202results about How to "Promote intelligent development" patented technology

The invention provides a prediction model construction method and system for personalized equipment operation and maintenance, and relates to the technical field of data processing, and the method comprises the steps: collecting equipment operation data and environment data, extracting the change characteristics of the equipment operation state, carrying out the correlation analysis of the environment data based on historical equipment fault records, and obtaining a prediction model. Performing normalization processing, performing time sequence analysis, generating a joint feature matrix, segmenting the joint feature matrix into time slices of a fixed window according to a time sequence, classifying the time slices into a normal state and an abnormal state, performing feature contribution degree analysis, generating a feature weight matrix, and performing feature extraction; re-combining the equipment features in the time slice feature data set with the environment factors according to feature weights, generating fusion feature data layer by layer, performing supervised learning training on the fusion feature data, constructing an equipment prediction model, and outputting the equipment prediction model as a prediction model of personalized equipment operation and maintenance; according to the invention, the operation and maintenance effect of personalized equipment is improved.

The invention discloses an automatic fine ore sorting method and system based on XRT (X ray tomography) and hyperspectrum, which are used for realizing comprehensive fusion of ore surface and internal information by combining X-ray tomography and hyperspectral imaging technologies so as to improve the recognition precision and the sorting efficiency. XRT is used for extracting the internal structure and density characteristics of the ore, hyperspectral imaging is used for identifying the mineral composition characteristics of the ore surface, and information complementation is achieved. The system adopts a multi-modal convolutional neural network to perform independent feature extraction on XRT and hyperspectral data, and the extracted features are fused through a deep residual network. And the fused features are processed by a cross-modal attention mechanism to ensure that key features of different modals are fully integrated and optimized, and finally high-precision ore classification and sorting are realized. According to the method, the limitation of a single data source on feature extraction is overcome, the ore recognition and sorting precision is greatly improved, and the refined and intelligent development of ore sorting is promoted.

The invention provides a power transmission and transformation project ecological influence space-time boundary determination method and system based on multi-source remote sensing information deep fusion interpretation, and belongs to the technical field of power transmission and transformation project management. The method comprises the steps of data acquisition, data preprocessing and multi-source data integration, multi-scale space-time analysis, ecological influence index construction, remote sensing ecological index model construction, ecological influence space-time boundary determination, dynamic monitoring and early warning system platform establishment and long-term monitoring and evaluation. By integrating satellite remote sensing, unmanned aerial vehicle aerial photography and ground monitoring data and combining an image fusion algorithm, a principal component analysis method and a random forest algorithm technology, the method is beneficial to promoting accurate evaluation of the influence of the power transmission and transformation project on the ecological environment, visualizes the space-time boundary of the ecological influence, breaks through the limitation of a traditional evaluation method on time and space coverage, and improves the evaluation efficiency of the power transmission and transformation project. And a scientific method system for analyzing and evaluating the ecological influence of the power transmission and transformation project is further improved.

The invention relates to a coal mine fully-mechanized excavation face intelligent ventilation dust control and removal system based on an optimal coordination mechanism, and belongs to the technical field of coal mining. According to the system, through integrating a plurality of functional modules such as data processing, area control, optimal parameter calculation, execution control, safety guarantee and expert system evaluation, intelligent management of ventilation, dust control and dust removal is realized. The system adopts a method of combining fuzzy control and PID (Proportion Integration Differentiation) control, and automatically calculates and adjusts operation parameters of the air control device and the dust remover according to environmental parameters such as dust and gas concentration which are monitored in real time, so as to realize an optimal ventilation and dust control effect. Through training and prediction of a machine learning model, the system can autonomously learn and optimize technical parameters of a dust control and removal process, the adaptive capacity and the operation efficiency of the system are improved, the dust and gas concentration of a working face is effectively reduced, and health of workers and safe production are guaranteed. The invention aims to improve the environmental quality of the coal mine fully-mechanized excavation face and promote the intelligent development of the coal mine ventilation dust control and removal technology.

The invention, which relates to the technical field of intelligent logistics management, discloses an AI-based intelligent logistics management system comprising a warehouse management unit, a data fusion acquisition unit and a digital twin supervision unit. The warehouse management unit is used for managing warehouse-in and warehouse-out information and cargo information of a warehouse, reminding inventory risks, and carrying out demand prediction and inventory optimization; the data fusion acquisition unit is used for acquiring personnel, goods, equipment and environment information of a warehouse; the digital twin supervision unit comprises an intelligent AI recognition unit and a data analysis processing unit, the intelligent AI recognition unit is used for carrying out recognition analysis and risk reminding on personnel, goods, equipment and environment of a warehouse through an AI intelligent algorithm, and the data analysis processing unit is used for mapping data of an entity warehouse to a digital twin warehouse. And visual interaction management and intelligent scheduling of personnel, goods and equipment are carried out on the digital twin warehouse, so that the warehouse can be comprehensively monitored, and the goods storage and management efficiency is improved.

The invention belongs to the technical field of intelligent weighing, and more particularly relates to an intelligent self-adaptive electronic scale system based on machine learning and a data processing method, and the system comprises a composite sensor module which is used for collecting gravity, volume and shape information; the automatic calibration module is connected with the composite sensor module and is used for carrying out regular calibration and calibration according to the data of the composite sensor module; the wireless networking module is connected with the automatic calibration module and is used for wirelessly transmitting and interacting the calibrated data; the intelligent operation and maintenance module is connected with the wireless networking module and is used for receiving the transmitted data and realizing equipment state monitoring and anomaly detection; the environment adaptation module is connected with the intelligent operation and maintenance module and is used for realizing adaptive compensation in a severe environment according to a detection result; and the man-machine interaction module is connected with the environment adaptation module and is used for displaying a compensated result and realizing a user-friendly operation interface, so that high-precision and all-around weighing perception is realized.

The invention discloses an artificial intelligence perioperative period anesthesia depth detection system which comprises a data acquisition module which is provided with a multi-mode sensor and is used for acquiring physiological data of a patient in a perioperative period, and an artificial intelligence model module which is used for constructing an anesthesia depth prediction model based on a deep learning algorithm. The result output and display module is used for outputting the prediction result of the anesthesia depth in real time and visually displaying the prediction result on a display screen in the form of digits and charts, and the communication and interface module is provided with an interface for data communication with a hospital information system and an electronic medical record system. By means of multi-mode collection and innovative design, the anesthesia depth detection accuracy is improved, precise monitoring and intelligent auxiliary decision making are achieved, the method can be used for various scenes, the safety of patients in the perioperative period is effectively guaranteed, and the medical efficiency is improved.

The invention relates to an intelligent fault prediction system based on deep learning, and aims to improve the fault prediction capability and operation and maintenance efficiency of an IT system. The system comprises a data collection module, a preprocessing module, a deep learning model module, a fault prediction module and an alarm feedback module. The system collects data, performs cleaning, normalization and feature extraction on the data, and performs training and prediction by using deep learning algorithms such as a convolutional neural network (CNN) and a long short-term memory (LSTM) network. The core technology is to analyze operation data in real time by using a deep learning model and generate a fault prediction result and early warning information. The fault prediction module analyzes data in real time, and the alarm and feedback module provides feedback for system optimization. According to the system, the accuracy and the real-time performance of fault prediction are remarkably improved, manual intervention is reduced, the maintenance cost is reduced, and the system stability and the operation and maintenance efficiency are improved. The method is widely applicable to the fields of data centers, network equipment and the like, and has remarkable technical and commercial values.

The invention discloses a shield tunnel risk early warning method and system based on digital twinning and federated learning, and the method comprises the steps: a shield tunnel construction side client serves as an edge calculation terminal, and obtains basic parameters; a first shield digital twinning system, a second shield digital twinning system and a third shield digital twinning system are correspondingly constructed by the shield machine producer client side, the shield tunnel construction side client side and the third-party client side according to the basic parameters; a shield construction risk early warning model is arranged in each shield digital twin system; the shield machine production side client, the shield tunnel construction side client and the third side client are all participants, and each participant sends model parameters of the respective shield construction risk early warning model to the central server; the central server is used for averaging the parameters of the three models and issuing the average value of the parameters to each participant; and each participant updates the model of the participant according to the average value of the parameters, and realizes shield construction risk early warning based on the updated model.

The invention relates to the technical field of soil health analysis, in particular to a tobacco planting soil health analysis and fertilization prediction system. Comprising a server, a health analysis module and a prediction management module, soil state parameters are obtained through soil microorganism activity action state analysis, soil nutrient state analysis and soil moisture state analysis of tobacco planting soil, then the soil change degree is analyzed, a prediction updating instruction is generated, and comprehensive detection, analysis and evaluation of the soil are achieved. By comprehensively analyzing the state parameters of the soil, the demand change of the soil, including the opportunity and quantification of fertilization, pesticide application and irrigation, can be accurately predicted, so that the growth efficiency and yield of crops can be improved; meanwhile, through the generated prediction notice, a farmer or a manager can be guided to take corresponding measures in time, it is guaranteed that soil and crops grow in the optimal state, and malnutrition, pest and disease damage and other problems are avoided.

The invention discloses a safety engineering optimization method and system based on artificial intelligence, and belongs to the technical field of safety engineering optimization, and the method comprises the steps of data preparation, data preprocessing, elevator running state evaluation, elevator fault prediction and elevator safety engineering optimization. According to the method, the elevator running state is evaluated by adopting the long-short-term memory model for enhancing parameter optimization, so that the sparrow population diversity is increased, the global search capability of the algorithm is improved, the parameter optimization is better performed, and the model prediction effect is improved; an auto-encoder long-short-term memory parallel model is adopted for elevator fault prediction, through parallel processing of an auto-encoder and a long-short-term memory network, rich feature representation is better captured, the model prediction precision is improved, integrated learning is carried out through a gradient lifting technology, and the model robustness is enhanced; the elevator running state evaluation information and the elevator fault prediction information are integrated, elevator safety engineering optimization is achieved, the influence of subjective factors is reduced, and the overall stability and reliability of an elevator system are effectively improved.

The invention discloses an intelligent nutrition and metabolism management system for tumor patients, and particularly relates to the technical field of intelligent management. Comprising a patient treatment duration division module, a patient treatment nutrition and metabolism information acquisition module, a patient treatment nutrition intelligent management module, a patient metabolism risk intelligent evaluation module, an auxiliary decision data analysis module, a patient treatment effect intelligent early warning module and a patient treatment nutrition and metabolism intelligent optimization module. According to the invention, the nutrition and metabolism monitoring data of each sub-period of the disease treatment duration of the target patient is collected, the nutrition state evaluation index and the metabolism function risk management index are calculated, and the patient treatment effect early warning index is obtained through analysis, so that the nutrition and metabolism conditions of the patient are intervened, the treatment effect evaluation accuracy is improved, and the patient treatment efficiency is improved. Waste of medical resources is reduced, and intelligent development of treatment effect decision making is promoted.

The invention discloses an intelligent impact rock drilling while-drilling lithology identification system and method, and belongs to the technical field of coal mining. The system comprises a front drill boom, an electrical unit, a hydraulic unit, an intelligent operation table, a data acquisition unit and a data processing unit, the electric unit and the hydraulic unit provide working power required by rock drilling construction; the intelligent operating platform controls the front drill boom to advance, swing and pitch; the data acquisition unit acquires propulsion pressure, impact pressure, rotation pressure, rotation speed and displacement signals in the construction process; the data processing unit stores and processes data; a reverse learning strategy and a self-adaptive t distribution strategy are used for improving a Tianying algorithm and optimizing and training a support vector machine model, then data collected by a data collection unit serve as input parameters, the optimized support vector machine model is input, an intensity prediction result is output, and real-time prediction of the tunnel face rock intensity in the drilling process is achieved.

The invention relates to the field of aquatic product safety monitoring and early warning, in particular to an aquatic product safety monitoring and early warning system and method, which comprises a data acquisition module, a data transportation module, a data analysis module, a data early warning processing module and an information management module, through real-time acquisition of related data in links of aquatic product production, processing, storage, transportation and the like, water quality parameters, temperature and humidity are analyzed, the safety condition of aquatic products and a potential risk early warning processing module are evaluated, and when a data analysis result shows that potential safety hazards exist or exceed a set standard, early warning signals of different levels are sent out. Therefore, equipment and related personnel can take measures in time. And the information management module effectively manages and stores various data, thereby facilitating query and tracing. And related reports are generated to provide a basis for decision making.

The invention discloses a cross-domain data fusion method and system for distribution network middle stations based on a knowledge graph, and particularly relates to the technical field of data processing and fusion based on the knowledge graph. Firstly, intelligent gateway equipment is adopted to collect operation, fault, maintenance and other data of all systems and all lines related to distribution network operation, the data are transmitted to a data processing and storage module through a 5G communication protocol port transmission technology, and data cleaning, data integration, data conversion and other operations are carried out; classifying and storing the data, and then constructing a mapping knowledge domain in the distribution network; according to the method, a 5G communication protocol port transmission technology and a composite model are specifically adopted to perform knowledge extraction and knowledge fusion of graph construction, so that the technical problems of data islands and other data processing in a distribution network are solved, data sharing, service sharing and resource integration are effectively realized, and the reliability of the distribution network is improved. The problem that data between distributed systems in the current distribution network field is difficult to cooperate is solved, semantic integration and sharing of data between multiple systems are achieved, and the operation and maintenance efficiency and the intelligent level of a power distribution network are improved.

The invention belongs to the technical field of manufacturing and production, and particularly relates to an intelligent manufacturing and production control system, which is characterized in that production data on a production line are collected, and a database is established for the real-time data of the production line according to time nodes and station nodes for storage; carrying out instant report, trend analysis and anomaly detection on the collected data, carrying out deep analysis by comparing historical data, and identifying potential problems or optimization opportunities in production by establishing a prediction model; carrying out production scheduling based on a data analysis result, and carrying out optimal configuration on production line resources according to data analysis and production scheduling; the production strategy is optimized on the basis of the data analysis result in combination with production line production planning, actions are executed through the executor according to the control strategy, production line automatic control is achieved, and the effects of improving production efficiency, reducing cost and promoting intelligent development of the manufacturing industry are achieved.

The invention discloses a thermal load prediction method, system and equipment based on multivariate input and a storage medium, relates to the technical field of heat supply control, and is used for solving the technical problem that multi-step prediction of thermal loads is realized by adopting multivariate time sequence data in the prior art. According to the technical scheme, the method mainly comprises the steps of obtaining multivariate time sequence data of the heat supply system; establishing a data set based on the multivariate time series data, and performing normalization processing on the data set to obtain a normalized data set; the normalized data set comprises a training set; a thermal load multi-step prediction model is constructed; the thermal load multi-step prediction model comprises a sequence-to-sequence model, an encoder, a decoder and a time sequence attention mechanism; the input data of the thermal load multi-step prediction model comprises a training set; and obtaining thermal load multi-step prediction data by using the thermal load multi-step prediction model. According to the method, the multi-step prediction of the heat supply system heat load data is realized by using the heat load multi-step prediction model.

The invention discloses an electric cylinder long shaft piece three-dimensional position adjusting and supporting device, and relates to the technical field of electric cylinder assembling, the electric cylinder long shaft piece three-dimensional position adjusting and supporting device comprises a rack, two sets of supporting mechanisms used for supporting an electric cylinder long shaft piece are movably arranged on the rack side by side, and the distance between the two sets of supporting mechanisms can be adjusted; the supporting mechanism is movably provided with a supporting frame, the supporting frame is provided with a clamping and supporting station used for clamping the long shaft part of the electric cylinder, and the supporting frame is provided with a moving executing mechanism used for adjusting the clamping and supporting station in the X direction, the Z direction and the Y direction; the moving execution mechanism comprises an X-direction moving execution assembly, a Y-direction moving execution assembly and a Z-direction moving execution assembly. The clamping and supporting stations of the two sets of supporting mechanisms are matched to drive the electric cylinder long shaft part to be subjected to position adjustment in a three-dimensional space, adjustment of the central axis of the part in the electric cylinder part assembling process is facilitated, and the assembling efficiency and precision are improved.

The invention discloses a maintenance personnel operation behavior monitoring method based on artificial intelligence, and relates to the technical field of artificial intelligence. In order to solve the problems that the current locomotive depot personnel behavior monitoring is mainly performed by manual monitoring and cannot give an alarm at the first time when an accident occurs, so that hidden dangers are caused to safety production, and video monitoring personnel often face relatively high working pressure. Through the face recognition and human body posture estimation technology, the system can accurately recognize the identity and the position of the maintenance personnel and monitor the behaviors of the maintenance personnel in real time, the workload of the monitoring personnel is relieved, the behaviors of the maintenance personnel are automatically recognized through the OpenPose algorithm, manual monitoring is not needed, the monitoring efficiency and the automation degree are improved, powerful support is provided for maintenance safety management, and the system is suitable for popularization and application. The system can effectively improve the monitoring efficiency, reduce the safety risk, improve the safety awareness, and promote the scientific and intelligent development of the maintenance safety management, thereby effectively preventing and controlling the safety accidents, and improving the maintenance safety management level.

The invention provides a fee deduction method, system and device of an intelligent traffic system and a medium, and belongs to the technical field of intelligent traffic. The method comprises the following steps: acquiring a vehicle image, vehicle driving information and traffic flow information at a preset position of a traffic system; the vehicle image comprises a license plate image of a vehicle; identifying and analyzing the vehicle image by adopting a deep learning algorithm to obtain license plate number information and vehicle type information; predicting a driving path and passing time of the vehicle based on the historical driving data of the vehicle; dynamically calculating a fee deduction amount according to the driving path, the passing time and the real-time traffic flow information of the vehicle; and sending the deduction amount to the mobile terminal of the user, and providing payment service. According to the invention, the intelligent identification system is constructed, the vehicle information is obtained in real time, dynamic fee deduction is carried out in combination with various factors, and accurate and reasonable traffic and parking management is realized.

The invention discloses a fault diagnosis method based on time sequence characteristic linear modulation intelligent oversampling, and the method supplements the details of a high frequency band through the improvement of the signal sampling rate, and improves the fault diagnosis performance. The method comprises the steps of obtaining an original vibration signal; an original vibration signal is preprocessed, and a low-sampling-rate signal is obtained through down-sampling. And pairing the low-sampling-rate signal and the original vibration signal for intelligent super-sampling network training. And a super-sampling rate model is constructed, and prediction output of super-sampling rate signals is realized. And constructing a fault diagnosis model, and learning features of signal fault classification. And inputting the training set into a super-sampling rate fault diagnosis model to train the model. And inputting a vibration signal into the trained model to obtain an oversampling rate signal, and classifying the oversampling rate signal to obtain a health state type of the oversampling rate signal. The fault diagnosis model provided by the invention better solves the problem that the diagnosis performance is poor when facing low-sampling-rate signals, and meanwhile, the high-frequency information of the signals is relatively truly recovered.

A remote automatic control system of a ship loader comprises a laser scanning modeling module, a dynamic data monitoring module, a digital twin simulation module, a safety anti-collision module, an automatic control module, a remote operation module, a video module, a voice module and a material level height detection module. The laser scanning modeling module and the dynamic data monitoring module comprise a plurality of peripheral sensing devices, and the peripheral sensing devices obtain data signals for a first recognition target, a second recognition target and a third recognition target and upload the data signals to the digital twin simulation module; by performing remote automatic system integration on the ship loader, continuity and high efficiency of ship loading operation can be realized, loading and unloading time can be greatly shortened, the throughput of a port can be improved, the ship loader can autonomously complete the ship loading operation at the automatic operation rate of 80%-85% through remote automatic research, the links of manual operation are greatly reduced, and the working efficiency of the ship loader is improved. And the manual intervention rate is lower than 15%, so that the working efficiency is improved.

The invention discloses an expandable power distribution network grid structure with high power supply reliability, and relates to the technical field of power distribution networks, the expandable power distribution network grid structure comprises a simple windmill type networking structure, and the simple windmill type networking structure comprises a three-station three-group single-ring network structure and a four-station four-group single-ring network structure. The power supply reliability is remarkably improved, power failure events caused by power grid faults are reduced, the expansibility of the grid structure enables the power grid to flexibly adapt to future load increase and power supply changes, diversified application scenes and investment level selection can meet the requirements and budgets of different users, and the power grid structure is suitable for popularization and application. The design of the grid structure considers the environmental adaptability, the influence of reasonable distribution on the geographical environment and space resources, the modernization and intellectualization development of the power distribution network is promoted, and a foundation is laid for the construction of a digital and intelligent power system.

The invention discloses an adjusting system for controlling the rotating speed of an automobile engine, and the system is characterized in that the system comprises a data collection module which collects the conventional operation parameters of the automobile engine in real time, the conventional operation parameters comprise the rotating speed, the torque, the air inflow, the oil temperature and the oil pressure, and also collects the acceleration, the gradient information, the external environment temperature and humidity of a vehicle and the driving behavior data of a driver; such as accelerator pedal depth change rate and brake frequency; the data analysis module is connected with the data acquisition module and is used for deeply mining a relationship and a potential rule in data by applying a long short-term memory (LSTM) network algorithm in deep learning and a fusion analysis method based on a physical model, identifying a real-time running state and a future change trend of the engine and intelligently diagnosing potential fault hidden dangers; the fuel injection amount and the ignition time can reach the optimal state through accurate rotation speed adjustment, fuel consumption is greatly reduced, fuel economy is improved, and expenditure is saved for users.

The invention belongs to the technical field of dredging engineering, and discloses a method for calculating a standardized drag head excavation model of a drag suction dredger, which comprises the following steps: determining a standardized drag head corresponding to the drag suction dredger based on water area position information of a current water area, and determining corresponding rake tooth information based on the standardized drag head; the rake tooth information and the water area position information calculate the mechanical ground breaking amount in unit time and a corresponding rake tooth ground breaking parameter table based on a mechanical ground breaking amount calculation model; on the basis of high-pressure water outlet characteristic information corresponding to the trailing suction dredger, the hydraulic ground breaking amount in unit time and a corresponding high-pressure water outlet characteristic table are calculated on the basis of a hydraulic ground breaking amount calculation model according to the high-pressure water outlet characteristic information; the hydrological environment information is divided into dredging influence grades according to the dredging requirements of the to-be-dragged suction area through a fuzzy function; and a multi-factor coupling excavation yield calculation model is established, and the excavation yield of the drag suction dredger under the action of the standardized drag head is obtained by considering the environmental constraint of the dredging influence level on the mechanical ground breaking amount and the hydraulic ground breaking amount in unit time.

The invention relates to the technical field of machine control, in particular to a robot operation control method and system and a robot. The method comprises the following steps: acquiring spatial data of a breeding work ship; performing multi-source sensor network deployment according to the spatial data of the cultivation work ship to generate a multi-source sensor network; performing sensor data integration on the multi-source sensor network to generate cultivation work ship environment sensing integrated data; performing real-time environment updating on the cultivation work ship environment sensing integrated data to generate cultivation work ship real-time environment monitoring data; performing spatio-temporal data synchronization on the real-time environment monitoring data of the breeding work ship to generate environment synchronization data of the breeding work ship; and according to the cultivation work ship environment synchronization data, environment model construction is carried out to generate a comprehensive cultivation environment model. Through real-time environment sensing, dynamic path planning, intelligent feeding control and anomaly detection, the stability and the intelligent level of robot operation control are improved.

The invention discloses a high-standard bearing steel quality prediction model based on machine learning. The high-standard bearing steel quality prediction model comprises the following steps: 1) data acquisition: collecting on-site key process and component data having potential influence on the qualification rate and defects of high-standard bearing steel; 2) data preprocessing: processing the collected various process and component characteristic variables; 3) data division: dividing the data into a training set and a test set according to a proportion; 4) constructing a model: training a deep neural network model constructed by using Python through the training set; 5) performing model evaluation: performing evaluation comparison on the trained model through a test set, and taking the trained integrated learning model as a prediction model; and 6) quality prediction: importing the data of the steel to be tested into the trained model to obtain a prediction result. According to the method, a perfect enterprise digital process and a component acquisition system are constructed, the high-standard bearing steel quality is accurately predicted by utilizing a machine learning technology, and the accuracy rates of water immersion flaw detection qualification prediction and crack defect prediction are both greater than or equal to 80%.

The invention discloses a visual identification detection system based on rice seedlings, relates to the field of visual identification detection of rice seedlings, and aims to overcome the defects of a rice transplanter operation detection method, assist a driver in mastering rice seedling leakage information, reduce manual monitoring on a rice transplanter and the like by a rice seedling transplanting and seedling leakage detection product. An MCU control board is arranged on the surface of the computing mainboard, a power switch and a function switch are embedded into the left end of the computing mainboard, the MCU control board is connected with an RDK X3 through a UART asynchronous transceiving transmitter, the MCU control board is connected with a GPIO small lamp board, an RTK board is arranged on the surface of the computing mainboard, and the RDK X3 is connected with a 4G module through a USB. The seedling leakage monitoring system of the rice transplanter can improve the agricultural production efficiency, reduce the agricultural production cost, improve the agricultural product quality and promote the agricultural intelligent development, and is of great significance to agricultural modernization.

The invention provides a rockburst micro-seismic monitoring early warning report generation method based on a large model agent, and relates to the technical field of rockburst micro-seismic monitoring, and the method comprises the steps: uploading and initializing intelligent data based on a predefined daily report template and a TBM parameter file; calculating and converting intelligent stake numbers of the dynamic early warning area; real-time data uploading and intelligent integration based on a micro-seismic monitoring sensor; accurate stake number positioning and calculation of the position of the intelligent sensor; uploading and intelligently screening micro-seismic event data; automatically generating a microseismic event spatio-temporal distribution characteristic graph and a microseismic event evolution law graph driven by a large model agent; performing automatic analysis and early warning quantification on the micro-seismic event evolution law diagram by a large model agent; intelligent rock burst grade matching and judgment based on a rule knowledge base; automatic on-site rock burst picture uploading and analysis integration; generating and dynamically updating an intelligent daily report driven by a large model agent; and finally, an accurate and timely daily report with a uniform format is generated.

The invention provides an end-cloud collaborative agricultural condition image acquisition system based on 5G communication and machine learning, and relates to the technical field of end-cloud collaboration of 5G communication and machine learning. A data acquisition end is used for acquiring multispectral image data of a farmland to be observed, and compression preprocessing is carried out, so that the data transmission burden is reduced; the compressed image is sent to a data processing module, and the data processing module carries out denoising and feature extraction on the image, constructs a feature data vector and transmits the feature data vector to a cloud server; the cloud server analyzes feature data vectors by using a random forest algorithm model, judges crop types and classifies the crop types, and finally returns an analysis result to the data display module, and the data display module generates an instrument panel by using a Flask framework and visually displays agricultural condition data including key indexes such as crop types and coverage conditions. Data transmission is carried out through 5G communication to realize a terminal-cloud collaborative architecture, and efficient and real-time data transmission and processing are realized.