12071results about How to "Reduce losses" patented technology

The invention relates to the technical field of data processing, in particular to a decision-making method and system for multi-source information fusion. The method comprises the following steps: obtaining hyperspectral image data, visible light image data and infrared image data collected by an unmanned aerial vehicle, and collecting ground environment monitoring data collected by a ground sensor; the hyperspectral image data, the visible light image data and the infrared image data are subjected to image preprocessing, so that a standardized multispectral data set is obtained, and image preprocessing comprises geometric correction, radiation correction and atmospheric correction; and color and texture feature extraction is carried out on the visible light image in the multispectral data set, and appearance feature and growth density identification of crops is carried out so as to obtain morphological feature data. Through integrated analysis of multi-source data, abnormal growth of crops and potential pest and disease damage risks can be identified more accurately, so that engineers are assisted to make irrigation and fertilization decisions.

The invention relates to the technical field of image processing, and particularly discloses a remote sensing image super-resolution reconstruction method and system based on a prior diffusion model, and the method comprises the steps: carrying out the shallow feature information extraction of a low-resolution image, and obtaining a shallow feature map; the method comprises the following steps: performing multi-scale down-sampling and convolution fusion processing on a shallow feature map and a high-resolution image, and performing high-frequency information extraction and adaptive high-frequency enhancement processing on the processed feature map to obtain condition information; according to the method, deep mining of high-frequency information is carried out on a low-resolution image input as condition information, so that the high-frequency information in the low-resolution image gradually approaches a high-resolution image, and the reconstruction precision is improved; the noise prediction network model guides step-by-step noise removal in the back diffusion process, and the detail reduction capability and precision of natural disaster remote sensing image super-resolution reconstruction are improved.

The invention discloses an SCL field safety accident analysis and early warning method and system, a medium and a program product, and the method comprises the steps: carrying out the distributed cleaning and feature extraction of multi-source heterogeneous monitoring data, and obtaining a multi-dimensional time sequence feature flow; constructing a dynamic association network of a security state; identifying a key influence factor chain, and establishing a semantic analysis model; constructing a safety threshold system; evaluating the multi-dimensional time sequence characteristic flow by using a safety threshold system; when the multi-dimensional time sequence characteristic flow triggers early warning, performing tracing analysis by using a semantic analysis model to obtain a root event triggering the early warning, and simulating the development trend of the root event based on a preset evolution model; determining a predicted risk range and a risk type according to the tracing cause analysis result and the accident development trend; and the corresponding accident early warning scheme is matched in the preset database according to the risk range and the risk type, and the accident early warning scheme is sent to the monitoring terminal, so that the accuracy of judgment and classified early warning of different types of safety accidents is improved.

The invention discloses a quality safety optimization method and system based on a multi-modal vertical large model technology, and relates to the technical field of quality safety optimization systems.The method comprises the steps that quality safety related data are collected from a multi-modal data source and preprocessed; fusing data through features and decision levels; a vertical large model of an improved Transform architecture is constructed by using fusion data, a quality feature extraction layer and a safety rule embedding layer are provided, and adaptive learning rate and gradient clipping training are used; inputting the new data into the large model to analyze the quality safety condition, including defect prediction, risk assessment and problem early warning; and according to an analysis result, generating an optimization suggestion through rule reasoning and reinforcement learning. According to the invention, multi-modal quality safety data can be comprehensively collected and preprocessed. The vertical large model is high in pertinence, quality defects, safety risks and potential problems are accurately analyzed, the optimization suggestion module is combined with rules and reinforcement learning, suggestions with expected effects and priorities are generated, and the quality safety level and the resource utilization efficiency are improved.

The invention provides a multi-modal large model construction and operation method and system for reservoir dam safety. The method comprises the following steps: constructing a training set, wherein the training set comprises a single sample; multi-modal data as model input: sensor data arranged on the dam, historical hidden danger records, engineering technology documents formed in various stages of planning, design, construction and operation of the dam, information resources related to dam safety, geometric and material information of the BIM model, timestamps and spatial position information; the probability value of occurrence of the hidden danger event serving as the training label is obtained; the training set is adopted to train a multi-mode pre-training model based on a Transform framework; performing real-time prediction and early warning on potential safety hazards of the dam; and calculating the actual influence degree, the comprehensive risk score, the dynamically set early warning threshold and the emergency measure suggestion of the hidden danger event according to the probability value of whether the hidden danger event occurs or not output by the model. The scientificity and effectiveness of dam safety monitoring are comprehensively improved.

The invention discloses a charging pile operation safety management and control system based on artificial intelligence, and belongs to the technical field of charging pile operation safety management and control. A charging pile operation safety management and control system based on artificial intelligence comprises a data acquisition module, a physical environment monitoring module, a central processing unit, a safety evaluation model, a fault detection module, a risk warning and execution module, a multi-charging pile collaborative management module and a user interface. The problems that existing charging pile data collection and environment monitoring are not comprehensive, an intelligent safety evaluation mechanism is lacked, the fault detection and response efficiency is low, and resource allocation is unreasonable are solved. Precise monitoring and safety trend prediction of the operation state of the charging pile are achieved through comprehensive data collection and the safety evaluation model, an intelligent fault detection and grading early warning mechanism can rapidly recognize and respond to potential and existing faults, resource allocation is optimized through the multi-charging-pile collaborative management module, the overall performance is improved, and the system is suitable for large-scale popularization and application. The operation safety is ensured.

The invention relates to the technical field of transformer abnormity monitoring, in particular to a transformer abnormity monitoring method, device and equipment and a storage medium. The method comprises the following steps: acquiring multi-source state parameters of a transformer; carrying out time sequence load change analysis on the multi-source state parameters of the transformer, and carrying out deep correlation mining between features to generate a load-temperature change atlas; performing three-dimensional imaging scanning on the transformer, and marking a transformer assembly; performing center point space positioning calculation on the transformer assembly to obtain a plurality of assembly accurate position coordinates; three-dimensional form point cloud modeling is carried out on the load-temperature change atlas based on the precise position coordinates of the multiple assemblies, dynamic state attribute rendering is carried out, and therefore an operation state twin model is constructed; and performing multi-period state trend prediction on the operation state twin model, performing sequential serialization processing, and constructing a state trend prediction curve. According to the invention, efficient and accurate transformer abnormity monitoring is realized.

The invention discloses an internet data security protection method and system based on an intelligent algorithm, and relates to the technical field of security protection. During system operation, user behavior data are collected in real time through log recording, a sensor and an API interface, the real-time operation behavior of a user is monitored, the interaction mode and transaction behavior of the user on a platform are analyzed, and the security of the user is improved. Calculating and acquiring a user behavior safety coefficient Yhxw, a data transmission safety coefficient Sjcs and a risk protection response coefficient Fxfh, identifying potential fraudulent behaviors, performing dynamic risk assessment by combining user historical behaviors, a transaction amount, a geographic position and data of payment equipment, performing threshold comparison to obtain an assessment scheme, and during login and transaction, combining multiple identity verification means to obtain a risk protection result; according to the method, user identity verification is carried out, security of login behaviors is dynamically monitored, network traffic and platform logs are analyzed, abnormal access and malicious requests or DDOS attacks are detected in real time, and abnormal behaviors or attacks are automatically identified and prevented.

The invention discloses a distributed new energy storage optimal configuration method and system for a power distribution network, and the method comprises the following steps: building a load prediction model through employing a probability discretization method according to historical power utilization data and real-time weather data; according to load demand data output by the load prediction model, a multi-target optimization model is constructed by taking maximization of distributed new energy power generation utilization rate, minimization of distributed new energy storage energy loss and minimization of operation cost of energy storage and scheduling as optimization targets; according to the multi-objective optimization model, adjusting weight distribution of the three optimization objectives, gradually optimizing power distribution between new energy power generation and energy storage, and generating an optimal new energy complementation strategy; charging and discharging are adjusted according to the optimal new energy complementation strategy, the health state, the battery temperature, the number of charging and discharging cycles, the environment temperature and the load demand change of energy storage equipment are collected in real time, and new energy power generation and energy storage configuration is optimized by dynamically comparing multiple indexes under different energy storage configurations in combination with power grid frequency fluctuation.

The invention discloses a management system for real-time monitoring and early warning of mine coal and gas outburst parameters, and belongs to the field of safety production, and the management system specifically comprises the steps: collecting gas and ground stress data in a mine in real time through a gas sensor and a microseismic sensor, and carrying out the real-time monitoring and early warning of the mine coal and gas outburst parameters through a time sequence analysis and spatial data mining technology and a multi-scale coupling strategy; identifying the change rule and potential correlation of the crustal stress and the gas parameters; a comprehensive outburst analysis and prediction model is established, the coal and gas outburst risk level is evaluated, and a three-level early warning mechanism and an emergency plan are designed; the method can be flexibly adjusted according to actual conditions of a mine, coal and gas outburst accidents are effectively prevented and controlled, and the safety of the mine is improved.

Along with promotion of industry 4.0 and rising of intelligent manufacturing, an industrial decision support system plays a more and more important role in improving enterprise operation efficiency and market competitiveness. The invention provides an industrial decision support system based on big data and artificial intelligence technology, and aims to provide accurate decision support for enterprises through deep analysis and real-time processing of massive industrial data. According to the system, by integrating an advanced data acquisition technology, monitoring and data capturing of the whole process of industrial production, including but not limited to production equipment states, process parameters, quality control, supply chain information and the like, are realized. By using artificial intelligence algorithms such as machine learning and deep learning, the system can identify a mode and predict a trend from complex data and construct a corresponding decision model. In addition, a built-in operation optimization engine of the system can provide an optimal solution for production planning, inventory management and resource configuration based on a data analysis result. Through an intuitive user interaction interface, a decision maker can easily obtain key business insights and intelligent decision suggestions so as to make a quick response in a continuously changing market environment. The industrial decision support system not only can improve the production efficiency and the product quality, but also can help an enterprise to reduce the operation cost, avoid potential risks, and promote the enterprise to transform to intelligence and digitization.

The invention discloses a system for realizing crop intelligent identification based on a CLIP multi-mode method, and belongs to the technical field of crop identification, and the method specifically comprises the steps: collecting real-time images, text description and environment data of crops; performing image processing on the real-time image; a CLIP model is used for carrying out feature extraction on the image and text description, visual features are extracted from the image, semantic features are extracted from the text, the text description comprises crop types and symptom description of diseases and pests, the environment data and the visual features are fused, and a multi-modal feature vector is generated; the multi-modal feature vectors are classified by using a pre-trained classifier, specific crop types and pest and disease damage types are identified according to extracted multi-modal features, identification results are displayed to users through a visual interface, and information is fed back to a monitoring center; according to the method, visual, semantic and environmental features are fused, so that the accuracy of crop disease and insect pest recognition is remarkably improved.

The invention discloses a power distribution network overhead line risk refined early warning method and system. The method comprises the following steps: collecting meteorological time sequence data and power distribution network topology and line parameter data; and preprocessing the data, and constructing a prediction data set. And establishing a fault probability prediction model based on the meteorological data and the line parameters. Calculating the fault probability by using the mathematical model, correcting the model, training in combination with static topology and dynamic meteorological data, and constructing a fault probability network model. And analyzing the importance of the nodes and lines of the power distribution network, and evaluating the load loss proportion of the nodes. And carrying out normalization processing on the importance indexes. And comprehensively calculating the predicted fault probability, the node loss load and the importance index to obtain a branch risk parameter. Fine early warning is carried out according to a set early warning threshold value, and dynamic adjustment is carried out to adapt to the actual operation condition. The method has the advantages that comprehensive risk assessment is provided, and the accuracy of fault prediction and the timeliness of risk early warning are improved.

The invention relates to the technical field of fire hazard monitoring, and discloses a fire hazard remote monitoring and multistage early warning mechanism integration system which integrates a data acquisition module, a heat gathering map generation module, an image fusion module, a data calculation module, a risk prediction module, an early warning notification module and the like. Through an infrared temperature measurement unit, a smoke concentration acquisition unit and an image acquisition unit, the system can acquire temperature, smoke and image information of a key area in real time. A heat gathering map generation and image fusion technology is utilized, a fusion image is systematically generated, a fire scene and temperature distribution thereof are visually displayed, and the fire source positioning efficiency is improved. Meanwhile, the system analyzes the gradient change of the temperature, the heat accumulation area and the smoke concentration through a data calculation module, constructs a risk prediction model in combination with a machine learning algorithm, and achieves the refined prediction of the fire risk. And according to a prediction result, the early warning notification module automatically sends a corresponding early warning message to ensure rapid transmission and timely response of the early warning message. According to the invention, the intelligent, precise and efficient level of fire monitoring is improved.

The invention provides a live pig health monitoring method based on multi-source information fusion, and the method comprises the steps: collecting the sound data of a live pig in a natural environment, carrying out the classification and preprocessing of the sound data, forming a sound sample data set, inputting an LSTM neural network model, and carrying out the training, and obtaining a sound detection model; acquiring a live pig attitude image to obtain an attitude sample data set, and inputting the live pig attitude sample data set into the YOLOv5s model for training to obtain an attitude detection model; inputting live pig sound data collected in real time, preprocessing the data, and inputting the data into the sound detection model to obtain sound features; inputting live pig attitude images collected in real time into the attitude detection model to obtain attitude features; measuring the body temperature of the live pig by using an infrared temperature sensor, measuring the weight of the live pig by using a weight sensor, and fusing the sound feature, the posture feature, the body temperature and the weight to obtain a fusion vector; and inputting the fusion vector into a random forest classifier for decision making, and determining the health state of the live pig. According to the method, multi-modal data are fused, comprehensive evaluation and diagnosis of the health condition of the live pigs are facilitated, and the accuracy and robustness of the whole monitoring system are further improved.

The invention discloses a digital GIS equipment fault intelligent monitoring management system, and relates to the technical field of fault intelligent monitoring, and the system comprises an information retrieval module which takes attribute information as a constraint, and carries out the networking retrieval to obtain a sample fault event set; the event clustering module is used for obtaining a plurality of high-frequency fault event sets; the traceability analysis module is used for determining a high-frequency fault factor set and generating a high-frequency fault feature matrix; the fixed-point monitoring module is used for obtaining a real-time monitoring data set and constructing a monitoring data matrix; the similarity analysis module is used for performing similarity analysis on the high-frequency fault feature matrix and the monitoring data matrix to determine comprehensive similarity; and the fault early warning module is used for performing fault early warning on the target equipment. Through the method and the device, the technical problem that real-time and accurate monitoring and management are difficult due to the fact that the intelligent degree and the detection accuracy are insufficient in the prior art can be solved, and the technical effect of improving the automation degree and the detection accuracy is achieved.

The invention discloses a jet aerator with low energy consumption and high oxygenation efficiency. The jet aerator comprises a Venturi tube and a quick-pass rhombic tube which are of a split structure, multi-rod supports are installed on the inner wall faces of round pipes at the two ends of the venturi pipe and support tail hook bionic stirring blades, a double-end reverse screw is arranged in the venturi pipe, one end of a quick-pass rhombic pipe is connected with the venturi pipe, the other end of the quick-pass rhombic pipe extends into an air suction chamber, and the upper portion of the air suction chamber is connected with an air supply pipe. The end, away from the Venturi tube, of the air suction chamber is connected with a mixing cavity tube, the mixing cavity tube is connected with the front end of a throat tube, a divergent wing-like blade set and a special-shaped taper hole plate are arranged in the mixing cavity tube, and a grid tray scattering nozzle is arranged in the throat tube; compared with a circular pipeline, the quick-passing rhombic pipe can provide higher water conveying capacity; the gradual change spiral body design on the double-end reverse screw can generate vortexes and turbulent flows, the turbulent flow intensity of water flow is improved, the mixing and diffusion capacity of the water flow is improved, and therefore the oxygenation efficiency is improved.

The invention discloses a fresh commodity replenishment method and system, electronic equipment and a storage medium, and relates to the field of commodity management. According to the method, fresh food hot topics are obtained from social media, demand hotspots are determined according to the fresh food hot topics, weather influence factors are determined according to predicted weather data, and holiday influence factors and seasonal influence factors are determined according to the current date; determining sales influence factors according to the second sales data of the competitor and the first sales data of the target store; through a preset prediction model, according to the sales influence factors, the demand hotspots, the weather influence factors, the holiday influence factors and the seasonal influence factors, determining predicted demand quantities of various fresh commodities in a second preset time period after the current moment; and determining the purchase quantity according to the predicted demand quantity and the stock quantity of the target store, and determining a purchase scheme according to the purchase quantity and the supplier information. By implementing the technical scheme provided by the invention, the accuracy and efficiency of replenishment of fresh commodities are improved.

The invention relates to the technical field of geological disaster evaluation, in particular to a mine geological disaster risk comprehensive evaluation method considering spatial distribution characteristics. The method comprises the following steps: acquiring mine multi-source spatial data; performing mine geological disaster risk distribution analysis on the mine multi-source spatial data to generate a mine geological risk distribution map; performing spatial region potential disaster probability analysis on the mine geological risk distribution map to generate mine geological distribution disaster probability data; and performing high-complexity spatial region screening on the mine geological risk distribution map through the mine geological distribution disaster probability data to obtain high-complexity spatial region data. According to the method, multi-source data are integrated, spatial distribution characteristics are emphasized, active and inactive areas are distinguished, ecological and water body changes are analyzed, and the accuracy and comprehensiveness of mine geological disaster risk comprehensive evaluation are improved.

The invention relates to the technical field of part typesetting, in particular to a nesting typesetting mixed common-edge analysis method and system based on laser cutting. The method comprises the following steps: obtaining initial typesetting part structure data, and carrying out part common-edge relation analysis to obtain part common-edge relation data; common-edge path optimization is carried out according to the initially-typeset part structure data, and optimized part cutting path data is obtained; performing laser cutting simulation based on the optimized part cutting path data to obtain laser cutting simulation data; performing typesetting heating balance optimization according to the laser cutting simulation data so as to obtain optimized part typesetting data; and performing cutting waste rate evaluation on the optimized part typesetting data to obtain typesetting cutting waste rate data, and performing minimum cutting waste rate optimization on the optimized part typesetting data based on the typesetting cutting waste rate data to obtain optimal part typesetting data. The cutting efficiency and precision are improved.

The invention belongs to the technical field of fire sensing and detection, and provides a fire sensing method and related device for a double-layer cable tunnel structure, and the method comprises the steps: respectively collecting the temperature, smoke and image data of a double-layer cable tunnel, and carrying out the double-layer data linkage; respectively constructing a temperature distribution model, a smoke diffusion model and an image target detection model according to the collected temperature, smoke and image data; and performing temperature, smoke and image multi-modal data fusion based on the temperature distribution model, the smoke diffusion model and the image target detection model, and when the fused data meets a set fire threshold, determining that a fire occurs. Through the fire sensing and verification method aiming at the characteristics of the double-layer cable tunnel, scientific and reliable decision support is provided for improving tunnel fire safety management, the safety of personnel and equipment in the tunnel is ensured, and meanwhile, the standardization process of the fire sensing technology is promoted.

The invention relates to the technical field of fault detection, and discloses a remote electric energy meter fault detection and identification system and method, and the system comprises a data collection module, a data integration module, a data inspection module, a feature extraction module, a fault analysis module, and a fault alarm module. According to the method, multiple electric energy meter target data are collected and integrated into a corresponding data set, and a complete and ordered data basis is provided for analysis; abnormal values are recognized and replaced by calculating the average value and the standard deviation, the data quality is improved, and the accurate analysis result is guaranteed; key parameters covering multiple aspects are accurately extracted from a data set, and a powerful basis is provided for fault analysis; from metering, hardware and communication fault dimensions, according to comparison of extracted parameters and threshold values, fault types and positions are accurately judged in multiple directions; the fault alarm module remotely transmits an analysis result to a monitoring center, and generates an alarm message containing detailed information such as an electric energy meter number, a fault type, a parameter value, a threshold value and the like when a fault occurs, thereby facilitating timely maintenance by maintenance personnel.

The invention relates to an electric power meteorological disaster risk early warning method and system based on artificial intelligence. The method comprises the following steps: cleaning and preprocessing basic meteorological data including temperature, humidity, wind speed and precipitation to obtain a cleaned basic meteorological data set; on the basis of satellite cloud picture or radar image data, meteorological changes are modeled through a neural network model, assimilation features are obtained, and the basic meteorological data and the assimilation features are spliced to form a joint feature vector; and constructing a comprehensive disaster risk assessment model based on the meteorological factors, the power grid factors and the geographic information, carrying out training and multi-objective optimization on the comprehensive disaster risk assessment model, and carrying out real-time electric power meteorological disaster risk early warning by using the optimized comprehensive disaster risk assessment model. According to the invention, meteorological data, power grid equipment states and geographic information can be monitored in real time, meteorological disasters possibly suffered by power equipment can be accurately warned, and the safety and stability of a power system are improved.

The invention relates to the technical field of process adjustment, in particular to an automatic adjustment method and system for the drying process of traditional Chinese medicinal materials, and the method comprises the following steps: monitoring the moisture evaporation condition of the traditional Chinese medicinal materials in the drying process in real time through a weight sensor, calculating the moisture evaporation speed of the traditional Chinese medicinal materials; and analyzing the relationship between the moisture evaporation speed and the environmental condition, dynamically adjusting the temperature and humidity condition to match the moisture evaporation speed requirement, and generating dry environment basic data. According to the method, the moisture evaporation rate and temperature and humidity conditions are monitored in real time, the drying environment is dynamically adjusted, uniform removal of moisture is ensured, heat and humidity distribution analysis is combined, the temperature and humidity in the drying chamber can be accurately adjusted, uneven local drying is avoided, the moisture evaporation rate and temperature and humidity changes in the drying process are dynamically adjusted and optimized, and the drying efficiency is improved. And the risk of excessive drying or moisture residue is avoided, control parameters are adjusted according to the difference between water evaporation and the target drying process, and the drying effect stability is improved.

The invention discloses an unmanned ship navigation path intelligent optimization system and method based on ocean data, and particularly relates to the technical field of unmanned ship navigation. The accuracy of monitoring data of the sensor in a marine environment is continuously evaluated through a series of steps of preliminary calibration, data acquisition, filtering processing, abnormal marking, feature extraction and the like of the unmanned ship sensor, and real-time evaluation and time period division of the quality of the monitoring data are realized through comprehensive analysis of abnormal features of sound velocity and conductivity. The abnormal degree is predicted in the abnormal monitoring time period, and for the condition of high abnormal degree, a safety avoidance strategy is automatically set through a path planning system, and the route and the navigational speed of the unmanned ship are dynamically optimized, so that a risk area is avoided, the navigation precision and safety are improved, the reliability of sensor data is remarkably improved, and the safety of the unmanned ship is improved. The influence of data deviation on path planning is reduced, and the task success rate and the navigation safety of the unmanned ship in a complex marine environment are enhanced.

The invention discloses a customer voice analysis system based on a large model, and the system comprises a voice data collection and preprocessing module which is used for collecting and preprocessing customer voice data; the voice recognition module is used for recognizing the preprocessed customer voice data; the feature fusion module is used for extracting semantic features of the text data and fusing customer voice data features to generate voice text representation; the large model fine tuning module is used for performing fine tuning on the pre-trained starry fire cognition large model; the semantic analysis and emotion recognition module is used for processing the voice text representation, extracting semantic information, recognizing an emotion state and generating an analysis result; and the dialogue context management module is used for associating customer historical voice data, establishing dialogue context information and updating a voice analysis result. According to the method, the stellate fire cognitive large model and the improved quantitative low-rank adaptation are utilized, and the method has the advantages of high precision, low calculation cost and high intelligent adaptability.

The invention relates to the technical field of geological exploration, in particular to a geological exploration safety monitoring and early warning method and system, and the method comprises the following steps: carrying out the exploration region division based on a geological exploration data set, carrying out the risk assessment and density assignment of geological units in combination with the risk degree, and generating a geological risk density map. According to the method, through geological risk assessment and density assignment, exploration area division and risk assessment, and in combination with risk level judgment of geological units, safety monitoring and early warning in the geological exploration process are more refined and scientific, and by means of stress analysis simulation and terrain sensitivity analysis, the safety of the geological exploration process is improved. According to the geological disaster early warning method, the stress response of the geological unit under natural and artificial loads is revealed, the capability of identifying risk points and sensitive areas in an exploration area is improved, early warning is carried out on geological disasters in the early stage, the real-time performance and pertinence of geological disaster early warning are optimized, and losses caused by the geological disasters are reduced.

The invention relates to the technical field of intelligent building management, in particular to an intelligent building operation and maintenance management system and method based on a BIM platform. The method comprises the following steps: performing equipment connection topology network construction according to acquired target BIM building model data, and generating equipment physical connection topology data; performing automatic logic conflict elimination on the equipment physical connection topological data to generate equipment connection topological data; performing fault influence link analysis on the equipment connection topological data to generate fault propagation link data; performing fault severity evaluation on the fault propagation link data to generate equipment fault severity data; and performing intelligent operation and maintenance path planning according to the equipment fault severity data to obtain intelligent equipment operation and maintenance path data. Through fine division of the operation and maintenance equipment management space and analysis of the equipment fault propagation link, intelligent scheduling of the fault operation and maintenance task is realized, and the operation and maintenance efficiency and the resource allocation precision are remarkably improved.

The invention relates to the technical field of image recognition, in particular to a crop disease monitoring and hazard degree evaluation method. The method comprises the following steps: acquiring a crop image set; performing object growth stage image screening on the crop image set to obtain a crop growth stage image set; performing crop growth morphological feature extraction on the crop growth stage image set to obtain crop growth morphological feature data; performing virtual crop health visualization according to the crop growth morphological feature data to obtain a virtual crop growth health image set; acquiring crop growth environment data; according to the virtual crop growth health image set, crop stage growth vigor abnormity matching is carried out on the crop image set, and crop abnormal growth stage data is generated. According to the invention, through multi-dimensional information acquisition, staged feature extraction, combination of environment and internal disease monitoring, hierarchical risk assessment and dynamic adjustment of prevention and control strategies, the accuracy and effectiveness of disease identification are improved.

The invention belongs to the technical field of intelligent management, and particularly discloses and provides an unmanned aerial vehicle group collaborative fire extinguishing intelligent management system, which comprises a flame position analysis module, a fire scene image is acquired by screening target unmanned aerial vehicles, and a flame area is identified to determine a fire source position. The fire behavior evaluation module divides a fire scene area according to the position of a fire source, monitors the wind speed, the combustible density and the flame temperature, analyzes a fire behavior spreading degree index, and screens out a fire behavior spreading area. The launching path analysis module screens fire extinguishing unmanned aerial vehicles based on unmanned aerial vehicle functions, and analyzes flight paths in combination with fire spreading areas, maps and unmanned aerial vehicle position information of a fire scene. The fire extinguishing agent throwing module collects data such as vertical temperature distribution, analyzes the target height and throwing distance of the fire extinguishing unmanned aerial vehicle, judges whether the target height and throwing distance are qualified or not and conducts adjustment. And the coverage area qualification judgment module is used for collecting a put fire extinguishing agent image, analyzing a coverage area qualification degree index, judging whether the fire extinguishing agent is qualified or not and performing early warning.