39results about How to "Strong technical support" patented technology

The invention discloses an unmanned aerial vehicle three-dimensional path planning method based on an improved dung beetle algorithm, and relates to the technical field of unmanned aerial vehicle path planning. According to the technical scheme, an unmanned aerial vehicle flight environment is modeled; establishing constraint conditions; establishing a target function; and performing unmanned aerial vehicle global path planning by adopting an improved dung beetle optimization algorithm. The method has the beneficial effects that initialization is carried out by utilizing a good point set strategy, so that the population diversity is improved; an improved sine algorithm is combined with a variation strategy of Levy flight and Brownian motion to be embedded into position updating of dung beetles, so that the search range is expanded, and the global optimization ability of the algorithm is improved; a simplex method and a self-adaptive reverse learning strategy are added for disturbance, and the algorithm is helped to jump out of a local optimal solution; the efficiency and accuracy of unmanned aerial vehicle path planning are remarkably improved, the robustness and adaptability of the algorithm are enhanced, the flight path of the unmanned aerial vehicle is optimized, and further development of the unmanned aerial vehicle technology is promoted.

The invention relates to the technical field of legal text generation, in particular to an intelligent case generation method based on a judicial knowledge domain atlas, which comprises the following steps: S1, constructing the judicial knowledge domain atlas; s2, extracting case facts and judgment reason parts of the document; s3, classifying and splitting case facts of the document; s4, carrying out deep learning to generate an abstract; s5, correlation comparison of the content is carried out, and integrity and accuracy are verified; s6, optimizing a judgment key through a convolutional neural network model; s7, automatically generating correlation indexes with other historical cases according to correlation analysis in the judicial knowledge domain graph; s8, summarizing the results, and splicing the results into a complete case document; according to the method, by combining the judicial knowledge domain graph and the deep learning technology, the legal document is automatically generated, the integrity of document content and the accuracy of legal logic are ensured, historical cases are efficiently associated, and the efficiency and quality of judgment document generation are remarkably improved.

The invention provides an ultrasonic straight-blade cutter wear test device and method, and aims to simulate and research a wear evolution rule of a cutter in ultrasonic machining. The ultrasonic straight-blade cutter wear test equipment comprises a cutting system, a positioning system, a holding system, a cutter system and a test system. The cutting system outputs linear reciprocating motion to simulate a low-frequency contact period of a hole grid and a cutter; the positioning system achieves accurate tool setting, and it is ensured that a tool is just in contact with a sample piece; the holding system is used for fixing and tensioning the sample piece and maintaining a stable cutting condition; the cutter system applies high-frequency vibration to the ultrasonic straight-edge cutter to simulate a high-frequency vibration period in actual ultrasonic machining; the testing system monitors the contact condition of the cutter and the sample piece in real time and records the abrasion behavior. According to the ultrasonic straight-blade cutter wear test equipment, by simulating the superposition effect of a low-frequency contact period and a high-frequency vibration period, the cutter wear rule in an ultrasonic machining environment is truly revealed, and powerful technical support is provided for prolonging the service life of a cutter and improving the performance of the cutter.

The invention relates to the technical field of hardware fault early warning, in particular to a server hardware fault early warning and recovery method and system based on an intelligent optimization algorithm, and the method comprises the steps: obtaining hardware self-inspection information; acquiring load state information; acquiring historical operation data; constructing a preliminary mapping matrix based on the acquired hardware self-inspection information, load state information and historical operation data; according to the preliminary mapping matrix, through a topological space optimization algorithm, key modes are extracted, and an optimized mode matrix is generated; based on the optimized mode matrix, combining a fault-tolerant strategy group, and screening a fault-tolerant or backup strategy suitable for the current state; according to a fault-tolerant or backup strategy, evaluating the benefit, the cost and the success probability of the strategy, and generating an optimal decision scheme; outputting fault early warning information including early warning time, an early warning level and a trigger strategy; and outputting an optimal decision scheme, including a fault-tolerant strategy or a backup strategy, so that a more complex hardware abnormal mode can be captured, and the accuracy of early warning is greatly improved.

The invention discloses an automatic wafer packaging detection and sorting system, which relates to the field of semiconductor manufacturing and detection, and comprises the following steps: automatically acquiring packaged wafers from packaging equipment and a storehouse by integrating automatic logistics and intelligent scheduling; high-precision camera multispectral detection is combined with AI analysis, and surface defects of the packaged wafer are detected; terahertz imaging and X-ray are combined with an AI technology, the internal structure of a packaged wafer welding spot is detected, and holes, cracks and welding quality defects are recognized; electrical detection is carried out on the packaged wafer through a flexible probe, and intelligent fault positioning is carried out in combination with AI; an extreme temperature change is simulated to carry out a thermal cycle test, and thermal stress is monitored in real time; and an AI optimization temperature curve and atmosphere control are adopted to simulate a multiple reflow soldering process. By integrating an advanced detection technology and an intelligent means, the wafer packaging detection precision and sorting efficiency are improved, meanwhile, the production cost is reduced, and powerful data support is provided for continuous improvement.

The invention provides a spacecraft-oriented multi-task data set construction method. A high-precision satellite geometric model is created. The image rendered based on the PBR not only has a bright-eye visual effect, but also conforms to a physical optics principle, and particularly, a normal mapping simulates a star body wrinkle effect. A space simulation scene is constructed by using an unreal engine 4, and a vivid rendering effect is provided. A high-quality satellite image is generated by combining a scene capture component and a target rendering function of an unreal engine, and a semantic segmentation tag, an instance segmentation tag, a depth tag, a normal tag and a pose tag are automatically generated based on a 3D model 2D image automatic labeling method. And a large-scale data set can be quickly generated in a frame-by-frame rendering and automatic labeling mode. Semantic segmentation is carried out on the generated image through the deep learning model DeepLab V3 +, the accuracy and efficiency of model prediction are further improved, and the rendering trueness is greatly improved through detailed analysis and optimization.

The invention provides a safety management and control system for an intelligent lock of a transformer substation, and the system comprises an intelligent wearable device which is used for monitoring the physiological data of a wearer in real time; the environment monitoring module is used for monitoring the environment risk of the working area in real time; the video monitoring module is used for monitoring the in-out dynamic state and the behavior standard of the personnel; the intelligent lock linkage management module is used for automatically locking an intelligent lock and giving an alarm according to monitoring results of the intelligent wearable device, the environment monitoring module and the video monitoring module when abnormity is monitored; the risk assessment module is used for establishing a risk assessment model and dynamically adjusting the operation authority of the intelligent lock; and the situation awareness control module is used for changing the operation authority of the intelligent lock according to the situation evaluation result. The invention further discloses a corresponding method. According to the invention, the safety and management efficiency of electric power facilities such as a transformer substation are improved.

The invention discloses a deep mutation scanning method based on protein-protein interaction in bacterial cells and a binding affinity prediction method. In the invention, split dCas9 proteins (SEQ ID NO.1 and SEQ ID NO.2) are respectively subjected to fusion expression with a receptor and a ligand with binding affinity to be detected, and are combined with a toxic sgRNA guide sequence (SEQ ID NO.3), and when PPI occurs, a toxic dCas9: sgRNA complex is formed to inhibit bacterial growth, so that a PPI affinity size signal is converted into a bacterial cell generation speed signal, and the signal can be quantified into a copy number value through next-generation sequencing. The invention further designs a deep learning binding affinity prediction model, high-affinity sequences can be screened from theoretical mutation space, the workload and cost of experimental verification are remarkably reduced, and a reliable basis is provided for subsequent protein function optimization and design.

The invention belongs to the field of power grid safety prediction, and provides an urban underground cable tunnel network fire hazard temperature prediction method and a related device, and the method comprises the steps: collecting the environment data in a cable tunnel and the material data of a cable; establishing a three-dimensional finite element model comprising an electromagnetic field, a thermal field and a fluid field based on the environmental data and the material data, simulating temperature responses of the cable under different conditions, and obtaining sample data of cable core temperature; the method comprises the following steps: training a convolutional neural network (CNN) to extract features based on environment data and material data, processing time sequence data through a long short-term memory (LSTM) network, and constructing a CNN-LSTM hybrid model; and taking the obtained sample data of the cable core temperature as the input of the CNN-LSTM hybrid model to obtain a predicted value of the cable core temperature. The method not only improves the accuracy and real-time performance of prediction, but also enhances the adaptability and robustness of the model to a complex environment, and has wide application prospects and important social values.

The invention relates to the technical field of path planning, in particular to a bucket wheel machine unmanned collaborative operation reclaiming path planning method, which comprises the following steps of: constructing a three-dimensional stacking model of a stock ground, and determining an operation overlapping area of adjacent bucket wheel machines according to operation positions and operation ranges of the adjacent bucket wheel machines; dividing the stockpile in the operation overlapping area into a plurality of stockpile blocks, and generating a priority distribution table of the stockpile blocks in the overlapping area through priority calculation; based on the priority distribution table, the stacking blocks in the operation overlapping area are distributed to the two adjacent bucket wheel machines; planning a preliminary path for each bucket wheel machine by using the distribution result, wherein the path planning needs to be followed; and updating the stacking form and adjusting the path in real time. According to the invention, the conflict of processing the same stockpile block by two bucket wheel machines is effectively avoided, dynamic balance of task load is realized, and high efficiency and continuity of collaborative operation are ensured.

The invention relates to the field of robot path planning, and discloses a service robot path planning system with a self-repairing function and a method thereof. The voice recognition module is used for carrying out voice activity detection and transcription and realizing accurate understanding of natural language commands; the object recognition module is used for carrying out object detection and classification and simultaneously receiving text input so as to provide recognition prompts and enhance the perceptual ability of the robot in the environment; the task planning module is used for converting a natural language command into an executable format and realizing automatic execution of a complex task by executing combination of skill functions; and the semantic mapping module integrates the environment information into a 3D semantic map, carries out image and text coding, and provides rich semantic information for task planning with a self-repairing function of the robot. According to the invention, efficient path planning and task execution in a dynamic and huge service environment are realized.

The invention discloses a traditional Chinese medicine analysis and identification system, method, equipment and medium based on a large model, and the system comprises a data collection module which is used for collecting multi-dimensional data of traditional Chinese medicines, a data preprocessing module which is used for carrying out the cleaning, standardization processing and feature extraction of the collected data, and a data analysis module. The data processing module is used for analyzing and processing received data by utilizing a pre-trained deep large model, the result output module is used for outputting an analysis result to a user in a visual manner, and the analysis result output module comprises a visual chart, a detailed report form and an intelligent feedback module according to the feedback of the user on the analysis result and the actual application condition. Through multi-dimensional data acquisition, advanced preprocessing, deep large model analysis and the like, the practicability is improved by modules of accurate identification, intelligent feedback, collaborative analysis and the like of the traditional Chinese medicine, data updating and the like ensure that the system advances with the times, powerful technical support is provided for industrial development of the traditional Chinese medicine, and the quality and safety of the traditional Chinese medicine are guaranteed.

The invention relates to a nuclear magnetic resonance image classification diagnosis method for adult diffuse glioma. The method comprises the following specific steps: step 1, respectively acquiring T2-fluid attenuation inversion recovery T2-FLAIR and contrast enhancement T1 weighted imaging CE-T1WI magnetic resonance images of glioma labeled by a doctor according to histological and genomics information; step 2, carrying out data preprocessing operation on the T2-FLAIR image data and the CE-T1WI image data; step 3, each pair of T2-FLAIR and CE-T1WI images after preprocessing is sent to a decision tree diagnosis model, and an Attention-DenseNet model is used for learning and extracting the characteristics of the T2-FLAIR and CE-T1WI images; 4, obtaining a prediction result based on a plurality of independently trained Attention-DenseNet models, fusing the outputs of the plurality of models by adopting a voting mechanism or a weighted average strategy, and generating a final classification decision; and 5, sending the obtained final classification to a platform, and making final diagnosis based on a classification result.

The invention relates to a centrifugal pump fault diagnosis method and system based on a visual sensing technology. The system comprises a video stabilization module, a vibration amplification module, a vibration measurement module, a fault diagnosis module and a data processing and storage module. And the video stabilization module is used for processing a video by adopting an SIFT (Scale-Invariant Feature Transform) algorithm and an RANSAC (Random Sample Consensus) algorithm, so that the jitter of a camera is eliminated. And the vibration amplification module is used for realizing vibration visualization by using an improved N-PBVM (Non-Fourier Phasease-based Video Magnitation) algorithm (N-PBVM), and the vibration amplification module is used for realizing the vibration visualization by using the improved N-PBVM algorithm (Non-Fourier Phasease-based Video Magnitation, N-PBVM). The vibration measurement module calculates vibration displacement through an optical flow method related to an image phase. The fault diagnosis module identifies fault features based on frequency domain analysis, such as rotor imbalance, misalignment, and base looseness. And the data processing and storage module stores and analyzes the vibration data and predicts a fault trend by adopting a machine learning algorithm. According to the system, non-contact measurement and fault diagnosis of the centrifugal pump are achieved, and an efficient and reliable technical means is provided for maintenance of industrial equipment.

The invention provides a well drilling parameter optimization method and device based on clustering analysis, and relates to the technical field of well drilling construction data analys.The method comprises the steps that for a to-be-optimized target well, well logging data and well drilling data of adjacent wells of a target block where the to-be-optimized target well is located are obtained; according to the logging information of the target well to be optimized and the logging data of the adjacent well, dimension reduction processing is carried out, and a logging curve after dimension reduction processing is obtained; performing clustering analysis according to the logging curve, and determining similar wells of the same category as the target well to be optimized; according to the mechanical specific energy and the mechanical drilling speed of the same kind of wells, a drilling parameter range corresponding to each layer position is screened out from the drilling data, and the optimal adjacent well corresponding to the target well to be optimized is determined through the drilling parameter range; and optimizing the drilling parameters of the target well to be optimized by using the drilling data of the optimal adjacent well to obtain optimized drilling parameters.

The invention provides a language resource library system based on data security control, and the system comprises an acquisition module which collects language resources in a manner of manual input and crawler program directional capture, obtains a language data set, and stores the language data set in a database; the processing module develops a software program according to a preset metadata specification, and processes and manually marks the language data set to obtain a standard data set; the management module is used for performing data management, user management and log management in combination with the data access control model and the network security monitoring model; the presentation module is used for providing data retrieval and statistical analysis functions according to the data type and carrying out visualized display of the data; and the product library module is used for developing a language resource platform. By establishing a perfect security mechanism, unauthorized access and data leakage can be effectively prevented, the security of language resources in cross-border transmission and use is improved, and efficient and stable operation of the system is ensured.

The invention discloses a retrieval sample selection and multi-round error correction method for solving a mathematical problem of a large language model. The retrieval sample selection and multi-round error correction method comprises the following steps: S1, constructing a retrieval sample library; s2, a mathematical character problem is given, cue words and a plurality of fixed samples for successfully solving the problem are used as input, and the large language model is guided to generate an initial thinking chain and Python codes; s3, the generated initial Python code is submitted to a Python interpreter to be executed; s4, if the initial Python code is successfully executed, ending the process, and taking an execution result as a final answer; s5, if the execution of the initial Python code fails, selecting a retrieval sample from a pre-constructed retrieval sample library according to the error type and the problem semantic similarity returned by the Python interpreter; s6, taking the selected retrieval sample and the original mathematical character problem in the step S2 as new input, submitting the new input to the large language model again, and carrying out the next round of thinking chain and code generation; and S7, repeating the steps S2-S5 until the program runs normally or the preset maximum number of attempts is reached.

The invention relates to an inspection evidence storage method and device, equipment and medium in the technical field of property information, and the method comprises the steps: responding to an inspection task execution event, and obtaining inspection point clock-in information and inspection point real shooting information correspondingly generated when a terminal executes a field clock-in operation and a field shooting operation at a corresponding inspection point, the method comprises the steps of forming inspection point detail information, performing symmetric encryption on original image representation corresponding to an inspection real-shot image according to watermark position information in the inspection real-shot image contained in the inspection point real-shot information to obtain an information encapsulation ciphertext, generating a tamper-proof code containing the information encapsulation ciphertext, and synthesizing the tamper-proof code into the inspection real-shot image to obtain the tamper-proof information of the inspection real-shot image. And generating an information check code corresponding to the detailed information of the inspection point, submitting the inspection point information and the information check code to the block chain platform, and obtaining a block chain evidence storage certificate returned by the block chain platform. According to the invention, highly safe and non-tampering inspection information evidence storage is realized.

The invention belongs to the technical field of highway maintenance operation warning, and relates to a highway maintenance operation safety warning robot cluster and a control method thereof. The safety warning robot cluster comprises a plurality of road maintenance operation safety warning robots; the road maintenance operation safety warning robot comprises a movable chassis and a warning device arranged on the movable chassis. A depth camera is arranged on the warning device; a GPS positioning unit, a control unit and a power supply are arranged on the movable chassis; the control motor, the GPS positioning unit and the depth camera are all connected with the control unit. The control method comprises a remote control mode and an autonomous control mode. According to the remote control mode, an external remote control unit transmits a control signal to the control unit, and the road maintenance operation safety warning robot cluster is controlled to move to a target position. In the autonomous control mode, the control unit calculates the target position and the target track of the navigation safety warning robot and controls the navigation safety warning robot and the following moving safety warning robot to move to the target position.

The invention relates to the technical field of robot control, in particular to a double-arm collaborative robot fuzzy adaptive sliding mode control method, which comprises the following steps: S1, establishing a dynamic model of a double-arm collaborative robot; s2, fusing a Taylor series expansion method on the basis of the dynamic model of the double-arm collaborative robot, and establishing an improved dynamic mathematical model suitable for a time delay condition; s3, using a fuzzy algorithm and an average residence time mechanism to develop a fuzzy adaptive sliding mode tracking controller; and S4, designing a neural network tracking control scheme based on fuzzy recursion to eliminate a time delay error existing in the model. According to the control method provided by the invention, the negative influence of the time delay on the system stability can be effectively overcome while the interference influence is eliminated, and the application range of the theoretical delay value is obtained on the basis of improving the control quality of the double-arm system.

The invention relates to a doubly-fed wind turbine generator gearbox operation pressure generation method and system. The method comprises the following steps: acquiring real-time operation pressure sequence data of a doubly-fed wind turbine generator gearbox; feature selection and downsampling are carried out based on the real-time operation pressure sequence data, multi-granularity pressure sequence data are obtained, and the multi-granularity pressure sequence data comprise fine-granularity pressure data and coarse-granularity pressure data; and inputting the multi-granularity pressure sequence data into a pre-trained pressure data generation model for processing so as to analyze the trend of the pressure sequence data with different granularities, and finally generating running pressure data of multiple step lengths in the future. Compared with the prior art, the method has the advantages of effectively reducing noise interference, being high in precision and the like.

The utility model provides an electric field driven liquid-liquid multiphase reactor based on a fluidized bed. The electric field driven liquid-liquid multiphase reactor comprises a reactor body, a mesh partition plate, inert particles, a first electrode, a second electrode and a controller, the mesh partition plate is mounted at the lower part of the reactor body, a cavity above the mesh partition plate in the reactor body is a reaction area, and the inert particles are distributed in the reaction area. The first electrode and the second electrode are respectively arranged on two sides of the inner wall of the reactor body, are positioned in the upper reaction area, and are respectively and electrically connected with the positive electrode and the negative electrode of the high-voltage power supply. And the high-voltage power supply is connected with the controller. According to the utility model, electric field force is utilized to apply acting force to charged liquid drops, the liquid drops are driven to generate violent collision and shearing action in the fluidized bed, and mass transfer and reaction between liquid and liquid are promoted. Meanwhile, the electric field force can also control the movement of the liquid drops, so that the reaction process is strengthened and controlled.

The invention discloses a perspective C2P conversion and layout generation method for room layout estimation. Comprising the steps of obtaining a target indoor scene image; inputting a target indoor scene image into the deep learning model, and outputting a predicted complete depth map, a global plane visibility confidence map, a wall category and a plane semantic tag; the method comprises the following steps of: classifying a plane, defining anchor points uniformly distributed on a perspective image, calculating centroid coordinates of a corresponding segmentation region of a dominant plane for the dominant plane, and obtaining a rough layout segmentation mask by a label assigned to the plane corresponding to an index of the anchor point closest to the centroid; a plane depth parameter is generated from the complete depth map and the plane visibility confidence. A planar depth map is generated using a C2P conversion technique, and the planar layout of a room is accurately estimated through sequential planar depth map integration. According to the method, C2P transformation can be flexibly applied to perspective images, and powerful technical support is provided for plane depth reconstruction.

This invention relates to the field of pathogen detection technology, specifically to an intelligent adaptive virus detection and optimization method and system. The method includes: acquiring raw sequence data generated by probe capture sequencing; performing real-time analysis of the sequence data using a deep learning model, outputting analysis results including the virus's presence state and sequence characteristics; dynamically adjusting control parameters of the hybridization capture process based on the analysis results through an adaptive control system; and completing hybridization capture according to the adjusted parameters and outputting a detection report. The system includes a sequencing data acquisition module, an analysis module, a sensor data acquisition module, an adaptive control module, and a detection report generation module. This invention, by combining artificial intelligence analysis with real-time process control, achieves intelligent optimization of the virus detection process, significantly improving the accuracy, sensitivity, and adaptability of detection. It is particularly suitable for detecting highly variable viruses and low-viral-load samples, and has significant clinical application value.

The present invention discloses a single-cell data relational sequencing clustering method based on a generative adversarial network, the method comprising the following steps: step 1, data acquisition; step 2, data processing; step 3, graph construction; step 4, graph autoencoder training; step 5, point selection and deep model clustering; step 6, re-clustering. The method deeply mines the local structure and dynamic changes in the cell population by constructing a graph structure containing cell-to-cell + cell-to-gene, and uses an adversarial network to optimize the embedded representation to improve the accuracy and computational efficiency of clustering. The present invention not only enhances the ability of feature representation, but also better expresses the biological characteristics and relationships of cells in a low-dimensional space, and is suitable for fields such as cell classification, cancer research, and immunological analysis.

The invention belongs to the technical field of biomedicine, and provides a breast pad in-situ tumor animal model blood flow velocity prediction method for solving the problem that an existing solid tumor blood flow velocity detection method is difficult to be widely applied to conventional research, and the method comprises the steps that the blood flow velocity and the tumor volume of in-situ tumor blood vessels are continuously collected; analyzing the animal plasma sample to obtain the plasma cell factor concentration related to the blood vessel function; and fitting the morphological characteristics of the tumor tissue, the cell factor concentration and the blood flow velocity of the tumor blood vessel to construct a blood flow velocity prediction model based on the tumor volume and a blood flow velocity prediction model based on the plasma cell factor concentration. Through the constructed prediction model, a researcher can conveniently predict the blood flow velocity of the tumor blood vessel through the obtained data only by obtaining tumor volume data or concentration data of a certain plasma cell factor, additional animal experiments are not needed for measurement, and the method can be widely applied to conventional research.

The invention discloses a multi-level feature multicast transmission method and device, equipment, a storage medium and a product, and relates to the technical field of semantic communication.The method comprises the steps that multi-level features are obtained according to a target picture, the multi-level features comprise semantic features, semantic errors of different levels are determined according to the semantic features, and the target picture is obtained; and determining the communication resource of the data receiving device in the current hierarchy according to the target feature of the target picture, thereby obtaining the receiving capabilities of different users, and facilitating reasonable resource allocation during subsequent semantic communication for multiple users. An optimization problem is established based on communication resources of different levels of data receiving equipment and semantic errors of different levels to obtain an optimal solution, and furthermore, multicast transmission is performed on target features based on the optimal solution, so that the communication resource overhead in a multi-user scene is reduced, the requirements of different mobile agents are met in information transmission, and the information transmission efficiency is improved. And the transmission time delay and the resource consumption are reduced on the whole.

The invention discloses a form data visualization generation method and system based on Data Science, belongs to the technical field of data visualization, and aims to solve the technical problem of how to provide whole-system-oriented data visualization and data analysis for a user, improve the data visualization experience of the user and improve the user experience. According to the technical scheme, the method comprises the steps that data preprocessing is carried out, wherein data preprocessing is carried out on fields and data of a form separately, and preprocessed data is obtained; feature extraction: utilizing a Glove model to perform word embedding feature extraction on the preprocessed data by constructing a vocabulary; predicting a data topic: predicting a topic result through an SVM model; visual parameters are generated, wherein parameters used for data visualization are generated through a Transform model; and data visualization is carried out, wherein data visualization is realized through a numpy library, a matplotlib library or a search library.

The invention relates to the technical field of forestry dynamic monitoring, and discloses a forestry dynamic monitoring method and system based on cloud network collaboration. The method comprises the following steps: acquiring forestry environment data through Internet of Things sensor nodes deployed in a forest region, and carrying out real-time data cleaning and format normalization by a built-in edge calculation unit of the nodes to generate a standardized compressed data packet; a transmission path is dynamically selected by using a wireless communication network, a data subpackage and retransmission mechanism is adaptively adjusted, and reliable transmission of data is ensured. The cloud platform analyzes the data and performs space-time correlation analysis, and generates monitoring information through abnormal mode recognition, thereby dynamically adjusting the data acquisition frequency of the sensor node and the working mode of the edge computing unit, and improving the forestry disaster early warning capability and the resource management efficiency.

This invention belongs to the interdisciplinary field of neuroscience, sensory science and technology and food science. It provides a method for objectively quantifying taste perception using electroencephalography. The invention aims to separate and decode the neural dynamics of the brain in processing taste stimuli of different intensities, and to objectively evaluate and classify them based on individual differences in innate taste sensitivity.