829 results about "Distribution matrix" patented technology

The invention discloses a welded pipe surface defect detection system, particularly relates to the technical field of metal pipe surface quality detection, and is used for solving the problem of defect misjudgment and missing detection caused by mixed reflection interference under single light source irradiation in the existing visual detection technology. An annular light source is adopted for time-sharing pulse triggering through a light source control module, reflected light images at different angles are synchronously collected, a feature extraction module constructs a diffuse reflection intensity ratio and mirror reflection angle distribution matrix to generate a three-dimensional reflection feature spectrum, and reflection anomaly characterization of a defect area is enhanced; the area positioning module screens candidate areas and inhibits interference by combining strength ratio circumferential deviation degree and reflection angle gradient change, and the distortion correction module compensates geometric distortion errors based on curvature radius and station movement parameters, filters pseudo defects of abnormal time sequence fluctuation, and improves the accuracy of the distortion correction. And the defect judgment module utilizes a gradient-gray scale space coupling classification model to distinguish the types of cracks, scratches and corrosion, and finally judges the defect authenticity in combination with reflection characteristic deviation vector superposition.

The invention relates to the technical field of welding defect identification, and discloses a welding defect identification method and system based on a molten pool image. According to the method, a multispectral high-speed camera is used for collecting a molten pool dynamic image sequence in the welding process, after multi-scale morphological filtering preprocessing is conducted, a fusion feature vector is extracted through a depth separable convolutional network, and then the fusion feature vector is input into a defect classification model adopting a heterogeneous graph neural network architecture to obtain a defect probability distribution matrix. Then, constructing a dynamic sparse optimization model to position defects, generating a defect space coordinate set, and finally, outputting welding defect types and position information through hierarchical verification framework processing. According to the method, the problems of welding image noise interference, complex defect characteristics and the like are effectively solved, the accuracy and reliability of welding defect identification are improved, and powerful technical support is provided for welding quality control.

The invention discloses a computing power load stabilizing and energy-saving management system based on a flywheel energy storage system, and belongs to the technical field of energy storage and management, and the system comprises a data collection module which is used for collecting and calculating a real-time current harmonic spectrum of a cluster and server rack vibration data to generate an electric energy quality characteristic spectrum; the prediction modeling module is used for establishing a prediction model output computing power demand dynamic distribution matrix based on multi-dimensional space-time correlation; the optimization control module is used for analyzing a peak load phase in the power demand dynamic distribution matrix to generate a dynamic coordination control strategy; and the power execution module is used for executing the dynamic coordination control strategy to adjust the phase parameter of the reverse electromotive force of the flywheel so as to offset the voltage harmonic component of the server bus. Harmonic phase compensation is realized through dynamic reverse electromotive force modulation, the calculation load prediction precision can be effectively improved, and the energy utilization efficiency and the equipment operation reliability are remarkably improved.

The invention provides an AI intelligent diagnosis method and system based on an intelligent system, and the method comprises the steps: collecting a building electromechanical equipment operation data flow containing multi-source heterogeneous data, such as a vibration spectrum, a thermal imaging characteristic and an energy consumption fluctuation parameter, through an edge calculation node, and carrying out the state time sequence coding to generate a coding sequence; and inputting the sequence into a fault prediction neural network to obtain a multi-dimensional fault feature distribution matrix. And then obtaining a standard diagnosis report of the expert knowledge base and an emergency diagnosis report of the field operation and maintenance record. And then a composite training objective function is constructed based on the result and equipment operation characteristic hidden space mapping, a fault prediction neural network parameter space is optimized through a back propagation algorithm, and a target fault prediction neural network is generated. And finally, fault prediction and diagnosis are carried out on the operation data flow of the building electromechanical equipment by using the network, so that comprehensive, accurate and intelligent fault diagnosis is realized.

The invention provides a photovoltaic intelligent sensing fault diagnosis method and system for an energy internet of things, and relates to the field of photovoltaic technology, and the method comprises the steps: deploying a multi-dimensional sensor network based on a star topology structure; a deep learning model based on a graph attention network and a bidirectional gating loop unit is utilized to extract space-time correlation features, and matching learning is carried out on the space-time correlation features and historical fault samples to generate fault feature mapping; constructing a fault diagnosis classifier by adopting a comparative learning method and optimizing the fault diagnosis classifier through a knowledge distillation technology, and classifying the fault feature mapping to obtain a fault type probability distribution matrix and a fault early warning level; constructing a multi-dimensional fault evaluation model based on Bayesian reasoning and time sequence correlation analysis, and evaluating fault credibility; and finally, constructing a fault evolution prediction model based on deep reinforcement learning, predicting the fault of the photovoltaic module in combination with the fault credibility and historical data, and generating a health assessment report.

The invention provides a multi-source heterogeneous distributed computing power fusion scheduling method and system, and the method comprises the steps: carrying out the multi-dimensional collection and preprocessing of heterogeneous scheduling source data, generating a scheduling joint data set, carrying out the key demand prediction processing of a task set based on a preset quantum channel topology, generating a key pre-distribution matrix, and carrying out the key pre-distribution matrix, and generating a real-time task queue according to a task set of the scheduling joint data set, and performing multi-target matching degree calculation processing on the key pre-distribution matrix and the real-time task queue based on a dynamic priority quantification model to generate a distribution decision matrix. Then adaptive key supplement and migration path optimization processing are performed on the allocation decision matrix to obtain a migration task set, and finally hidden node resource prediction and preset quantum channel topology updating are performed on the migration task set, so that the rationality of key allocation is improved, the problem of compatible conflicts of resource scheduling is solved, and the resource scheduling efficiency is improved. Therefore, the task migration success rate is improved.

The invention relates to the technical field of wind turbine generator control, and discloses a wind turbine generator control optimization method based on dynamic changes of wind speed and wind direction. According to the method, real-time wind speed time sequence data and three-dimensional wind direction vector field data of a target wind field are received, a wind field dynamic analysis model is constructed by using a space-time convolutional neural network, and a wind field energy density distribution matrix and a turbulence intensity probability graph are generated. And constructing a multi-target adaptive optimization model on the basis, generating a unit control parameter instruction set, optimizing a cooperative adjustment coefficient according to a preset unit load-power generation efficiency balance equation, and outputting an optimal control action sequence to a wind turbine generator master control system through Bayesian optimization framework iterative updating. The method can accurately sense the wind field change, effectively balance the unit load and power generation efficiency, realize multi-unit cooperative control, improve the wind energy capture efficiency, and improve the operation stability and economic benefits of the wind turbine generator.

The invention provides a construction method of a composite trauma medical record intelligent quality control system, and relates to the technical field of multi-modal data intelligent processing, and the method comprises the steps: obtaining trauma medical record data containing a text record and a medical image, generating an image-text confrontation sample based on a shielding recombination mechanism, and carrying out the reconstruction of the image-text confrontation sample; and mapping the text and image features to a unified feature space by adopting a cross-modal embedding method to generate semantic association features. And the system maps the semantic features with a preset quality control rule, constructs a clinical event atlas, identifies missing information through causal effect analysis and anti-factual reasoning, and generates quality control defect data. And then, constructing a risk propagation model according to the number of defects and criticality, forming a thermal distribution matrix and an evolution path, calculating a medical record risk score, and finally realizing quality control alarm output and data writing. The method has the characteristics of high universality, high timeliness and high intelligence, and is suitable for quality control system construction tasks in various complex data environments.

The invention relates to the technical field of temperature measurement, in particular to a multi-zone temperature distribution real-time calibration method based on a heating body, which comprises the following steps of: 1, etching on the surface of a metal alloy heating body of which the resistance temperature coefficient is greater than a preset material threshold value to form N discrete series resistance heating units which are electrically connected in series but thermally discrete; 2, establishing a resistance-temperature dynamic relation library of each discrete series resistance heating unit; 3, synchronously measuring the real-time resistance value of each unit when the heating body works; 4, reversely deducing the temperature of a unit center point through a unit exclusive relational expression according to the real-time resistance value, and reconstructing a continuous temperature distribution matrix on the surface of the heating body in combination with a heat conduction equation; and 5, comparing the target temperature field with the real-time temperature field matrix, and adjusting the driving power of the corresponding unit in the out-of-tolerance region. Through continuous calibration and temperature adjustment, accurate control of a temperature field can be maintained in different use environments.

The invention relates to an inverter IGBT junction temperature prediction method and related equipment, and the method comprises the following steps: generating a loss characteristic spectrum through collecting IGBT electrical parameters and analyzing switching loss; calculating surface heat flux density, and constructing a heat flux distribution matrix. And performing thermal-electric coupling analysis by combining the two to obtain a junction temperature dynamic response sequence. Fourier transform is utilized to extract temperature fluctuation harmonic features, and a feature set is constructed; and extrapolating and predicting a junction temperature change trend based on the feature set, and outputting an IGBT junction temperature early warning signal through threshold judgment. The technical problem that the service life of a device is affected due to the fact that a large amount of loss is generated due to frequent switching actions in the working process of the inverter, the junction temperature of a chip rises and even temperature fluctuation occurs is solved.

The invention relates to the technical field of medical image processing and tumor treatment, and provides a tumor image boundary identification method and system fused with a fluorescence boundary, and the method comprises the steps: collecting a tumor dynamic fluorescence image sequence and breathing waveform data through a fluorescence imaging device; performing multi-scale noise suppression processing on the image sequence to generate a preprocessed image without artifacts; extracting fluorescence intensity distribution characteristics through a multi-layer characteristic weighted fusion network and generating an intensity distribution matrix; constructing a motion compensation model according to the breathing waveform data, and correcting the boundary displacement of the intensity distribution matrix; fusing the corrected fluorescence features and the tumor tissue texture features to generate an initial tumor boundary contour; iterative topological optimization is carried out on the initial boundary, and boundary discontinuity is eliminated; and mapping the optimized boundary to a space coordinate system of the laser treatment equipment, generating a boundary mapping model and updating the boundary mapping model in real time. The precision and safety of laser treatment can be improved, and damage to normal tissue is reduced.

The invention relates to the technical field of strategy analysis, and discloses an alpine grassland rapid restoration strategy analysis method, system and device and a medium. The method comprises the following steps: performing quality evaluation and restoration on an alpine grassland multispectral image to obtain a standard image set; spectral texture features are extracted, degradation classification is carried out, and a feature set and a score table are obtained; evaluating the degradation degree in combination with multi-dimensional indexes, and generating a distribution matrix and an index set; forming a repair scheme library and a parameter list through case analysis and parameter optimization; analyzing a restoration effect in combination with environmental data to obtain an index matrix and an evaluation table; and optimizing the monitoring data to obtain a strategy scheme and an adjustment parameter set. According to the method and the device, the degradation conditions of alpine grassland of different types and different areas are accurately identified, the restoration strategy can be dynamically adjusted according to the real-time monitoring data, and the restoration effect is improved.

The invention relates to the technical field of quantum communication security, and discloses a quantum key distribution secure communication system based on a quantum mechanics principle. The system comprises a quantum state preparation unit which controls a quantum emission device to output a quantum state sequence containing a preset polarization angle and a phase modulation photon group; a quantum channel feature extraction unit captures channel environment interference parameters and constructs a photon polarization distribution matrix; a quantum communication security evaluation unit receives the matrix, and calculates a channel security coefficient threshold through a quantum bit error rate analyzer; the quantum key dynamic optimization unit generates an optimized key segmentation strategy in combination with a security coefficient threshold and a historical key negotiation record; the quantum key rotation control unit switches quantum state modulation parameters according to a segmentation strategy and a time window; and the quantum node synchronization management unit monitors node clock offset and injects a calibration signal to realize key distribution time slot alignment. The system can dynamically cope with environmental interference, and improves the security and adaptability of quantum key distribution.

The invention discloses an intelligent adjusting method and system for self-adaptively adjusting a pillow type based on a sleeping posture of a user, belongs to the technical field of intelligent pillow type adjustment, and is used for solving the defects of the prior art in the aspects of support adaptability, adjustment accuracy, data monitoring dimension, interaction capability and the like of an intelligent pillow system. And better sleep experience cannot be provided for the user. The method comprises the following steps: acquiring a pressure distribution matrix of a head and physiological data of a user through a multi-mode perception sensor array; wherein the user physiological data at least comprises respiratory frequency information and cervical curvature information; analyzing the pressure distribution matrix and the physiological data of the user through the edge calculation chip, and identifying the current sleeping posture of the user; according to the current sleeping posture of the user and the cervical vertebra curvature information, adjusting parameters of the intelligent adjusting air bag pillow are determined; and according to the adjusting parameters, the intelligent air pump is controlled to adjust the height and rigidity of the to-be-adjusted air bag.

The invention discloses a visual target tracking method and system for an unmanned aerial vehicle, and relates to the technical field of image acquisition and processing.The method comprises the steps that firstly, a bridge area is scanned in a directional mode in multiple different sun incident angle periods (the interval is larger than or equal to 30 degrees), and stain interference is eliminated through natural light multi-angle changes; the geometric stability of the crack edge is quantified by constructing a gradient direction distribution matrix of the time dimension; secondly, dividing multiple groups of scanning data into at least six direction groups, calculating pixel proportion variances group by group, accumulating the pixel proportion variances into a total variance parameter, and effectively distinguishing cracks from surface stains by combining a direction consistency verification mechanism (the total variance lt is judged as cracks by a preset threshold value); and finally, establishing a dynamic feedback closed loop based on a historical variance parameter sample library, adaptively adjusting a threshold value through a moving average value deviation ratio formula, and solving the problem of false detection caused by material aging and environmental fluctuation.

The invention relates to the technical field of fatigue evaluation, and discloses an offshore wind turbine blade frequency domain fatigue evaluation method and system based on an MIMO nonlinear dynamic system, and the method comprises the steps: collecting the load data of an offshore wind turbine blade; preprocessing the load data; an MIMO nonlinear dynamic system is established; establishing a dynamic mapping relation between the preprocessed load data and the frequency domain response by using an MIMO nonlinear dynamic system; decomposing the time domain dynamic response data by using a frequency domain decomposition method, and quantifying the influence weight of each frequency band load on the blade stress based on a frequency domain energy distribution matrix to obtain a frequency domain energy distribution function of the total stress; and calculating the fatigue damage of the blade through a frequency domain fitting optimization method based on the frequency domain energy distribution function of the total stress. The method solves the problems that fatigue damage evaluation confidence is insufficient under the multi-load coupling effect and actual engineering requirements are difficult to meet in the prior art, and has the characteristic that the coupling effect of the wind load and the wave load can be accurately processed.

The invention discloses a thunder and lightning early warning method based on atmospheric electric field networking monitoring. The method comprises the following steps: collecting multi-point electric field intensity data in a region in real time through a high-density electric field sensor network, and constructing an electric field intensity distribution matrix; for the constructed electric field intensity distribution matrix, space-time evolution characteristics of lightning activities are extracted, and lightning occurrence probability distribution is generated; acquiring temperature, humidity and air pressure parameters from multi-source meteorological data, performing data fusion with the generated thunder and lightning occurrence probability distribution, and constructing a comprehensive environment characteristic data set; and for the constructed comprehensive environment characteristic data set, calculating a comprehensive risk index, and if the index exceeds a preset thunder and lightning triggering threshold, etc. According to the lightning early warning method, the distribution matrix is constructed through the high-density electric field network, the spatial-temporal characteristics are extracted to generate the initial probability, and the physical rationality and the environment false alarm resistance of early risk identification are remarkably improved through fusion with the multi-source meteorological data.

The invention provides an outer wall leakage point positioning detection method and system based on infrared thermal imaging, and relates to the technical field of computer vision, and the method comprises the steps: firstly collecting a multi-period infrared thermal imaging data set of a target building outer wall, covering a surface temperature distribution matrix at different environment temperatures, and then carrying out the dynamic temperature correction processing, the method comprises the following steps of: forming a standardized temperature distribution set for eliminating the influence of environment temperature fluctuation, extracting a thermal characteristic parameter set containing the characteristics of temperature gradient distribution and the like from the standardized temperature distribution set, calling a pre-trained thermal anomaly detection model to carry out leakage risk probability mapping on the thermal characteristic parameter set, and generating a leakage point probability distribution diagram; and finally, performing region segmentation processing according to the leakage point probability distribution diagram and a preset dynamic judgment threshold value, outputting a leakage point positioning coordinate set and a corresponding leakage strength grade identifier, and realizing accurate positioning and strength grade identification of the leakage points of the outer wall.

The invention discloses a double-force-control automatic compensation method and double-force-control grinding and polishing integrated device.According to the technical scheme, the double-force-control automatic compensation method comprises the steps that workpiece surface crystal orientation data are obtained through a diffraction pattern, and a crystal orientation distribution matrix is generated through denoising and clustering analysis; dividing a crystal orientation region and associating the friction coefficient, hardness and other mechanical properties; based on the material removal rate distribution matrix, a gradient algorithm is adopted to correct grinding tracks and cutting parameters of all areas, and a compensation track matrix is generated; and double-force-control feedback of normal force and tangential force is combined, the abrasive particle contact force and the feeding rate are dynamically adjusted, and a closed-loop compensation strategy is formed. Through crystal orientation partition optimization and double-force-control dynamic compensation, the problem that compensation is insufficient in the grinding process of the glass crystal is solved, and high-precision and self-adaptive surface machining is achieved.

The invention provides a split type direct current charging multi-split group charging and group control control system, and belongs to the technical field of split type direct current charging piles. The split type direct current charging system comprising a main control cabinet, a power distribution unit, a charging pile terminal and other hardware architectures is constructed; a voltage monitoring unit, a current sensor module, a harmonic filter and other multi-dimensional sensor networks are integrated to acquire power grid state data in real time, and a sliding time window method is used to carry out segmented analysis on the data to extract power grid characteristic parameters; establishing a multi-objective optimization model comprising a power balance equation and a power grid stability equation to calculate an optimal power distribution matrix, adopting a neural network to realize intelligent distribution and dynamic balance adjustment of the power of each charging pile terminal, and starting an emergency power redistribution mechanism to ensure stable operation of the system when the power grid is detected to be abnormal. The technical problem of unbalanced power distribution of the multi-pile charging system caused by power grid voltage fluctuation and harmonic pollution is solved.

The invention discloses a power distribution network dynamic multi-target regulation and control method and system based on graph-model driving, and belongs to the technical field of power system automation. The method comprises the following steps: generating a dynamic topological graph model with a time mark correction label based on an acquired power distribution network topological state and preset power grid graph model verification; in combination with the dynamic topological graph model and the electrical parameters, executing state estimation and security risk analysis, and generating a security risk space-time distribution matrix; based on the security risk space-time distribution matrix, generating a multi-target strategy set for simultaneously optimizing a network loss index, a voltage fluctuation index and a load balancing index; and executing the multi-target strategy set, collecting power grid state response data after execution, calculating a control effect index, and judging whether to iteratively update the dynamic topological graph model or not. The method is used for solving the problems that topology modeling is static, state estimation granularity is coarse, risk assessment lacks a spatio-temporal evolution mechanism and strategy closed-loop capacity is weak in a traditional method.

The invention provides an optimization method of a heat supply system, which comprises the following steps: acquiring a real-time heat source operation state, a power grid load, a user demand and an environment constraint condition of the heat supply system, extracting characteristic information of sudden cooling and equipment fault information, and determining a heat source priority and a response speed initial value; acquiring parameters required by a cost function corresponding to the adjusted heat source priority sequence, determining cost weights of different heat sources after adjustment through a linear weighting method, and outputting a cost optimization sequence; identifying a power grid load according to the cost optimization sorting and the environment constraint condition, and if the power grid load is lower than a preset threshold value, calculating a heat source output distribution initial scheme through a dynamic programming algorithm to obtain a preliminary output matrix; and according to the optimized output distribution scheme, summarizing the total heat source emission amount, and if the total heat source emission amount exceeds a preset threshold value, reducing the high-emission heat source output proportion through an iterative adjustment algorithm, and outputting a target output distribution matrix.

The invention relates to the technical field of artificial intelligence processing, in particular to a manufacturing and processing system based on artificial intelligence simulation control parameters. According to the manufacturing and processing system, three-dimensional space-time coding is carried out on vibration, temperature and deformation time-varying signals through a multi-mode sensing module; generating a composite feature body including an equipment rigidity distribution matrix, a thermal deformation gradient vector and a material residual stress tensor, and solving the problems of spatial-temporal asynchronization and physical field feature splitting of multi-source data; the parameter resolving module constructs a parameter incidence matrix based on a fractal neural network, combines dynamic constraint conditions of thermal deformation gradient vectors, performs multi-objective optimization on a rigidity distribution matrix and residual stress tensor, generates an initial parameter solution set for eliminating time-lag deviation, and breaks through the nonlinear coupling optimization bottleneck of a traditional static model; and the state dissociation module calls a historical wear feature library to construct a performance attenuation reference surface, and tool accumulated wear and instantaneous thermal deformation disturbance are synchronously inhibited through convolution operation and projection correction of a dynamic compensation factor.

The invention provides an all-silicon carbide double-sided heat dissipation module packaging method based on gradient thermal resistance optimization, and belongs to the technical field of electronic component packaging. Establishing a thermal resistance characteristic curve, and calculating a gradient thermal resistance vector to determine a heat flow path; constructing a heat dissipation temperature distribution matrix; obtaining thermal diffusion data; heat dissipation path thermal resistance is calculated by combining the thermal diffusion matrix and the temperature distribution matrix, and a thermal resistance sparse matrix is established and is dimensionally reduced into a thermal resistance low-dimensional matrix; calculating an optimal heat dissipation path parameter set; inputting the parameter set into a thermal resistance prediction deep attention network model for verification and fine tuning, and generating a final thermal resistance distribution scheme; a module structure and a material ratio are designed according to the scheme to form a sandwich structure; a silver paste sintering process is adopted to realize low thermal resistance connection and complete packaging, and the technical problems of non-uniform thermal resistance distribution and difficulty in optimizing a heat flow path in a double-sided heat dissipation structure of a silicon carbide power module in the prior art are solved.

The invention relates to the technical field of machine tool intelligent monitoring, and discloses a numerical control machine tool intelligent monitoring and optimizing system based on digital twinning, which comprises a data acquisition module used for carrying out discrete time working condition data acquisition and dynamic precision weight distribution on a numerical control machine tool to obtain a dynamic precision distribution matrix; the calculation module is used for calculating a geometric simulation precision grade and a physical simulation precision grade; the construction module is used for constructing a self-adaptive geometric grid model; the self-adaptive refinement module is used for carrying out self-adaptive refinement on a contact area for cutting force monitoring to obtain a cutter workpiece contact physical simulation model; the cooperative adjustment module is used for inputting the self-adaptive geometric grid model and the tool workpiece contact physical simulation model into a working condition conversion state machine for cooperative adjustment, and a self-adaptive precision simulation result is obtained. And the reliability and robustness of the system are improved.

The invention relates to a low-carbon park comprehensive energy system multi-agent collaborative carbon responsibility allocation method, which comprises the steps of collecting multi-source data of a park, and performing multi-energy flow optimization scheduling by taking the minimum total operation cost of a comprehensive energy system as a target; constructing a dynamic carbon flow distribution matrix by utilizing a bidirectional carbon flow dynamic correction model considering carbon flow transfer caused by an energy storage charging and discharging process; a load carbon responsibility decoupling distribution model is adopted to decouple the total load into a plurality of sub-load categories, and different carbon responsibility distribution coefficients are given to each category of sub-load basis; the method comprises the following steps: performing multi-agent collaborative optimization by taking each benefit party in a park as a game participant, taking a Nash bargaining as a collaborative optimization framework, taking maximization of utility gains of all participants as an objective function, and taking a Shapley value as a carbon responsibility distribution basis, and obtaining a carbon responsibility distribution scheme of each agent in combination with a user carbon responsibility distribution coefficient. Compared with the prior art, the method ensures fairness, efficiency and operability of a carbon responsibility allocation scheme.

The invention discloses a remote medical interaction method based on virtual reality, and particularly relates to the field of remote medical interaction.The method comprises the steps that by synchronously collecting multi-modal tactile data and transmission state parameters of a patient end, a joint feature vector is established, and the transmission delay of each modal tactile signal is predicted in real time. The method comprises the following steps: extracting sensitivity indexes of an operation stage and a tactile signal through a doctor end real-time image, calculating a reward value in combination with a double-branch regression network, updating a modal transmission strategy, and generating a dynamic priority distribution matrix; based on the matrix, optimizing a bandwidth slicing strategy and performing signal compensation by combining a real-time network state and delay prediction; in addition, switching and pathological mutation of surgical instruments are detected, priority distribution is dynamically adjusted, and efficient transmission and operation feedback of tactile signals are ensured.

The invention relates to the technical field of building environment temperature control, and discloses a temperature control equipment intelligent adjusting method and system based on machine learning, and the method comprises the steps: building a space thermal field sensing network, obtaining multi-dimensional temperature field data, and generating a temperature gradient distribution matrix; and the positions of cold and hot spots are calibrated by combining infrared temperature measurement data, an airflow path is predicted, and air supply parameters are optimized. Meanwhile, building heat capacity characteristics are analyzed, and a thermal response dynamic model is constructed. And the air supply direction is adjusted for the local supercooling area. And based on the temperature change rate and the air supply parameters, the equipment start-stop time is optimized. Through real-time temperature sampling, continuous updating of temperature gradient distribution, cyclic execution of cold and hot spot calibration, airflow prediction and air supply optimization, accurate control of space temperature is realized. According to the invention, the accuracy and energy efficiency of building space temperature adjustment can be obviously improved, and an innovative solution is provided for an intelligent temperature control system.

The invention provides a photovoltaic power station cleaning robot path planning method, medium and system, and belongs to the technical field of photovoltaic power station operation and maintaining.The photovoltaic power station cleaning robot path planning method includes the steps that firstly, a photovoltaic panel grid division model is established, and stain distribution information is obtained through the image processing technology to construct a stain distribution matrix; secondly, calculating a cleaning water demand matrix based on stain distribution, and meanwhile, considering an influence relationship among vertical column grid units; determining the cleaning duration by combining the efficiency of the cleaning device; constructing a robot energy consumption model; calculating an optimal cleaning path by applying a resource optimization distribution function; integrating the environmental parameters and inputting the environmental parameters into a photovoltaic cleaning neural network model to predict the actual cleaning difficulty; a cleaning strategy is dynamically adjusted accordingly; and executing the final path and recording data to update the related model. According to the method, through real-time sensing and analysis of the stain distribution characteristics, intelligence of path planning of the cleaning robot is realized, and the technical problem that the cleaning robot cannot perform intelligent path planning according to the surface stain distribution characteristics of the photovoltaic panel is solved.

The invention relates to the technical field of fault equipment detection, and discloses a power equipment fault detection method and system based on high-precision temperature measurement, and the method comprises the steps: scanning the surface temperature of power equipment through a high-precision temperature measurement device, obtaining a temperature distribution image, processing the temperature distribution image, and obtaining a temperature distribution matrix; carrying out gradient analysis and temperature difference identification on the temperature distribution matrix to obtain a hot spot candidate area; performing clustering analysis on the hot spot candidate region to obtain an initial hot spot region, and generating a static boundary by adopting a boundary tracking algorithm; correcting the static boundary according to the temperature change of each boundary point to obtain a hot spot area; and performing fault risk assessment according to the load data of the power equipment and the temperature data of the hot spot area. According to the invention, through temperature data analysis and region boundary correction, the hot spot region can be accurately identified, and in combination with a machine learning algorithm, the accuracy of power equipment fault risk assessment is improved, and safe and stable operation of a power system is ensured.