14532 results about "Thermal source" patented technology

The embodiment of the invention discloses a fire monitoring and early warning method and device based on big data analysis, and belongs to the technical field of data analysis. According to the embodiment of the invention, multi-source data such as satellite remote sensing heat source images, meteorological environment parameters, historical fire event records and geographic information raster data are integrated, a space-time associated dynamic monitoring system is constructed, and environmental anomaly symptoms before fire occurrence can be comprehensively captured. Wherein the space-time reference difference of different source data is eliminated through the space-time fusion processing, so that the heat source distribution, the meteorological change and the historical fire mode form multi-dimensional association under a unified geographic framework, the sensitivity and the reliability of early fire point identification are remarkably improved, and the subsequent accurate fire risk prediction is realized.

The invention belongs to the technical field of intelligent control of heating, ventilation and air conditioning systems, and particularly relates to an intelligent optimizing and energy-saving control method for a central air conditioner based on deep learning, which comprises the following steps of: acquiring operation data of a central air conditioning system in real time through an internet of things technology; the operation data comprises operation parameters of cold and heat source equipment, flow and lift parameters of a water pump, fan frequency parameters of a cooling tower, temperature and humidity data of an air conditioner terminal, environment temperature and humidity data, weather forecast data and the like. Through deep integration of Internet of Things perception, deep learning prediction and a multi-objective optimization technology, the limitation of a traditional control framework is broken through, meanwhile, accurate prediction of building cooling and heating loads is realized through construction of a hybrid deep learning model, an optimization objective of a full life cycle perspective is established in combination with an equipment performance degradation model, and the system performance is improved. A federal learning framework is innovatively introduced into region-level energy efficiency management, and the model generalization ability is improved on the premise of ensuring data privacy.

The invention relates to the technical field of air conditioner energy-saving operation control, and discloses a central air conditioner energy-saving operation control method and system. The method comprises the following steps: acquiring and processing data through a temperature and humidity sensor to obtain a temperature and humidity distribution index; calculating load values according to the indexes and the personnel density to form a distribution diagram; regulating and controlling cold and heat source equipment according to the distribution map to obtain operation parameters; adjusting the water pump fan based on the parameters to form balance data; adjusting the end equipment according to the data to generate a control instruction; and the system state is adjusted in real time according to instructions and comfort feedback to realize energy-saving operation. The problem of energy waste caused by decentralized control of subsystems of a traditional central air conditioning system is solved. By constructing a complete closed-loop control system from environmental perception, load prediction to equipment regulation and control, the operation efficiency of the system under partial load conditions is improved. And meanwhile, refined control is achieved according to different requirements of different areas, and energy consumption is reduced to the maximum extent on the premise that the comfort level of the user is guaranteed.

The invention relates to an AI real-time intelligent guidance and self-adaptive obstacle avoidance method for multi-source sensing fusion of an unmanned aerial vehicle, and the method comprises the steps: collecting flight data through a visual sensor and an infrared sensor, obtaining a timestamp and space coordinate information, calculating a sampling frequency difference, and generating a fusion data set of time-space alignment through a self-adaptive interpolation algorithm; image and heat source features in the fusion data set are extracted, and a multi-dimensional feature set is generated, so that whether an obstacle exists or not is judged, and related information is extracted; and analyzing environment data, calculating an environment complexity index, generating a risk level in combination with obstacle information, predicting a dynamic weight of the sensor, and adjusting a working mode of the sensor. And then based on the obstacle condition, calculating a comprehensive risk value and predicting a motion trend through a path risk model, and finally generating an optimal obstacle avoidance path and a flight instruction, thereby realizing high-efficiency processing of sensor data, accurate risk assessment and real-time obstacle avoidance planning, and ensuring stable and safe flight of the unmanned aerial vehicle in a complex environment.

The invention relates to the technical field of computer hardware monitoring, in particular to a case internal temperature intelligent monitoring system which comprises a temperature acquisition synchronization module, an environment difference analysis module, a cold control response judgment module, a heat source positioning recognition module and an energy consumption load evaluation module. According to the invention, by performing uniform timestamp sorting and collection on the temperature data at different positions, the time consistency of multi-node temperature information is improved, high-precision dynamic tracking of temperature change is realized, the timeliness and integrity of a time sequence are ensured, and the accuracy of the temperature change is improved. The temperature difference between the nodes and the environment and the time distribution characteristics of the nodes support accurate identification and positioning of thermal anomalies, fan response opportunities are judged according to the peak value and fluctuation rate of the temperature difference, fan numbers and control paths are calibrated, the pertinence of response is enhanced, and the core position of a heat source is positioned through temperature difference space comparison of adjacent nodes. The fan rotating speed and current load change analysis establishes dynamic association between a heat source and energy consumption, and thermal anomaly and energy efficiency trend evaluation is realized.

The invention discloses a control method and device of a building cold and heat source system self-adaptive to load requirements. The control method comprises the following steps: acquiring historical operation data, physical characteristics and equipment system data of a building, and establishing a building load prediction model; generating the hourly load curve, and predicting the cooling or heating demand of each time period in the future according to the curve; dynamically adjusting the refrigerating or heating load demand, and generating a corresponding cooling or heating curve; and constructing a machine learning algorithm based on an equipment mechanism model, optimizing a control strategy of the cold and heat source system based on the historical operation data of the equipment, and outputting control parameters of the equipment. According to the technical scheme, the load of the building cold and heat source system is intelligently predicted and adjusted, the energy efficiency of the system is improved, invalid energy consumption is reduced, energy utilization is optimized, control parameter optimization is achieved through a machine learning algorithm, efficient operation is ensured, indoor comfort is kept, and the purpose of saving energy is achieved.

The invention discloses an intelligent heat supply regulation and control system based on digital twinning and deep reinforcement learning. The system comprises a physical layer, a control layer and a control layer, wherein the physical layer is a physical heat supply system composed of heat source equipment, a transmission and distribution pipe network and a user terminal; according to the digital twinborn layer, a virtual heat supply system mapped with the physical layer in real time is constructed, the virtual heat supply system comprises a multi-physics field coupling model based on the thermodynamics and fluid mechanics principle, operation data of the physical layer are collected through a distributed sensor network, and the state vector of the virtual system is dynamically updated; and the intelligent decision-making layer is integrated with a DRL intelligent agent, the state space of the DRL intelligent agent is defined as a virtual system state vector output by the digital twin layer, the action space of the DRL intelligent agent is a regulation and control instruction combination of heat source power and pump valve opening, and a reward function fuses an energy consumption penalty term, a room temperature comfort reward term and a pipe network stability constraint term. According to the method, global optimization, high-precision continuous regulation and control and collaborative balance are realized through deep collaboration of digital twinning and deep reinforcement learning.

The invention provides a multi-heat-source multi-material additive manufacturing optimization method and system based on machine learning, and relates to the field of additive manufacturing, and the method comprises the following steps: collecting process parameters, material physical properties and target response data in real time, and constructing a multi-source data set; on the basis, a multi-task learning model is trained, common features are extracted, target responses are synchronously predicted, a proxy target function is constructed through predicted values, if errors exceed a threshold value, experimental verification is conducted, and new data is fed back to update the model. And applying the weight coefficient to a multi-objective evolutionary algorithm, iteratively screening a Pareto candidate solution set, verifying a high-uncertainty solution, and updating the model to error convergence. And selecting and deploying an optimal parameter combination by using an entropy weight-TOPSIS method, and embedding an online learning mechanism to form closed-loop optimization. According to the method, multi-task learning and a self-adaptive evolutionary algorithm are combined, and efficient optimization of multi-heat-source multi-material additive manufacturing process parameters can be achieved.

The invention discloses an end-side cloud collaborative intelligent fire-fighting fire monitoring system based on image fusion, and relates to the technical field of intelligent fire-fighting, the system is composed of a plurality of functional modules, and the system comprises a multi-modal image fusion module which generates a dynamic scanning priority map based on prior data, distinguishes a natural heat source from an abnormal fire by using a dual-light fusion algorithm, and sends an image fusion result to a cloud server; a scanning area is divided according to the thermal risk grade, and the thermal imaging resolution is dynamically adjusted; the distributed edge computing module is used for carrying out space-time synchronization on cross-modal data through a multi-modal feature alignment network, and carrying out dynamic allocation on a CUDA core and CPU resources through adaptive computing scheduling; an improved artificial bee colony algorithm is adopted, the bandwidth of the multi-sensor data flow is dynamically allocated through a time-sharing multiplexing protocol, and three-dimensional path planning is carried out; and the end-side cloud collaborative decision module constructs a federated learning driven model sharing network, and each edge node trains a lightweight YOLOv5s pruning model based on local data.

The invention provides a power plant heat tracing system intelligent optimization method based on environment temperature fluctuation, and relates to the technical field of temperature control of a power plant heat tracing system, and the method comprises the following steps: S1, data collection and preprocessing: deploying a sensor array, collecting an operation data signal # imgabs0 # of a power plant heat tracing pipeline, carrying out the preprocessing of the operation data signal # imgabs1 #, and storing the preprocessed operation data signal # imgabs0 #; outputting a standardized feature vector; s2, model establishment and parameter optimization: constructing a physical-data dual-drive heat transfer model, and inputting standardized feature vector optimization initial parameters; compared with the prior art, the method has the following beneficial effects: firstly, control parameters can be automatically adjusted according to factors such as thermal load change, external environment temperature fluctuation and equipment aging; and secondly, the temperature trend in the future short time can be predicted by constructing a heat transfer dynamic model, the control strategy is adjusted in advance, the temperature is prevented from being out of control, heat dissipation can be increased in advance or heat source input can be adjusted in advance, and safe and efficient operation of the system is effectively guaranteed.

The invention relates to the technical field of fire early-stage recognition, in particular to a multi-source information-based conveying belt fire early-stage recognition and early-warning system, which comprises a sudden change detection module, a synchronous analysis module, a spatial trend recognition module, a coupling fluctuation screening module and a probability evaluation module. According to the invention, through multi-source information linkage acquisition, collaborative monitoring of parameters such as along-line temperature, smoke, gas, images, load and heat source temperature difference, combined characteristic analysis among parameters and synchronous response trend determination are driven, and active revelation of early risk hidden dangers and dynamic discrimination of spatial distribution consistency and fluctuation continuity are realized. Potential abnormal focusing locking under a high-interference complex working condition is promoted, collaborative fluctuation between a load and a heat source temperature difference further eliminates environmental noise influence, risk weight dynamic adjustment strengthens classification sensitivity of probability identification, fire risk clustering division promotes accurate mastering of distribution of tiny initial hidden dangers, and the probability identification accuracy is improved. And the reliability of fire early warning in a coal mine area conveying belt scene is obviously improved.

The invention provides a data center energy efficiency optimization method and system. A data acquisition node is arranged at the inlet end of a cooling unit to obtain refrigerant transportation data, the data are input into a path optimization model constructed based on a physical topological structure of the cooling unit, and a dynamic adjustment coefficient set containing heat exchange efficiency, phase change delay and pressure compensation parameters is generated. Meanwhile, the temperature difference generating capacity is converted into a driving parameter set based on the heat source distribution map by obtaining the surface waste heat of the cooling unit. And in combination with the dynamic adjustment coefficient set and the driving parameter set, the temperature gradient compensation amount, the phase change trigger threshold value and the pressure balance interval are determined, joint optimization is carried out, and a refrigerant flow rate control scheme adapting to machine room environment temperature fluctuation is generated. And energy efficiency optimization of the data center machine room environment is realized based on the refrigerant circulation parameters. According to the technical scheme provided by the invention, the energy efficiency optimization effect of the data center is remarkably improved by optimizing the flow speed control of the refrigerant.

The invention relates to the technical field of new energy automobile thermal management, in particular to a direct cooling and heating management system and method in an automobile power battery. The method comprises the following steps: acquiring vehicle real-time operation condition data and battery pack state monitoring data; working condition mode recognition processing is conducted according to the vehicle real-time operation working condition data, and vehicle working condition mode feature data are generated; performing thermal load demand analysis on the battery pack state monitoring data based on the vehicle working condition mode characteristic data to obtain dynamic thermal load demand data of the battery pack; and according to the dynamic thermal load demand data of the battery pack, refrigerant demand quantity calculation and analysis are carried out, the refrigerant supply capacity of the current direct cooling system is evaluated, and refrigerant supply and demand matching feature data are obtained. The specific requirements of three heat sources of the battery, the motor and the cockpit for the refrigerant flow are quantified, and a refrigerant distribution strategy is formed according to the proportional relation, so that the safety of the battery is improved, and the driving comfort and the working stability of the electric driving system are also guaranteed.

The invention relates to the technical field of data processing, and provides a multi-heat-source networking heat supply optimization operation method and system.The method comprises the steps that environmental parameters, heat source data, market dynamic information, user behavior characteristics and a pipe network topological graph are collected by deploying IoT equipment; acquiring a thermal load time sequence predicted value in a future preset time period; the heat source data and the pipe network topological graph are processed, and a pipe network operation state matrix is obtained; performing feature dimension alignment processing on the pipe network operation state matrix to obtain a pipe network spatial topology feature mapping value; performing weighted fusion on the thermal load time sequence prediction value and the pipe network spatial topological feature mapping value to obtain a final thermal load prediction value; and optimal operation of heat supply is realized according to the final heat load predicted value. According to the method, a real-time response mechanism for environmental parameters, market dynamics and user behavior characteristics can be realized, collaborative optimization of multiple heat sources can be realized, the heat source collaborative efficiency is improved, and energy waste and operation cost are reduced.

The invention relates to the field of grain drying, and discloses a grain drying energy-saving control system based on intelligent optimization, which comprises the following steps: after warehousing grains are detected, judging whether the water content of the grains exceeds a preset safety threshold value or not by collecting the initial water content of the grains and environment temperature and humidity information; according to the grain moisture content, the grain size distribution and the current environment condition, a multi-section type moisture change trend model fitting the drying process is constructed, and the decrease rate of the grain moisture content and the drying curve form in the future stage are predicted; analyzing the deviation value between the water content change slope and the target drying rate in the prediction curve; judging whether an adjustment potential exists or not by modeling a response relationship among the current hot air temperature, wind speed, heat source power and water content change; and according to the heat energy use analysis result, control instructions of hot air temperature, air speed and air supply modes are automatically generated. The self-adaptive energy-saving grain dryer has the advantage that the self-adaptive capacity and the energy-saving effect in the grain drying process are improved.

The invention discloses a wind power blade defect data automatic acquisition and analysis method based on an unmanned aerial vehicle, and particularly relates to the technical field of data acquisition and analysis. The method comprises the following steps: collecting blade surface heat distribution data, locally applying a heat source to a blade, collecting a temperature attenuation curve, calculating a heat diffusivity gradient difference value and a temperature attenuation time index, integrating a multi-parameter space, and classifying a heat distribution abnormal region into a normal region and a real defect region in combination with a deep learning model; cross validation is carried out through optical images and ultrasonic data, so that the detection accuracy is improved; dynamically adjusting a sensitivity threshold value and model parameters of the thermal imager according to a classification result, and generating a detection report; through data feedback iterative optimization model and task planning, the problems of misjudgment and missed judgment of the thermal infrared imager in complex material distribution are effectively solved, the accuracy, reliability and automation level of wind power blade defect detection are improved, and important support is provided for efficient wind power operation and maintenance.

A heat source inversion-based tunnel high-temperature heat hazard advanced geological detection method and system. The method comprises: by means of multi-view joint observation of "rock wall and drilling", extracting two kinds of collected temperature data to form a new sequence, carrying out inversion interpretation on the sequence, constructing a least square-based three-dimensional heat source inversion objective function and an inversion equation, and solving for the inversion equation; and on the basis of a solution result, forming a heat source and temperature field imaging result graph of high-temperature heat hazard detection within a detection area range. The present invention achieves effective positioning and capturing of a high-temperature heat source, high-temperature heat hazard level division and heat hazard risk evaluation, and provides a reference basis for determining a tunnel design and construction scheme.

The invention discloses a temperature monitoring method for a photovoltaic grid-connected cabinet, and particularly relates to the technical field of photovoltaic grid-connected cabinets. The method comprises the following steps: arranging multiple types of temperature sensors in a grid-connected cabinet, collecting real-time temperature data, constructing a heat distribution model, comparing a current heat map with a historical steady-state heat field template, and identifying heat field offset and an atypical temperature rise trend; when the system detects that early warning is not triggered by an existing measuring point in a heat abnormal area, redundancy alarm logic is automatically started, temperature abnormal early warning is generated, and the speculated abnormal heat source position is recorded; according to the method, the sensing capability of the thermal anomaly in the blind area of the sensor is effectively improved, and the safety and the intelligent level of the system are enhanced.

The invention discloses a multi-heat-source coupling heat pump control system for recycling waste heat of industrial circulating cooling water, which comprises the following steps: monitoring key parameters of various heat sources in real time through an environment sensing module, screening and preprocessing through a data processing unit, screening out a heat source set suitable for the current condition, and sending the heat source set to a control module; the recovery potential and the use efficiency of the heat source are evaluated by utilizing an adaptive learning model, the use priority and the distribution proportion of the heat source are intelligently determined in combination with real-time load change, and the working time and the switching time of the heat source are dynamically adjusted by adopting a heat source time window distribution mechanism. The operation parameters and the heat source coupling mode of the heat pump are optimized through the intelligent complementary adjusting module, the heat recovery path is dynamically adjusted through the autonomous temperature control unit, the scheduling strategy and the operation mode of the heat source are optimized through the self-adaptive feedback mechanism and the intelligent learning module, and the adaptability and the energy-saving effect of the system are improved. According to the system, efficient cooperative utilization of multiple heat sources is achieved, and the heat recovery efficiency and the system stability are improved.

The invention provides an indoor environment monitoring method and system integrating temperature and humidity, and is applied to the field of sensing data processing. According to the method, the reading difference of the temperature sensor is collected, whether local deviation exists or not is judged, the arrangement type of the sensor is combined, a low temperature value generated by the wall-leaning sensor due to the cold bridge effect is dynamically compensated, misjudgment of an environment system is avoided, and the human body temperature is introduced as a sensing reference by constructing a temperature difference deviation function. According to the method, comprehensive temperature indexes closer to real experience can be generated, when abnormal fluctuation is detected, the space structure and heat source information are further combined, boundary conditions are constructed, dynamic heat flow and temperature and humidity distribution diagrams are simulated, temperature sensing deviation is effectively and reversely corrected, the precision, stability and energy-saving performance of an environment adjusting system are remarkably improved, and the comfort degree of a human body is improved.

An electronic device comprises a heat source and a heat distribution structure coupled to the heat source to distribute heat generated by the heat source during operation of the electronic device.

The invention discloses a live-line work safety monitoring system based on artificial intelligence, and relates to the technical field of intelligent electric power safety, and the system comprises a data collection module which is used for collecting and preprocessing work data in a live-line work area in real time, and extracting live-line body point cloud data based on the work data; the finite element grid unit is used for dividing all charged bodies into finite element grid units and judging high-temperature charged bodies; and the violation judgment module is used for calculating the electric field intensity value of the position where the operator is located in real time. By combining an electric field, a temperature field and three-dimensional point cloud multi-modal data, comprehensive dynamic perception of an operation area is realized, finite element modeling and dynamic non-uniform heat source distribution analysis are adopted, a high-temperature charged body is accurately identified, the equipment fault early warning capability is improved, action characteristics of an operator are captured through a long-short-term memory network, and the working efficiency is improved. And the illegal behaviors are accurately judged by dynamically combining environmental data, and an optimal operation path is generated in real time by utilizing a three-dimensional grid division and dynamic planning algorithm.

The invention discloses a fire-fighting monitoring and generator set collaborative optimization multi-target constraint solving method, which comprises the following steps: carrying out real-time acquisition and structured processing on multi-source data, acquiring safety monitoring and equipment operation data through a sensor network, and storing the data in a distributed real-time database after preprocessing; carrying out dynamic thermodynamic field modeling and risk area division, discretizing a plant area as a grid unit, solving a heat conduction equation in combination with heat source modeling and boundary conditions, and dividing three levels of risk areas; building a multi-objective optimization model, defining three types of core constraints, building a normalized objective function, and realizing multi-objective decoupling through dynamic weight adjustment; according to an asymmetric load migration strategy, differential load adjustment is implemented according to an optimization result, an auxiliary system is linked, constraints are verified in real time, and closed-loop control is formed. According to the technology, the collaborative goals of safety risk controllability, system efficiency maintenance and equipment redundancy optimization are achieved, and the method is suitable for fire prevention and efficient operation in an energy production scene.

The invention relates to the technical field of heat load prediction, and discloses a heat supply system load prediction method and system, and the method comprises the steps: collecting multi-source sensing data in real time, and collecting outdoor meteorological parameters and building structure information; based on building distribution, a pipe network structure and user load characteristics of a heat supply area, a multi-stage heat supply load prediction model is constructed. And constructing a thermal topological graph model of the heat supply area based on the graph neural network. And periodically collecting parameters of the building-level edge prediction model, the heat exchange station-level aggregation prediction model and the thermal topological graph model, performing global aggregation optimization, and updating and optimizing each edge node model. And obtaining an edge prediction result according to the optimized model, and jointly controlling the heat source output power, the main pump rotating speed and the area valve opening according to the edge prediction result and the heat source level scheduling prediction model. According to the method, the depiction capability of the system on the dynamic load change and the space heat conduction path is improved, and the generalization capability of model updating and the real-time responsiveness of edge deployment are ensured.

The invention discloses an automatic control method and device for a thermal management system of an electric vehicle, relates to the field of intelligent control, and realizes multi-step prediction of battery and motor heat production and thermal load change in the future by fusing external information such as a navigation path and weather forecast, plans a thermal management strategy in advance, and improves the response perspectiveness of the system. A multi-source disturbance dynamic structured correlation equalization technology is adopted, the coupling relation between heat sources is modeled, cross disturbance is compensated through a joint state function, and multi-heat-loop cooperative control is achieved. On the basis of a model predictive control (MPC) framework, temperature tracking errors and energy consumption penalty are optimized, the energy consumption of actuators such as a compressor and a water pump is reduced while a safe temperature interval is met, and the endurance is prolonged. And in combination with a state estimator fusion model and observation data, the control precision and robustness under a complex working condition are enhanced, and finally global optimal thermal management considering the service life of parts, the comfort of passengers and the energy efficiency is realized.

The invention belongs to the technical field of intelligent manufacturing and laser arc hybrid welding, and discloses a double-robot laser arc hybrid welding real-time regulation and control method based on digital twinning. According to the method, double robots are used, a laser-electric arc composite heat source digital twinborn model is constructed, a Gaussian distribution laser heat source and a double-ellipsoid electric arc heat source are integrated, and the dynamic behaviors (molten pool flowing and keyhole stability) of a molten pool and the generation probability of defects (air holes, cracks and the like) are predicted. And a visual sensor and an acoustic emission sensor on the two sides of the welding robot are used for capturing molten pool oscillation frequency and internal defect signals. And in combination with a reinforcement learning algorithm, welding path offset self-adaptive compensation and laser arc energy ratio adjustment are achieved, and closed-loop real-time regulation and control are formed by controlling the position of a welding gun of a welding robot and technological parameters. Based on a digital twinborn model, robot welding guns on the two sides can conduct synchronous welding on the two sides and can be independently adjusted while coupling is guaranteed according to the characteristics of specific materials, and high-quality welding is achieved.

The invention provides a vehicle automatic sound production control method based on infrared recognition, which comprises the following steps: acquiring an infrared image in a preset range in front of a vehicle, recognizing a heat source target in the image, and judging whether the heat source target is an animal or a human according to the shape, size and temperature distribution characteristics of the heat source target; the prediction unit is used for predicting the moving track of the animal and the human in a period of time in the future according to typical moving track modes of the animal and the human so as to obtain predicted moving position and speed information, and transmitting the predicted information to the acoustic control unit; the acoustic control unit dynamically adjusts the sound pressure level and the frequency of the loudspeaker according to the received predicted moving information and the target type so as to generate an acoustic warning mode matched with the predicted moving position, the predicted moving speed and the target type; when the acoustic signal is sent out, the actual moving track of an animal or a person is continuously tracked, and the track prediction method is optimized by comparing the deviation between the actual track and the predicted track, so that the optimal avoidance effect is achieved.

The invention relates to the field of intelligent monitoring, and discloses an infrared identification-based equipment defect detection method, which comprises the steps of infrared thermal imaging data acquisition, noise reduction processing, nonlinear heat conduction modeling, high-order partial differential optimization and variational regularization inversion solution. An infrared thermal imager collects equipment surface temperature distribution data, after multi-scale wavelet noise reduction and boundary heat flow balance optimization, a non-linear heat conduction model is established by utilizing a temperature-related heat conduction coefficient, a heat diffusion boundary is optimized by combining a high-order Laplace operator, defect heat source distribution is inverted through a variational regularization method, and the defect heat source distribution is calculated. And geometric characteristics, thermal parameters and position coordinates of the defects are accurately extracted. The method can adapt to complex working conditions, realizes high-precision detection and characteristic analysis of equipment defects, overcomes the problems of noise interference, insufficient boundary processing, weak defect inversion capability and the like in the prior art, and is widely applied to fault diagnosis and health monitoring of industrial equipment.

The invention provides a multi-mode fusion gas station leakage-fire early warning method and system, and belongs to the field of gas system leakage and fire monitoring. The method comprises the following steps: firstly, acquiring infrared image data, pressure and gas concentration time sequence data of a gas station pipeline; secondly, the collected data are preprocessed; thirdly, an abnormal heat source image data set is manufactured, and a yov5 abnormal heat source monitoring model is constructed and trained and used for recognizing an abnormal heat source and calculating the fire symptom abnormal probability; and then, merging the fire symptom anomaly probability with a sensor data set, making a leakage-fire data set, and establishing a multi-mode gas station leakage-fire anomaly identification model for training and tuning. And finally, the trained multi-mode model is used for carrying out leakage-fire abnormity monitoring on the gas station, and corresponding early warning information is sent out according to an output result. According to the invention, gas leakage-fire joint abnormity discrimination can be realized, and the safety and early warning accuracy of the gas station are improved.

The invention discloses a cold and heat supply unit and process capable of deeply utilizing a heat source in the technical field of absorption refrigeration. The cold and heat supply unit comprises a refrigerating unit, a heating unit, a heat source flash tank and a high-pressure absorber. The refrigerating unit utilizes an external heat source to produce cold energy, the heating unit utilizes the heat source used by the refrigerating unit to produce cold energy so as to cool circulating water of the refrigerating unit, and the heat source flash tank utilizes the heat source discharged by the high-pressure absorber to heat; the absorption refrigerating unit is optimally designed, so that the absorption refrigerating unit can deeply utilize a heat source and supply cold and high-level heat energy at the same time, the problem that the heat source utilization rate of the unit is not high is solved, and the requirement for high-temperature heat energy in the production process is met; and low-grade waste heat is converted into cold energy and high-level heat energy, transition and upgrading of energy quality can be achieved, the unit has the cold and heat combined supply function, and the application scene is greatly expanded.