978 results about "Remote area" patented technology

The invention discloses a photovoltaic panel defect detection method, system and device and a medium, and relates to the technical field of photovoltaic defect detection.The method comprises the steps that an infrared image and a visible light image of a photovoltaic module are obtained; visual features are extracted and fused; marking defect areas in the fused image data set, storing marking information of the defect areas, and constructing a training data set; inputting the training data set into a CLIP model for processing, and generating embedded representation; adopting the embedded expression to initialize the layer weight of the YOLOv11 model, and performing fine adjustment on the YOLOv11 model; inputting image data of a to-be-detected photovoltaic module into the trained photovoltaic defect detection model, and detecting to obtain a defective area in the image data; according to the method, the problem that a large amount of data for photovoltaic detection cannot be obtained in a remote area, so that model training is difficult is solved.

The invention is suitable for the technical field of database management, and provides a distributed database management method and system based on artificial intelligence. The system comprises a database content analysis module, a storage layout adjustment module, an anomaly detection module, an unmanned aerial vehicle storage communication module, an access request guide module and an unmanned aerial vehicle node recovery module. The scheme that an unmanned aerial vehicle is adopted as a storage and communication node brings remarkable innovation and multiple advantages to a distributed database management method. Firstly, the mobility of the unmanned aerial vehicle enables the unmanned aerial vehicle to quickly take over a data storage task when a network is interrupted or a node fails, so that the integrity and availability of data are ensured, and the response capability in a traditional architecture is relatively limited. In complex scenes such as disaster recovery or remote area deployment, the unmanned aerial vehicle can break through network limitation through off-line transmission, and data safety and storage continuity are effectively guaranteed.

The invention discloses a method and equipment for constructing a large-scale lake water body salinity estimation model. Lake form data, lake geographical attribute data and hydrological characteristic data, climate characteristic data, land utilization ratio and soil component ratio in a basin where a lake is located are used as input characteristic variables, and actually measured lake salinity data are used as output; utilizing a machine learning XGBoost algorithm to construct a lake water body salinity estimation model and carrying out model training; models trained based on different training sets are constructed through random experiments, and the model with the optimal precision is selected as a lake water body salinity estimation model for unknown lake water body salinity estimation. According to the method disclosed by the invention, the unknown lake water salinity is estimated only by using the on-site measurement of the lake water salinity and the public acquisition of the related physical characteristic variables generated by the data source, so that the method has relatively high model precision; and the time and the economic cost for monitoring the water salinity of the lake which cannot be actually measured and investigated in an unknown remote area are greatly reduced.

The invention discloses an intelligent scheduling method for video equipment based on solar power supply, and relates to the technical field of energy management and video. Through multi-dimensional data acquisition, intelligent prediction and dynamic scheduling technologies, the energy management challenge faced by solar power supply video equipment in remote areas is effectively solved, the power generation amount, equipment power consumption and environmental change data are acquired in real time by using an Internet of Things sensor, and the energy management efficiency is improved by combining time sequence analysis and a machine learning algorithm. The power generation amount and power consumption fluctuation are accurately predicted, an energy supply and demand matching model is established based on a dynamic planning algorithm, a power distribution strategy is optimized, operation of high-priority equipment is guaranteed preferentially when the power generation amount is insufficient, meanwhile, the working mode of the equipment is dynamically adjusted through a reinforcement learning algorithm, and energy consumption optimization and self-adaptive matching are achieved. The energy utilization efficiency and the system stability are improved, the adaptive capacity to dynamic environment changes is enhanced, and continuous and stable operation of video equipment under complex conditions is ensured.

The invention relates to the technical field of unmanned aerial vehicle geological survey, and discloses a path planning method and system for unmanned aerial vehicle geological survey, and the method comprises the steps: obtaining the image, spectrum and topographic data of a remote mountainous area through a multispectral sensor and a laser radar, and recognizing potential vein abnormal points through data fusion and geological feature analysis; and constructing a geological interestingness weight map and planning a flight path. Dense sampling flights are performed in a high interestingness region, high resolution data are processed in real-time to identify key reconnaissance targets, a weight map is updated, and a flight trajectory is dynamically adjusted. Through the closed-loop self-adaptive exploration process, accurate positioning and feature description of the hidden vein are realized, the mineral exploration efficiency is improved, the labor cost and the safety risk are reduced, and an innovative solution is provided for mineral resource exploration in remote areas.

The invention discloses a high-speed internet access system and method suitable for a low-orbit satellite, and relates to the field of satellite internet communication. The system comprises a low-orbit satellite group, a ground receiving station, user terminal equipment, a central control center and the like. The low earth orbit satellite group forms a dense satellite network on a near earth orbit so as to realize seamless network coverage at any corner of the earth. The ground receiving station supports simultaneous establishment of connection with a plurality of satellites, and ensures stability and high speed of data transmission. The user terminal equipment enables a user to directly communicate with the nearest low-orbit satellite, and achieves the quick access to the Internet. The central control center adopts a dynamic resource allocation algorithm to dynamically adjust spectrum resources and channel allocation according to real-time communication requirements and the state of the satellite network, and network performance is optimized. The invention not only provides an unprecedented network access solution for special environments such as remote areas and offshore platforms, but also has great significance for improving the popularity rate and quality of global internet access.

The invention discloses a transformer oil chromatographic analysis method based on an improved fuzzy clustering algorithm and an LSTM (Long Short Term Memory) algorithm. The method comprises the following steps: acquiring sample data; s2, randomly dividing the sample data into a plurality of cluster sets; s3, obtaining an initial clustering center of the clustering cluster set; s4, acquiring a comprehensive distance between the initial clustering center of each clustering cluster set and the sample data about the Euclidean distance and the cosine distance; s5, the membership matrix is updated according to the comprehensive distance, the steps S3 to S4 are repeatedly and iteratively executed until the iteration convergence condition is met, and the final clustering center of the clustering set is obtained; and according to the final clustering center, determining a clustering result of the transformer fault type. S6, dividing the clustering result into a data training set and a data test set according to a preset proportion; s7, establishing an LSTM model and taking the LSTM model as a prediction model for transformer fault analysis; and obtaining an optimal prediction model based on the data training set and the data verification set. The problems that a large amount of manpower and financial resources are consumed in a traditional regular overhaul mode, particularly in remote areas with inconvenient traffic, overhaul work faces many challenges, and in addition, human errors possibly occur in the overhaul process, and equipment is damaged are solved.

The invention discloses a reservoir capacity curve rechecking method based on satellite altimetry and remote sensing imaging technology, which belongs to the field of water conservancy and hydropower engineering and hydrology and water resource management, and comprises the following steps of: inverting and correcting the water level elevation of a reservoir by adopting altimetry data monitored by satellite remote sensing; recognizing a reservoir water body range based on a remote sensing image, and counting an area sequence; constructing a reservoir capacity calculation model based on the synchronous remote sensing water level and area sequence of the reservoir; remote sensing water level-area and water level-storage capacity curves are fitted, the precision is evaluated, and the process that the same satellite task is adopted to invert the same-period water level and area to reconstruct the storage capacity curve is achieved. By adopting the method, the defect that the water level and the area of the reservoir characteristic curve reconstructed by using satellite remote sensing data in the prior art are not synchronous can be avoided, the defects that the traditional height measurement satellite based on laser or synthetic aperture radar can only obtain the sub-satellite point cloud elevation and the step length is relatively long are overcome, and the precision of the reconstructed characteristic curve can be remarkably improved; the method has remarkable advantages in reconstructing characteristic curves of small reservoirs in remote areas and large ranges.

The invention discloses a fire spreading prediction method based on a time-space point process, and the method comprises the steps: extracting multi-dimensional features through observation of each time point, and enabling the features to be embedded into a ConvLSTM network, and forming time-space sequence data of fire spreading; local spatio-temporal features are extracted through a ConvLSTM network, and local dependency capture is achieved; a self-attention mechanism is introduced, the dependency relationship between grids is calculated, fire behavior interaction in a remote area is carried out, and remote dependency capture is achieved; and after processing of the ConvLSTM network and the self-attention mechanism, outputting a prediction result of the fire behavior at the future moment. According to the method, the local fire dynamic state is captured through the convolutional long-short-term memory network, and the remote space-time correlation is processed through the self-attention mechanism, so that the prediction precision of fire spreading is improved; the problems that in the prior art, local and global space-time dependence capture is insufficient, the model generalization ability is poor, the calculation cost is high, and the real-time performance is poor can be effectively solved.

The invention relates to the technical field of new energy, and discloses an independent energy supply system suitable for a high-altitude area and a control method thereof, electric power is generated through photovoltaic power generation, part of the electric power is stored in a storage battery, and the residual electric power is used for electrolyzing water to prepare hydrogen and oxygen, and the hydrogen and the oxygen are respectively stored. The oxygen content in the air is increased by mixing the oxygen, the oxygen and the hydrogen jointly enter the fuel cell for chemical reaction, and stable electric power and hot water are output. In order to ensure the stability of the system, a low-temperature air source heat pump is configured to serve as standby heat supply equipment, and accurate adjustment and storage of electric power are achieved through cooperative work of a load tracking module and an efficient storage battery. According to the system, photovoltaic power generation, hydrogen energy storage and fuel cell technologies are combined, continuous and reliable power and heat combined supply can be achieved in high-altitude areas lack of power grid support, and an efficient and environment-friendly solution is provided for energy supply in remote areas.

The invention belongs to the technical field of lake remote sensing monitoring, discloses a lake water level and water reserve dynamic monitoring method based on satellite-borne active and passive remote sensing information fusion, and effectively solves the problem that lake information in remote areas is difficult to efficiently and accurately obtain. Comprising the following steps: S1, preprocessing laser data; s2, preprocessing the multispectral data; s3, taking the ICESat-2 data as a core, combining with a multispectral image covering a corresponding region, and constructing a lake bottom DEM inversion data set based on neighborhood features; s4, constructing and training an elevation inversion convolutional neural network model; s5, predicting and generating a lake bottom DEM by using the trained elevation inversion convolutional neural network model; and S6, calculating the absolute water volume of the lake according to the DEM at the bottom of the lake, and carrying out lake water level-water reserve monitoring. According to the invention, long-time-sequence hydrological observation of remote region lakes can be effectively realized with low cost.

The invention discloses a wireless ad hoc network and Beidou satellite short message converged communication method, which comprises the following steps: acquiring a voice service signal of a wireless ad hoc network, converting the voice service signal into text data, and generating a Beidou satellite short message service according to the text data; and acquiring a short message service signal of the Beidou satellite, converting the short message service signal into a voice signal, and generating a voice service of the wireless ad hoc network according to the voice signal. The invention further comprises a device for implementing the method. According to the application, the problem of realizing interaction and intercommunication between the voice service of the wireless ad hoc network and the text service of the Beidou satellite short message is solved, so that remote communication is realized by adopting the integrated Beidou satellite short message communication module when the island wireless ad hoc network base station in a remote area cannot realize communication with a rear command center.

The invention provides a plasmodium real-time detection method based on a lightweight neural network model. The plasmodium real-time detection method comprises the steps of obtaining a blood smear image data set and performing preprocessing; establishing a lightweight plasmodium detection model based on a deep learning algorithm; inputting the preprocessed blood smear image data set into a plasmodium detection model for iterative training; performing plasmodium detection on a to-be-detected blood smear image by using the trained plasmodium detection model; according to the method, a lightweight detection model is constructed based on a deep learning algorithm, so that high-precision plasmodium detection is realized, and the real-time performance of detection is effectively improved; meanwhile, the lightweight model design can adapt to a low-resource environment, so that the model can efficiently run on low-power-consumption equipment, and automatic real-time malaria detection requirements in remote areas and low-resource areas are met; in addition, various forms and life cycle stages of the plasmodium can be accurately identified, and the detection comprehensiveness is improved.

The invention discloses an unmanned aerial vehicle control system based on Beidou short message communication, and aims to solve the communication problem of an unmanned aerial vehicle in a remote area or an environment with unstable network signals. By integrating the Beidou satellite short message communication module, the data compression algorithm and the message confirmation mechanism, the unmanned aerial vehicle can still keep a stable and efficient communication link with the ground station under the condition that a conventional communication network fails, and the method is particularly suitable for the unmanned aerial vehicle to execute tasks such as disaster rescue and environment monitoring. The invention provides a multi-mode communication mechanism which comprises a conventional mode, an emergency mode and a mixed mode so as to cope with different task requirements and environment changes.

The invention relates to the technical field of farm irrigation, in particular to a multi-source fusion and V2X networked vehicle high-precision positioning system and method, and the system comprises a vehicle-mounted terminal which integrates the following modules: a multi-source sensor group which comprises a laser radar (LiDAR), a binocular vision camera (BVC), a global positioning system (GPS), an inertial measurement unit (IMU) and a wheel speed sensor; the V2X communication module supports vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication and is used for receiving positioning data of an adjacent vehicle and roadside equipment; the system effectively solves the problems that in the prior art, a traditional RTK technology depends on a ground base station, a positioning blind area exists in a signal shielding area, data conflicts are easily caused by independent work of multiple sensors, RTK needs to be covered and supported by dense base stations, and the positioning accuracy is poor. And high-precision positioning cannot be realized in signal shielding scenes such as remote areas, tunnels and mountainous areas, so that an automatic driving function fails.

The invention discloses an operation method and system for distributed organic solid waste centralized treatment and quality improvement and application, and relates to the field of chemical engineering, the operation method comprises the following steps: respectively carrying out pyrolysis treatment on organic solid waste distributed in different areas to obtain combustible gas and solid combustible materials and / or pyrolysis oil, using the combustible gas as fuel for pyrolysis treatment, and using the solid combustible materials and / or pyrolysis oil for pyrolysis treatment; the solid combustible comprises carbon powder and / or pyrolysis residues; concentrating and gasifying the solid combustibles and / or pyrolytic oil obtained in different areas to obtain mixed gas, dedusting the mixed gas, and converting the dedusted mixed gas by using water vapor to obtain a hydrogen crude product; purifying the hydrogen crude product to obtain a hydrogen product; packaging, storing or conveying the hydrogen product; the method solves the problem that large-scale industrial treatment and economic benefit improvement are difficult to realize due to the fact that the organic solid waste in a distributed area or a remote area has the characteristics of multiple types, small quantity, dispersive distribution, high transportation, recovery and treatment cost and the like.

The invention provides a low-orbit satellite communication method based on photovoltaic power generation equipment and related equipment, and the method comprises the steps: carrying out the light energy collection in the operation process of the photovoltaic power generation equipment, and carrying out the low-orbit satellite signal detection through satellite receiving equipment; when satellite signals are detected, operation parameters of photovoltaic power generation equipment are dynamically adjusted according to electric energy data collected by light energy and low-orbit satellite signals, a low-orbit satellite corresponding to the satellite signals is locked based on the operation parameters, and the operation parameters comprise the direction and angle of the photovoltaic power generation equipment and a dynamic power supply strategy of the photovoltaic power generation equipment; and controlling a satellite receiving device to receive and decode low-orbit satellite signals according to the dynamic power supply strategy. When the photovoltaic power generation equipment collects light energy, detection and stable communication of low-orbit satellite signals are achieved through the integrated satellite receiving equipment, flexible and efficient communication support is provided for remote areas, and meanwhile operation parameters of the photovoltaic equipment are dynamically adjusted to optimize the synergistic effect of power supply and communication.

The invention relates to the technical field of micro-fluidic chips and biosensors, in particular to a paper folding type micro-fluidic paper chip analysis device for detecting ATP (adenosine triphosphate) based on a difunctional DNA tetrahedron and application of the paper folding type micro-fluidic paper chip analysis device. The analysis device comprises a detection layer and a washing layer, and is characterized in that a difunctional DNA tetrahedron system containing detection ATP is added into a reaction zone; wherein the bifunctional DNA tetrahedron comprises a capture DNA tetrahedron (B-TDN) and a detection DNA tetrahedron (D-TDN), and a single-stranded DNA sequence extending from any vertex of the tetrahedron formed by the B-TDN and a single-stranded DNA sequence extending from any vertex of the tetrahedron formed by the D-TDN can be complementary. The paper folding type micro-fluidic paper chip analysis device based on the difunctional DNA tetrahedron has the advantages of being small in size, low in scientific research cost, biodegradable, high in sensitivity, good in selectivity and the like, a new method is provided for rapid ATP detection in the medical field, and the paper folding type micro-fluidic paper chip analysis device is suitable for remote areas and economic outdated areas.

The invention relates to the technical field of power generation management, and particularly discloses an optimal management system for distributed photovoltaic power generation, which is used for solving the problem that the demand fluctuation of an existing water pump and an illumination load is large, such as the water pumping demand in an irrigation period and the suddenly increased illumination demand at night. A dispatching system with insufficient dynamics cannot flexibly deal with the load fluctuation, and the problem of insufficient power supply or excessive waste is easily caused; according to the invention, through real-time prediction and scheduling of power demands of pumping and lighting loads, efficient utilization of electric energy is realized based on dynamic energy management and multi-energy supply and demand interaction, consumption of an energy storage battery can be reduced, the service life of equipment is prolonged, and power supply stability of the system is maintained under the condition of load fluctuation. The economical efficiency, the stability and the dynamic response capability of the system are improved, and the method is particularly suitable for remote areas or areas with insufficient power grid coverage.

The invention relates to the technical field of environment detection, and discloses an extensible underground water and soil in-situ online observation system, which comprises an underground water and soil automatic online monitoring management platform, an underground water control module, a soil control module and an auxiliary module, the underground water and soil automatic on-line monitoring management platform can check the state of the detection module and the detected data. The problems that when an existing underground water and soil on-line monitoring device monitors underground water and soil through an on-line sensor, the sensor may have drifting or errors in a complex environment, and the data accuracy is affected are solved; in remote areas and complex terrains, data transmission interruption can be caused by network instability; the problems that underground water and soil equipment are managed separately, data output is not synchronous, the data analysis difficulty is increased, and complex data analysis and modeling are difficult to achieve are solved.

The invention belongs to the technical field of hydraulic engineering, and provides a river reach form recognition method and system, a storage medium and electronic equipment, and the method comprises the steps: collecting and processing remote sensing image data of a river channel in a research region; extracting a waterline and a center line from the remote sensing image data, and determining a maximum waterline, a minimum waterline, an average waterline and a center line corresponding to the minimum waterline; and calculating a moving distance proportion value of the waterline based on the maximum waterline, the minimum waterline, the average waterline and the center line corresponding to the minimum waterline, and identifying the river reach form based on the moving distance proportion value. The remote sensing image of a remote area or an area not easy to enter is obtained through the remote sensing technology, the change characteristics of the river reach form are captured under the non-contact condition, large-scale and continuous monitoring is achieved, data support is provided for multilevel analysis of the river reach, and reliable technical support is provided for flood control and disaster reduction, ecological protection and drainage basin management.

A system for asset inventory and resupply for supplying individuals in remote locations is disclosed. The individuals may be people and / or machines. The system and method involve providing multiple individuals in the field with sensors that automatically collect data on the expenditure of their supplies. In some cases, wearable sensors are provided to people in the field. The data is sent to a central depository which tracks their inventory of supplies and anticipates needed supplies, compiles and packages additional supplies so that the additional supplies can be delivered to the individuals in the field. A precision supply delivery system is provided for delivering the supplies.

The invention discloses a remote area video monitoring automatic anomaly detection method based on deep learning. The method comprises the following steps: S1, collecting remote area video data and completing preprocessing; s2, adjacent frame difference is executed in the transverse direction, channel fusion is executed in the longitudinal direction, and a composite frame set is generated; s3, performing bidirectional mask modeling and disturbance reconstruction on the composite frame set to obtain a regional feature matrix; s4, mapping the regional feature vectors into nodes, constructing a weighted adjacent matrix, and generating graph structure representation; s5, performing Laplace characteristic decomposition on the graph structure representation, and judging that the nodes are abnormal; s6, according to the abnormal node set, sub-graphs are divided according to topological neighborhoods, abnormal scores are calculated, and an abnormal region set is obtained; and S7, mapping the abnormal region set into the video data, and outputting an abnormal position, starting and ending frame numbers and an abnormal scoring result. According to the method, efficient and automatic anomaly detection of videos in remote areas is realized, the false alarm rate is remarkably reduced, and the anomaly positioning precision is improved.

The invention discloses an independent micro-grid optimal scheduling method based on multi-time scale source-load-storage coordination, and belongs to the technical field of power system optimal scheduling. The output volatility of renewable energy sources (such as wind energy and solar energy), the randomness of load demands and the adjusting capacity of an energy storage system and demand response resources are comprehensively considered, and the economical efficiency, reliability and renewable energy source consumption rate of the system are comprehensively improved through multi-level optimization scheduling. The method is suitable for an independent micro-grid system in remote areas (such as industrial and mining areas, islands or frontier sentries), aims to solve the technical problem that traditional single-time-scale scheduling cannot adapt to variable working conditions, and provides an innovative solution for low-carbon, efficient and reliable energy supply.

The invention discloses a radar station lightning protection device online monitoring method and system based on the Internet of Things, and relates to the technical field of equipment online monitoring. According to the invention, sensors are deployed in each component of the lightning protection device, real-time transmission and integration of monitoring data are realized by using the Internet of Things technology, a health degree evaluation model based on artificial intelligence is constructed, and remote automatic monitoring and health degree prediction of the lightning protection device of the radar station in a remote area are realized; according to the invention, the health degree evaluation model is combined with the correction coefficient to analyze the assembly evaluation data, and the health degree of each component is accurately predicted, so that the lightning protection effect of the lightning protection device is determined; according to the technical scheme, the space-time limitation of traditional manual inspection is broken through, the problem that a lightning protection device of a radar station in a remote area is difficult to monitor is solved, and conversion from passive maintenance to active prediction is achieved; through real-time data acquisition and intelligent analysis, potential hidden dangers of components can be found in advance, so that workers can take measures in time, and the lightning stroke risk is reduced.

The invention discloses an educational resource recommendation method and system based on artificial intelligence. The method comprises the following steps: acquiring audio data, interaction data and task data acquired by a user terminal; performing feature extraction on the audio data, the interaction data and the task data to obtain an emotion feature vector, a learning rhythm vector and a content feature vector; inputting the emotion feature vector and the learning rhythm vector into a pre-constructed emotion recognition model to obtain a psychological state vector; the cognitive load is calculated based on the learning rhythm vector and the content feature vector, and then the cognitive load is corrected through the psychological state vector; matching a state interval of the corrected cognitive load according to a preset threshold interval; and according to the state interval, adjusting a difficulty coefficient of the recommended course, rearranging a course content sequence and an auxiliary learning prompt, generating structured data, and outputting the structured data as an intelligent auxiliary learning recommendation result. According to the invention, online learning interactivity and teaching quality in rural and remote areas are effectively improved.

The invention relates to the technical field of dam safety monitoring, in particular to a low-power-consumption dam termite monitoring system and method based on intelligent image recognition. According to the technical scheme, the low-power-consumption dam termite monitoring system and method based on intelligent image recognition comprises a data analysis module; compared with a traditional termite monitoring system and a manual patrol mode which are applied for a long time, the termite monitoring system has the obvious defects of high system power consumption, difficulty in stable operation in remote areas such as dams and the like for a long time, poor environmental adaptability, great reduction of the recognition effect especially when light is insufficient or weather conditions are poor, and the like. The termite monitoring system can continuously collect high-quality images under the condition that power consumption is limited, and is particularly suitable for long-time unattended environment monitoring of dams and the like. The system is ensured to realize precise monitoring and real-time early warning of termite activities under the condition of low energy consumption, and meanwhile, a termite image recognition model of deep learning and small target detection is constructed, so that accurate and efficient extraction of termite information is realized.

The invention discloses an anti-frost device for an orchard, and relates to the technical field of agriculture, the anti-frost device provided by the scheme has two operation modes, the modes can be rapidly switched, and the wide adaptability in the anti-frost process of the orchard is ensured through a long-term endurance mode and a high-heat heating mode. And by adopting the design of automatically adjusting the air inflow and uniformly throwing water, not only is continuous smoke output without manual supervision realized, but also excessive combustion of fuel is effectively inhibited, and efficient utilization and safe operation of the fuel are ensured. Meanwhile, by means of the steam-driven heat directional spraying technology, the fruit trees and the ground are evenly covered with heat, the heating efficiency is improved, and the influence of smoke on the fruit trees is reduced. The device can use various biomass fuels, the operation cost is greatly reduced, electric power support is not needed, the device is particularly suitable for large orchards in remote areas, and the low-cost, efficient and environment-friendly anti-frost effect is achieved.

The invention discloses application of privacy matrix decomposition on medical recommendation service based on a large language model. The system comprises a data acquisition module; a privacy matrix decomposition module; a large language model processing module; a recommendation decision module; the user interface module breaks through the restrictions of regions, time and professional resources through personalized online medical recommendation services, can obtain high-quality medical consultation and health recommendation services for social vulnerable groups such as remote areas, rural areas, old people and patients with mobility difficulties, promotes fair distribution of medical resources, enhances user trust, and improves the user experience. Particularly, due to the application of a privacy matrix decomposition technology, worries of a user on personal health data safety are eliminated, the trust degree of the user on a medical platform is improved, health consultation service popularization is promoted, the overall health management consciousness and the self-health-care ability of the society are improved, medical knowledge and health management concepts are popularized by means of a convenient online platform, and the health management system has a wide application prospect. Misdiagnosis and treatment caused by asymmetry of medical information are reduced, and the health quality of the whole society is improved.

The invention discloses a monocular image depth estimation method based on multi-scale information fusion, and the method comprises the steps: outputting a first feature map representing local information through a CNN branch, and outputting each second feature map representing global feature correlation at a Transform branch; enhancing the input first feature map and each second feature map from three dimensions of channel, space and cross-scale by using an adaptive fine-grained channel space collaborative gating module to obtain each third feature map; using attention mechanism decoders constructed based on an up-sampling module to sequentially process the input third feature maps to obtain a monocular image depth estimation feature map; according to the invention, the problem that the processing capability of a model on a remote area in a monocular depth estimation scene cannot be improved because local features and global features cannot be effectively combined in the prior art can be solved.