion. According to the technical scheme of the invention, the problems of data scarcity and high data acquisition cost caused by the fact that a model is constructed only depending on ground observation data in the prior art are solved, and the problem that the inversion precision completely based on satellite data is limited is solved.

The invention discloses a crosslinked cable eccentricity adjusting process and a crosslinking production line thereof. The adjusting process comprises the following steps: S1, setting the number of cable extrusion layers; s2, setting a marking notch; s3, sampling the material and carrying out layer number sequence number calibration; s4, carrying out multi-layer co-extrusion and moving a crude product; s5, acquiring a thickness parameter set 1; s6, vulcanizing and cooling; s7, acquiring a thickness parameter set 2; s8, thickness solving is carried out, and xi is stored in a standard thickness parameter; s9, dynamically solving the thickness xi of each layer and performing time parameter derivation to obtain a trend derivative d1 and a trend derivative d2; and S10, judging a trend derivative d1 and a trend derivative d2, and carrying out feedback adjustment. By adopting the process, the thickness data of each layer can be accurately acquired, the deformation reason of the extrusion layer can be judged, the data acquisition mode is simpler, the acquisition cost is lower, the hysteresis of parameter adjustment can be effectively overcome, and the product yield is greatly improved.

The invention belongs to the field of big data processing, and discloses a crowd travel mode identification method and system for multi-view subspace learning based on dynamic and static feature fusion, and the method comprises the steps: carrying out the preprocessing of original signaling data, and cleaning invalid data; extracting a travel track of each user, and processing possible abnormal conditions of ping-pong data and drift data; after track data of a single user in a sample time period is obtained, segmenting the track data, and extracting a single trip chain track; extracting static characteristics such as travel behaviors and travel traffic of a travel chain in combination with multi-source data such as mobile phone signaling data, an OSM road network and public traffic data; extracting time sequence features by using an LSTM model, and modeling dynamic features of a travel track; static and dynamic features are fused, and travel modes are captured from multiple views. According to the method, the complex travel mode can be more accurately captured, data redundancy and noise are reduced, and the accuracy of travel mode identification is remarkably improved.

The invention belongs to the technical field of intelligent seismic exploration, and particularly relates to a sparse active source constrained non-ideal illumination passive source seismic wave field intelligent reconstruction method, so as to solve the problems of false events and coherent noise under the condition of non-ideal distribution of underground passive seismic sources. In order to improve the reconstruction effect of a passive source seismic wave field under a non-ideal illumination condition, the method builds an improved U-Net network, so that the U-Net network can intelligently extract data features and derive an attention mechanism along two independent dimensions of a channel and a space in the network, and multiplies the attention mechanism by an input feature map, so that the reconstruction effect of the passive source seismic wave field is improved. Self-adaptive feature refinement is carried out, so that the purpose of carrying out intelligent reconstruction on passive source data is achieved; by learning the trained deep learning network, the sparse active source seismic record constrains the passive source seismic data prediction network, passive source data reconstruction which shares a measuring line with the active source under the sparse active source condition is realized, and the multi-dimensional deconvolution reconstruction effect and the calculation stability are improved.

The invention discloses a multi-energy virtual power plant optimization scheduling method and system, belongs to the technical field of power optimization scheduling, and solves the problems that an existing single prediction model cannot meet the requirements of multi-energy virtual power plant optimization scheduling, a data set applied to prediction calculation is large, and the calculation cost is high. According to the method, the influence factor indexes of the output of the wind and photovoltaic units are screened by adopting the random forest algorithm, the output of the wind and photovoltaic units is predicted by using the artificial neural network algorithm, and the defects of a single algorithm are further made up through the hybrid prediction algorithm, so that the prediction accuracy is improved; a Monte Carlo method is adopted for simulation analysis for load prediction of the electric vehicle, a function with the minimum operation cost of the virtual power plant as an objective is established, an optimal scheduling model of the multi-energy virtual power plant is established, and the problem that prediction and optimization can only be performed for single distributed energy in the prior art is solved.

The invention discloses a trusted system for realizing digital asset right confirmation through an agricultural block chain technology, and belongs to the technical field of computer programs. User data verification, data storage, data right confirmation, data cleaning (redundancy), data comparison and data classification operations are realized in a distributed bookkeeping mode, data storage in a data lake is realized, the stored data is subjected to ownership certification, data distribution of digital asset right confirmation is carried out on the data (group) subjected to right confirmation certification, and data distribution is carried out on the data (group) subjected to right confirmation certification. A verification algorithm proves ownership, use right and management right of data assets, ownership confirmation of the data is achieved, traceable trace information is generated, and records generated in the test process comprise the whole process node of user operation. The core algorithm adopts the most advanced computer algorithm model to build the whole system in the aspects of safety, data intelligent classification, data cleaning and data right confirmation, and it is ensured that the trust of a user to the system in the using process reaches 100%.

The invention discloses zero-carbon concrete based on shells and a mix proportion design method thereof, and belongs to the technical field of green building materials, the mix proportion design method of the zero-carbon concrete based on the shells comprises the following steps: calculating and trying to determine the mix proportion of common concrete with a certain strength grade as a reference mix proportion; based on a life cycle analysis method, determining a reference concrete carbon emission list database, and constructing a carbon footprint accounting model; calculating and determining the shell replacement amount according to the principle that the life cycle carbon footprint is zero; and correcting the actual use amount of the replaced sandstone aggregate so as to determine the mix proportion and the strength grade of the zero-carbon concrete. The method has the advantages of being simple, objective in quantification, low in cost, easy to popularize and the like, fills the blank of the method for designing and preparing the zero-carbon building material, and is beneficial for promoting resource utilization of the shellfish waste.

The utility model discloses a papermaking wastewater multistage filtering device which comprises a filtering box, a conveying cylinder is transversely arranged in the filtering box, one end of the conveying cylinder penetrates out of the filtering box and is provided with an opening communicated with a filter pressing box, a conveying device is arranged in the conveying cylinder, the outer side of the conveying cylinder is sleeved with a filtering part, and a slag inlet is formed in the top of the conveying cylinder; the filtering part comprises two lantern rings arranged on the outer side of the conveying cylinder in a sleeving mode, a plurality of fishing plates are evenly arranged between the two lantern rings and arranged along the radius of the section of the filtering box, each fishing plate is provided with a scraping assembly, one lantern ring is sleeved with a second gear, the second gear is meshed with the first gear, and the second gear is meshed with the second gear. The center of the first gear is connected with the output end of the first motor penetrating the side wall of the filter box. According to the utility model, by arranging the rotatable filtering part, suspended matters in the papermaking wastewater are fished and transferred into the filter pressing box for filter pressing, so that the moisture of the filtered suspended matters is further reduced, and the subsequent collection and transportation cost of the filtered suspended matters is reduced.

The invention relates to the technical field of sensor detection, and discloses a deep learning-based nitrogen-oxygen sensor aging test method, which comprises the following steps: extracting universal aging trend data from preprocessed historical large sample data; training the self-attention mechanism model by using the universal aging trend data to obtain a universal basic model; taking the trained general basic model as a transfer learning model, and performing model fine adjustment on the transfer learning model; adding a new full-connection layer, connecting the output of the fine-tuned transfer learning model to the new full-connection layer, activating the output of the new full-connection layer, and training the fine-tuned transfer learning model by using special aging trend data by taking minimization of a predefined loss function as a target to obtain a special detection model; performing an aging test on the to-be-detected nitrogen-oxygen sensor by using the special detection model; according to the invention, the accuracy and robustness of aging detection of the nitrogen-oxygen sensor can be improved.

The invention provides a configuration file processing method and device for an acquisition agent, relates to the technical field of data acquisition, is applied to a cloud native scene, and comprises the following steps: constructing a configuration center for managing a server configuration file corresponding to the cloud native scene through an open source configuration center component Nacos; when a proxy server corresponding to the acquisition proxy is started, establishing a monitoring link with the configuration center, and pulling an initial configuration file required by the client from the configuration center; establishing a link between the proxy server and a log acquisition server to acquire initialization configuration data corresponding to the initialization configuration file; and when the server configuration file is updated, the configuration center returns an updated target configuration file required by the client to the proxy server through the monitoring link so as to collect target configuration data corresponding to the target configuration file. Therefore, a new configuration center is developed based on Nacos collection, the configuration file of the collection agent in the industry is managed, the function that the collection agent should have in the cloud native scene is achieved, and the data collection cost is saved.

The invention provides a reading assistant processing method and device, an all-in-one machine and a storage medium, and relates to the technical field of intelligent reading, and the method does not depend on a preset script, and allows a user to initiate a conversation at any time in any reading stage, so that more natural and smooth user interaction experience is realized. Moreover, the general large language model adopted in the method does not need to depend on sample data in a specific field for pre-training, so that the collection cost of the sample data and the model training cost through the sample data can be reduced, and meanwhile, the limitation that a traditional model depends on the data in the specific field is broken through; and a more flexible and wide application scene is realized. Besides, the corresponding reading prompt information is determined by determining the demand information of the user, the prompt engineering technology is effectively used for stimulating and guiding the general large language model to generate more accurate and targeted reply content to be returned to the user, and the method has higher universality and adaptability.

This invention provides a method for modeling a cylinder motion system based on a physical information neural network, comprising: acquiring time-series data of the cylinder motion system, including time-series control quantities and system state quantities; constructing a physical information neural network model with a dynamic feature input layer and a physical parameter input layer, receiving the time-series control quantities and physical parameter vectors respectively; performing a nonlinear transformation on the physical parameter vectors through a physical parameter encoding network to obtain encoded physical feature vectors; fusing the time-series control quantities and physical feature vectors to form a joint input vector; inputting the joint input vector into the backbone neural network, synchronously predicting the system state quantities through multi-layer nonlinear transformations; constructing a composite loss function including a data loss term and a physical loss term; training the model using time-series data, updating the model parameters by minimizing the composite loss function until the model converges, thereby achieving high-precision modeling using only single motion data.

The application provides a vehicle operation data collection method and system, computer equipment and medium, and belongs to the field of vehicle data processing. The method comprises the following steps: obtaining vehicle model information and user demand of a vehicle to be collected; determining data analysis service project content options based on a data analysis service project content information table and vehicle model information corresponding information table, and screening data analysis service project content according to the user demand of the vehicle to be collected; generating a natural language text for the data analysis service project content to obtain a vehicle data collection strategy; collecting environmental data and judging based on data collection conditions; when the environmental data falls within the range of the data collection conditions, collecting operation data of the vehicle to be collected according to the data collection project, and obtaining operation data of the vehicle to be collected. The method can dynamically collect data based on the actual operation state, driving environment and user demand of the vehicle, and ensure the effectiveness of operation data collection.

The invention discloses an urban pipeline simulation data construction method based on intelligent remote sensing interpretation, and relates to the technical field of remote sensing image intelligent interpretation and urban geographic information basic data rapid generation. Comprising the following steps: acquiring an urban original ortho-image; carrying out preprocessing on the original orthoimage; marking the preprocessed orthoimage according to the road contour by using an image marking tool, and prefabricating a sample training set and a verification set; optimization is carried out based on coding and decoding structures in the U-Net original model, an urban pipeline simulation data construction model is established, and training of the model is completed; road line extraction is carried out by using the trained model; segmenting the road line; and assigning attributes to the segmented points and lines to complete generation of pipeline data. According to the method, the road of the remote sensing image is intelligently and automatically extracted, and the pipeline simulation data is constructed based on the extracted road data, so that the rapid generation of the urban pipe network data is realized, and the pipeline simulation data is provided under a specific condition.

The invention belongs to the technical field of new energy power generation and grid connection, discloses a grid-side converter fault ride-through control structure modeling method, system and equipment and a medium, and aims to solve the problems of difficult modeling, high cost, poor extrapolation and lack of universality caused by dependence on manufacturer internal logic or massive experimental data in the prior art. According to the method, disturbance input and current output data under a limited working condition are acquired, a dimension raising mapping function is constructed to map the input to a high-dimensional feature space, a Koopman operator is solved based on ridge regression to establish a global linear mapping model, active and reactive current reference components are predicted, and the current reference components are optimized. And finally, inputting the current inner loop time domain model to generate a current transient response curve during the fault period. According to the method, high-precision modeling and prediction can be realized only by a small number of samples, and the method has good extrapolation and universality and can be adapted to various fault ride-through strategies.

The invention relates to the technical field of image recognition, and discloses a wind power fan blade defect recognition method and system based on image recognition, and the method comprises the steps: carrying out the blocking cutting, illumination normalization and image enhancement processing of an original image, constructing a defect-free sample and a defect sample, and dividing the samples into a training set and a test set; the method comprises the following steps: taking a GANopen network as a basic framework, fusing the lightweight design of Mamba-YOLO, constructing a joint loss function by adversarial loss, reconstruction loss and coding loss based on a training set, carrying out unsupervised training, optimizing network parameters until convergence, and obtaining a defect identification model; inputting the preprocessed to-be-detected fan blade image into the trained defect recognition model, performing defect recognition and positioning, and outputting the defect type and position; based on an output result of the defect identification model, dynamically adjusting an early warning level and response measures through a self-adaptive early warning mechanism; according to the invention, the efficiency and accuracy of wind power fan blade defect identification are improved.

This application discloses a method for training a recognition model, a recognition method, a computer device, and a computer-readable storage medium. The training method includes the following steps: controlling a single-photon lidar to emit photon pulses towards a target scene and receiving one-dimensional single-photon echoes reflected from the target scene, where the target scene includes at least one object to be recognized; constructing a training sample set based on the one-dimensional single-photon echoes; constructing an initial model and inputting the training sample set into the initial model for iterative training; validating the initial model after each iteration, marking the validated initial model as the recognition model, and outputting it. The application method involves inputting the echo signal to be recognized into the trained recognition model, and obtaining the recognition result based on the processing of the recognition model. The recognition result includes the pose and type of the object in the target scene. Therefore, this application can achieve high-precision recognition of long-distance targets.

The utility model provides a greenhouse lighting supplementary lighting device which comprises a power supply module, a lighting module and a temperature and humidity acquisition module, the lighting module and the temperature and humidity acquisition module share a power supply provided by the power supply module, and the temperature and humidity acquisition module comprises a temperature and humidity acquisition unit, a main control unit and a remote communication unit which are in communication connection. Illumination light supplement and temperature and humidity collection are integrated, a light supplement area is consistent with an area where crops are located, the corresponding temperature and humidity collection point positions are high in consistency with the environment where the crops are located, the effectiveness of collected temperature and humidity data can be effectively improved, the collected temperature and humidity data can be sent to a mobile phone and other remote terminals through the remote communication unit, and the remote communication unit is convenient to use. And checking is convenient. According to the greenhouse lighting and light supplementing device, temperature and humidity collection and lighting and light supplementing are integrated, the temperature and humidity collection cost can be reduced, meanwhile, the temperature and humidity data collection and checking efficiency is improved, and convenience is provided for accurate control over the environment in a greenhouse.

The present invention belongs to the technical field of earthquake prediction, and specifically relates to a laboratory earthquake prediction method across multiple experimental devices based on transfer learning. First, a large model of a deep convolutional neural network is trained on a laboratory test data set to capture the key features of earthquake signals; then, using fine-tuning technology, the model is further adjusted to adapt to another laboratory test data set of a different category, achieving effective knowledge transfer and model generalization. The present invention is used to achieve efficient migration and fusion between laboratory test data of different types and conditions, so that the earthquake prediction knowledge obtained in one laboratory can be transferred to another laboratory earthquake test for prediction.

A method for evaluating and sampling the value of a dataset is disclosed, which aims to reasonably assess the value of the dataset and guide the sampling of high-value datasets. The method first establishes an evaluation model for assessing the value of individual data points and a function to describe the degree of value redundancy among data points. Then, it constructs a value evaluation model for the dataset by comprehensively considering the value of individual data points and the degree of value redundancy among them. Based on this data set value evaluation model, high-value datasets can be sampled from the data sampling space according to user needs. The main application of this invention is to evaluate the value of datasets and sample high-value datasets. It can guide data sampling in data-driven analysis, modeling, and decision-making tasks, thereby improving the quality of datasets and effectively reducing data acquisition costs while ensuring the effectiveness of the target task.

The embodiment of the present invention discloses a method, device and electronic device for acquiring speech training data and training a model, including: inputting speech data in a speech data storage device to a target device in the form of an electrical signal; processing the speech data through a channel; and acquiring the through-channel speech data after the through-channel processing. The embodiment of the present invention can reduce the acquisition cost of speech training data, improve the quality and versatility of speech training data, and thus improve the training efficiency and recognition effect of a speech recognition model.

The present invention discloses a method and device for restoring three-dimensional posture information. In the above method, a first frontal image of a current human body facing a camera device is obtained; multiple joints in the first frontal image are identified, and the length information of the rigid bones corresponding to each of the multiple joints in the first frontal image is determined; a second frontal image of the current human body facing the camera device after changing its posture is obtained, wherein the current human body changes its posture while keeping the hip joint line parallel to the imaging plane of the camera device, and the distance between the current human body and the imaging plane of the image remains unchanged; multiple joints in the second frontal image are identified, and the projection coordinate position information of the rigid bones corresponding to each of the multiple joints on the imaging plane of the image is determined; and the posture information of the current human body in the third dimension orthogonal to the imaging plane is restored. The acquisition cost of model samples and the computing power requirements for training models are reduced, and the amount of calculation is greatly reduced.

The invention discloses a method for detecting abnormal electricity consumption of an intelligent electric meter, which relates to the technical field of intelligent meter reading, and comprises the following steps of: 1, data acquisition and preprocessing, 2, data compression and dimension reduction, 3, lightweight data aggregation, 4, transfer learning and fine tuning, 5, self-supervised learning, and 6, model lightweight. According to the method, a lightweight aggregation algorithm is used, that is, data aggregation is carried out through a local weighted average method instead of a complex distributed data aggregation model, so that the calculation complexity is reduced, the aggregation efficiency is improved, and the dependence on hardware resources is reduced; before data aggregation, a data compression technology is used to carry out dimension reduction on power consumption data, so that the data volume is reduced, the aggregation efficiency is improved, and the information loss is reduced; transfer learning and fine tuning are adopted, a BERT pre-training model is utilized to accelerate training of the deep belief network, training data requirements are reduced, and model training efficiency is improved.

The application provides a microseismic real-time monitoring method and device based on compressed sensing and 5G node collection, solves the problem that microseismic monitoring data is easy to be submerged in noise interference, and microseismic data collection equipment cannot transmit data in real time, or the transmitted data is uneven and incomplete. It comprises the following steps: collecting data, establishing a geological and geophysical model, simulating fracturing based on the parameters of the well to be fractured to demonstrate the parameters of the microseismic monitoring observation system; establishing an adaptive optimal learning dictionary to form a compressed sensing collection scheme; conducting a due diligence investigation of field obstacles and interference sources to form a compressed sensing active obstacle avoidance scheme; using a 5G node collection system to collect field data; applying compressed sensing data reconstruction technology to reconstruct the collected field data to form three-dimensional microseismic monitoring data; carrying out microseismic data processing and interpretation, and feeding back the microseismic analysis results to the fracturing site to realize real-time monitoring of the fracturing construction process.

The invention provides a high-precision geochemical data generation method and device based on a remote sensing image, and the method can achieve the fusion of small-scale geochemical data and hyperspectral remote sensing image data in a whole region through a trained geochemical data generation model. Therefore, large-scale geochemical data acquisition work does not need to be carried out on the whole area range, and large-scale geochemical data capable of covering the whole area range can be generated and obtained, so that the data acquisition cost is reduced and the data acquisition efficiency is improved; and the geochemical survey personnel can simultaneously obtain the geochemical data of a plurality of corresponding different scales in the whole area range, so that the higher-precision work task requirements of the geochemical survey personnel can be met.