2694 results about "Recognition algorithm" patented technology

The invention relates to the technical field of Internet of Things, in particular to an electric energy meter with a wireless communication module, which comprises a parameter acquisition module, a communication optimization module, an interference analysis module, a fluctuation evaluation response module and a load classification module. According to the invention, a mutual inductor captures a single-phase circuit signal, analog-to-digital conversion is combined to realize parameter quantization, interference is eliminated based on an active power identification algorithm, a structured parameter set is integrated, a wavelet packet decomposition parameter and a quantization threshold are dynamically adjusted according to a load rate, and spread spectrum factor configuration is adopted to improve channel efficiency. The method comprises the following steps: extracting key features by using wavelet packet characteristics; continuously monitoring voltage fluctuation by a sliding window; detecting and identifying an instantaneous interference event by a break variable; inhibiting noise by Kalman filtering and predicting a trend; and the performances of the system in the aspects of acquisition precision, transmission efficiency, interference immunity and detection sensitivity are enhanced.

The invention provides a fire early warning and intelligent fire extinguishing method based on image recognition. The fire early warning and intelligent fire extinguishing method comprises the steps that a camera network is used for covering a target monitoring area, flame and smoke characteristic parameters are input, noise is removed through image preprocessing, and image data are standardized. A heat source point is selected as a camera installation position in combination with a fire propagation mode, and layout is optimized. And calling image data, performing real-time analysis based on a dynamic flame sequence, extracting abnormal response, calculating a fire risk value by using a mode recognition algorithm, and generating peak fire probability data. And collecting real-time fire characteristic data, comparing the data with a risk value after filtering and noise reduction, and setting multi-level threshold values to generate an alarm result. And analyzing a false alarm reason, optimizing the position of the camera and the starting condition of the fire extinguishing device, and generating a fire risk and fire extinguishing efficiency report. According to the invention, the accuracy and timeliness of fire early warning can be improved, the false alarm rate is reduced, and the fire extinguishing efficiency is enhanced.

The invention discloses an on-line detection method for gas in transformer oil based on photoacoustic spectrum self-correction, and relates to the technical field of spectrum detection and power equipment state monitoring, and the method comprises the following steps: S1, emitting modulation laser with a specific wavelength to a gas sample released by the transformer oil through a laser, and exciting gas molecules to generate photoacoustic signals; and S2, detecting the photoacoustic signal by using a photoacoustic sensor, converting the signal into an electric signal, and outputting the electric signal. According to the online detection method for the gas in the transformer oil, the correction coefficient is adjusted in real time through a self-correction algorithm, errors caused by light path changes and sensor aging are effectively compensated, the precision and stability of long-term detection are remarkably improved, and the detection accuracy is improved by combining a gas spectrum database and a mode recognition algorithm. According to the method, accurate qualitative and quantitative analysis of various gases is realized, the problem of cross sensitivity of multiple components is solved, and the method also has temperature and pressure compensation functions and can automatically adapt to environmental changes.

The invention relates to the technical field of image processing, and discloses an industrial part alignment method and system based on visual analysis and a storage medium. The method comprises the steps that a three-view camera collects an industrial part image, and preprocessing is carried out through gradient magnitude local contrast enhancement to obtain an enhanced image; performing hierarchical feature extraction to identify edge contours and key control points to form a multi-dimensional feature set; and establishing a dynamic reference coordinate system based on the feature set to obtain a part space attitude matrix. And the attitude deviation is compensated through Z-axis offset and rotation coupling error analysis. Posture adjustment is decomposed into a plurality of sub-stages, an alignment track is optimized by adopting a variable speed planning strategy, and accurate alignment of the parts is achieved. The problems that multi-view visual information fusion is insufficient, a special recognition algorithm for geometrical characteristics of the industrial parts is lacked, and Z-axis offset and rotation coupling error compensation is inaccurate in the posture adjustment process are solved, and the precision and stability of alignment of the industrial parts are improved.

The invention provides a posture recognition algorithm for any object under a monocular camera and an application system, and the algorithm comprises the steps: S1, constructing a target three-dimensional model, carrying out the multi-view annular shooting image collection of a target, and generating a dense grid model through feature extraction, matching, posture calculation and a multi-view geometric method; s2, generating an image depth map, and predicting depth information of a target in a motion process based on a monocular image sequence; s3, extracting a target image mask, and generating a target area mask graph through an image encoder, a prompt encoder and a mask decoder; and S4, executing attitude estimation, performing attitude initialization, correction and screening by combining the three-dimensional model, the depth map and the mask map, and outputting a six-degree-of-freedom attitude result of the target. According to the method, the target is subjected to annular shooting modeling through the method based on multi-view geometry, the three-dimensional model of the target is generated, attitude estimation is achieved in combination with the image mask and the depth map, the generalization ability of an attitude estimation algorithm in an actual scene is improved, and the application range of the attitude estimation algorithm in the actual scene is widened.

Provided is a self-adaptive identification method for nonlinear dynamic parameters of a reducer, which belongs to the design field of a reducer. The method includes: modeling a harmonic reducer corresponding to a flexible joint as a concatemer of a rigid reducer and an elastic torsion spring, and carrying out dynamic theoretical modeling and parameter variable independence processing on the concatemer to form a dynamic equation for parameter identification; giving an optimized motion trajectory to each joint of a robot and controlling the robot to act accordingly, acquiring relevant data needed for parameter identification based on a built-in torque sensor and double encoders inside the joint; using an offline identification algorithm to accurately identify a plurality of dynamic parameters of a collaborative robot considering joint flexibility and friction, and obtaining a minimum parameter set.

The invention discloses an intelligent defect detection system and a self-repairing method for a flexible base material RFID tag, and belongs to the technical field of Internet of Things electronic device manufacturing. According to the system, a three-dimensional dynamic scanning system is constructed by integrating a high-resolution image acquisition module, a multispectral sensor array and a mechanical arm motion platform, and surface and internal structure characteristics of a flexible substrate are captured in real time. A defect identification algorithm based on deep learning is combined with a multi-scale convolutional neural network and a transfer learning technology, precise classification of 12 types of defects such as microcracks, conductive layer fractures and base material deformation is realized, and the detection precision reaches 99.2%. A dual-mode self-repairing mechanism is put forward, specifically, nano-silver conductive colloid is injected through a microfluid channel for the defects of the conductive layer, and 3D structure reconstruction is achieved through a controllable temperature field; for substrate damage, a photoresponse shape memory polymer patch is adopted, and molecular-level bonding repair is achieved after ultraviolet light activation. According to the scheme, the detection efficiency is improved by more than 5 times, and the radio frequency performance of the tag is recovered to 98.7% of the initial value after self-repairing.

The invention relates to the technical field of operation data analysis, in particular to a multi-dimensional operation analysis index automatic adjustment and optimization management method, which comprises the following steps of: connecting a plurality of service systems through a distributed data acquisition gateway, and constructing a standardized data pool based on a dynamic pattern recognition algorithm; calling a dynamic index calculation engine, performing multi-dimensional feature extraction on the standardized data pool according to a preset index generation rule, and generating an initial index set; inputting the initial index set into an optimization model, generating an index optimization scheme through an association constraint analysis module, and outputting an optimization index set; and establishing an index dynamic update link. According to the invention, the health state of the system is monitored in real time in the release process of each business index, rollback control is triggered based on the change trend of the health index, and the updating safety is improved.

The invention relates to the field of edge computing, and discloses an intelligent recognition algorithm model edge-end fusion type deployment method, which comprises the following steps: collecting original data from different sensors, acquiring data from various sensors in real time by edge equipment, and carrying out primary processing and screening on the data; the edge device operates a lightweight intelligent recognition algorithm, processes the data in real time and makes a preliminary judgment; an intelligent task scheduling system is designed, and the processing requirement of each task is evaluated in real time; when the edge equipment cannot independently complete a calculation task, data is transmitted to the cloud, and the cloud processes a complex intelligent identification task by using powerful calculation resources to generate a high-level reasoning result; and the cloud transmits the optimized model or processing result back to the edge device, and the edge device performs adaptive adjustment according to the feedback of the cloud to optimize the local processing capability. The method has the advantages of improving the response speed and the computing power.

The invention discloses a remote Internet of Things control management system and method based on fire fighting, and belongs to the technical field of Internet of Things. The system comprises an image recognition module which obtains a video stream of a fire scene through a monitoring camera, and carries out the real-time analysis and recognition of an image through image processing and a deep convolutional neural network; the gas recognition module is used for monitoring the gas concentration of a fire scene in real time and recognizing gas components by using a gas recognition algorithm; the data fusion analysis module is used for carrying out feature fusion on the data acquired by the image recognition module and the gas recognition module, and carrying out analysis and modeling on the fused data; the decision support module is used for performing fire risk assessment according to an analysis result of the data fusion analysis module; based on historical data and the scene model, performing simulation and prediction of a fire development trend; and the remote internet-of-things control module is used for providing data for checking fire scene conditions in real time, so that a user can remotely control all equipment and devices of the fire extinguishing system.

The invention relates to the technical field of track structure health monitoring, in particular to a track crack recognition and fatigue life prediction method and system based on deep learning and finite elements, and the method comprises the steps: collecting and preprocessing a track surface crack image, and obtaining crack image data; a deep neural network model is trained, and a corresponding track crack recognition algorithm based on deep learning is determined; identifying and extracting positions and geometric dimensions of the cracks; based on the geometrical characteristics and the material attributes of the track, establishing a corresponding track finite element model, and inputting the position and the geometric size of the crack into the track finite element model to generate and obtain a track model containing crack damage; based on wheel-rail rolling contact fatigue finite element simulation, simulating the mechanical response of a track structure, determining the fatigue life of the track in combination with the stress-strain distribution of the track, and predicting the development trends of cracks under different operation conditions; automatic and efficient identification of track cracks is realized, and the accuracy of fatigue life evaluation is improved.

The invention discloses a system for improving the applicability of an intelligent camera, and relates to the technical field of intelligent camera shooting. RGB, infrared and depth sensors are used for cooperative sensing, a dynamic inter-frame interpolation algorithm is used for complementing video data, target trajectory prediction is carried out based on a target motion vector analysis algorithm in combination with a recurrent neural network LSTM + Kalman filtering, and the applicability of the intelligent camera is improved. Meanwhile, three-dimensional scene modeling and shielding recovery are introduced, and environment information complementation of a dead angle area is optimized; through a cross-camera target identity re-identification ReID algorithm, seamless tracking is carried out among multiple cameras by utilizing appearance characteristics, gait characteristics and biological characteristics of a target, and meanwhile, PTZ camera control is optimized in combination with reinforcement learning, and the angles of the cameras are dynamically adjusted; a generative adversarial network GAN based on space-time consistency is adopted, historical video data and environment modeling are combined, missing pictures in visual dead angles are intelligently complemented, and the continuity and authenticity of monitoring pictures are improved.

The invention relates to the technical field of medical image processing, in particular to an engineered heart tissue pulsation trajectory automatic analysis method based on an image recognition algorithm, which comprises the following steps: S1, multi-modal image fusion: performing space-time registration and feature fusion on acquired multi-modal heart images to generate a fused image sequence; s2, cardiac muscle tissue segmentation: outputting a cardiac muscle tissue segmentation result with a timestamp; s3, motion track modeling: generating three-dimensional track point cloud data in a pulsation period; s4, feature parameter extraction: performing spatial-temporal feature analysis on the track point cloud data, and extracting multi-dimensional motion parameters; and S5, heterogeneity atlas generation: generating a cardiac pulse heterogeneity atlas according to the multi-dimensional motion parameters. According to the method, automatic analysis of the cardiac pulse track and generation of the heterogeneity atlas based on the multi-modal image and space-time modeling are realized, and the precision and the intelligent level of cardiac motion anomaly recognition are remarkably improved.

The invention discloses a method and a device for dynamically adjusting a focus in laser flight processing. The method comprises the following steps of: 1, acquiring a data stream; 2, according to a laser type matching recognition algorithm, recognizing the data flow to obtain a preliminary result, and filtering to obtain a current position; 3, calculating the deviation between the current position and the reference, judging whether the deviation reaches a preset threshold value or not, and if yes, recording the deviation as a compensation value; step 4, acquiring focus depth and power; the focus depth and the power are optimized according to the compensation value; 5, processing the focus depth and optimizing the power by adopting a track interpolation method to obtain an optimized path; 6, the optimized path is sent to a motion controller, and a correction position is output; 7, judging whether the deviation value between the correction position and the optimal plane is within a threshold range or not, and if yes, completing machining according to the correction position; and if not, the third step to the seventh step are executed again till machining is completed. According to the method, the problem of unstable processing quality caused by focus offset in laser processing can be effectively solved.

The invention discloses an unmanned aerial vehicle cruising method and system based on a deep learning artificial intelligence image recognition algorithm. According to the method, an unmanned aerial vehicle carrying an improved YOLOv7-SwinT target recognition model collects real-time image data of an inspection area, and the model fuses a single-stage target detection architecture of YOLOv7 and a visual feature extraction network of Swin Transform. Progressive target detection is realized by adopting a three-level recognition architecture, wherein the progressive target detection comprises primary anomaly detection based on lightweight CNN, intermediate accurate positioning in combination with an attention mechanism and advanced target classification of multi-sensor data fusion. And the system combines the electric quantity of the unmanned aerial vehicle, the environmental condition and the task priority according to the identification result, generates a dynamic inspection path through an adaptive path planning algorithm, and realizes multi-vehicle collaborative operation by using an intelligent task allocation algorithm. In the inspection process, sensor data are processed in real time through edge computing equipment, and charging scheduling is optimized by adopting an intelligent energy management system. The target recognition precision and the cruising efficiency of the unmanned aerial vehicle in a complex environment are remarkably improved, and the method is suitable for application scenes such as electric power inspection and security monitoring.

The invention relates to the technical field of multi-source information integration, and discloses an intelligent fire-fighting command method and system based on a multi-modal AI large model.According to the intelligent fire-fighting command method and system, fire scene data are obtained in real time through multi-terminal information collection, scene reconnaissance feedback is conducted through flame and smoke recognition and dangerous goods recognition through an image recognition algorithm, and the fire-fighting commanding efficiency is improved. A pre-trained multi-mode live restoration model is utilized to carry out restoration simulation on a fire scene, a multi-source live simulation model is generated to analyze and formulate a personalized rescue plan, positioning and state information of a rescue unit is collected in real time, a rescue unit simulation model is constructed, and specific rescue execution steps are analyzed according to the personalized plan. The steps help commanders to realize accurate commanding and efficient rescue, the rescue decision accuracy is improved, the response time is shortened, the resource allocation is optimized, the emergency response capability and the successful rescue rate of a fire scene are remarkably improved, and the problem that in the prior art, the efficiency is low when multi-source information is manually integrated to restore the fire situation is solved.

The invention relates to an aliasing image intelligent deformation small target identification algorithm based on detector multiplexing, and relates to the technical field of aliasing image target identification, and the algorithm comprises the steps: designing a detector multiplexing optical system; designing a view field coding element, and splicing the whole system; simulating and generating a motion track of the early warning target under the space-based background; the target motion trail is imaged through a detector multiplexing optical system; aliasing video simulation of moving target imaging is carried out; an intelligent deformation small target recognition algorithm is applied to the aliasing image, and a moving target is recognized; target resolution and field-of-view positioning are realized by using the relevance between the inter-frame track of the moving target and the light spot shape. According to the aliasing image intelligent deformation small target recognition algorithm based on detector multiplexing, light originally imaged on a large-area-array detector is folded and imaged on a small-area-array detector, and therefore the purpose that the small-area-array detector receives large-view-field imaging is achieved.

The invention discloses a leakage current monitoring and early warning system based on current monitoring of a new energy photovoltaic inverter, and belongs to the technical field of leakage current monitoring. The invention discloses a leakage current monitoring and early warning system based on current monitoring of a new energy photovoltaic inverter, and the system comprises a sensing module, a data collection and preprocessing module, a central processing module, a multi-stage dynamic threshold module, a complex mode recognition module, and a multi-stage early warning module. And real-time early warning cannot be realized, and a complex leakage current mode cannot be identified. According to the environment parameters monitored in real time and the working state of the inverter, the threshold value of the leakage current is dynamically adjusted, the complex leakage current mode is recognized by adopting a machine learning and mode recognition algorithm in combination with the comprehensive analysis result of the zero-sequence current, the phase and the frequency parameters, and the reliability of the inverter is improved. The multi-level early warning module can give alarms of different levels according to the degree of the leakage current.

The invention relates to the technical field of digital simulation, and discloses an emergency rescue method and system based on unmanned aerial vehicles and an urban three-dimensional platform, and the method comprises the steps: calling an emergency rescue region model through the urban three-dimensional platform, driving an unmanned aerial vehicle formation to go to an emergency region to obtain real-time region information, and employing a pre-trained accident condition image recognition algorithm to recognize an accident condition image; accident condition characteristics such as smoke and fire, fire fighting access blockage, emergency exit blockage, urban inland inundation, dangerous slopes, dangerous building detection, illegal building of building roofs, river channel floating object recognition and high-voltage electric tower inclination monitoring are identified, and the accident condition characteristics are substituted into the regional three-dimensional model to generate an accident simulation model. Real-time accident condition feedback is provided through the accident simulation model, rescue workers are assisted to make scientific rescue decisions, through comprehensive analysis and accurate simulation of an emergency rescue area, the detection efficiency of the unmanned aerial vehicle is improved, the timeliness and accuracy of rescue are enhanced, and the problem that in the prior art, the efficiency is low through manual collection of the current accident condition is solved.

The invention belongs to the technical field of intelligent traffic, and discloses a space-time fitting-based whole vehicle type traffic feature accurate recognition algorithm, which comprises the steps of obtaining driver behavior data, vehicle dynamic data and road supervision data; preprocessing the driver behavior data, the vehicle dynamic data and the road supervision data to obtain a driver behavior feature data set, a vehicle dynamic feature data set and a road supervision feature data set; according to the vehicle dynamic feature data set and the road supervision feature data set, obtaining a vehicle initial driving track; optimizing the initial driving track of the vehicle through an improved space-time interpolation algorithm to obtain a complete driving track of the vehicle; according to the complete driving track of the vehicle, the vehicle driving speed of each road section is calculated and compared with the limiting speed of the corresponding road section, and whether the vehicle is overspeed is judged; and traffic accidents caused by overspeed, fatigue driving and the like are effectively avoided.

The invention relates to the technical field of digital surveying and mapping of constructional engineering and construction quality control, and discloses a house surveying and mapping system for constructional engineering, which comprises a data acquisition module for synchronously generating a panoramic image signal and an inertial measurement unit signal; the preprocessing module receives a panoramic image signal and an inertial measurement unit signal, and fuses the panoramic image signal and the inertial measurement unit signal through a visual inertial odometer to generate a pose calibration signal; the fusion modeling module receives the purification point cloud signal and the pose calibration signal, and generates building component parameterized data based on a deep learning component recognition algorithm; the result output module receives the building component parameterized data and generates a construction deviation analysis report based on a preset tolerance threshold value; the intelligent control module generates an acquisition quality feedback signal through a real-time quality evaluation algorithm to the data acquisition module so as to adjust a scanning path, and sends a dynamic sampling rate control signal to the preprocessing module so as to optimize a calculation load. The problems of low house surveying and mapping efficiency, poor modeling precision and weak field real-time feedback are solved.

The invention discloses a humanoid robot somatosensory control system and method based on deep learning. The method comprises the steps that physiological data and environment information of a user are collected through a wearable device and an environment sensor, and full-modal synchronous collection is achieved in combination with audio and visual data. The system carries out noise reduction processing on the collected sound, extracts a voice instruction and recognizes non-language sound features to judge the physiological state of the user; and analyzing the posture and gesture intention of the user through a human body detection and posture recognition algorithm. And multi-modal data is further fused, the intention of a user is accurately judged in combination with scene context and a security verification loop, a multi-dimensional state space and an atomic action library are constructed, and a composite reward function is designed to realize intelligent decision and execution of robot actions. The system comprises a multi-modal data acquisition module, a user intention understanding module, a dynamic action planning module, a safety monitoring and exception handling module and a continuous learning and collaborative optimization module, and can effectively improve the intention recognition precision, environmental adaptability and task execution efficiency of the robot in a complex home scene. And intelligent and personalized family service support is provided for the elderly.

The invention relates to the technical field of building construction monitoring, and discloses a method for evaluating mechanical properties of a steel structure building construction whole process based on digital twinning. The method comprises the steps of establishing a digital twin model fusing multi-source information, and performing real-time linkage with a sensor network arranged on site. Collected data such as deformation, temperature and wind speed are processed through dynamic fusion and an anomaly recognition algorithm, model parameters are continuously corrected, and real-time dynamic high-fidelity mapping of the mechanical state in the construction process is achieved. And based on the updated model, the intelligent analysis module performs cooperative calculation, autonomously identifies construction abnormity, quantitatively predicts potential risks, generates a process optimization decision instruction and feeds back the process optimization decision instruction to a site. According to the method, a closed-loop regulation and control mechanism from data perception, model analysis to decision execution is constructed, and accurate online evaluation of construction mechanical properties and active prediction control of safety risks are realized.

The invention belongs to the technical field of face recognition, and particularly relates to a face recognition algorithm adaptive to illumination variation, which uses Gaussian filtering to reduce noise caused by illumination variation, convert a color image into a grey-scale image, reduce calculation complexity, perform adaptive histogram equalization, estimate global illumination conditions in the image and improve the face recognition accuracy. Calculating the main direction and intensity of illumination in the image, predicting the current illumination condition, and enabling the image brightness to adapt to different environment illumination; identifying a face region in the image, using a deep learning method to position face related points, using an LBP to extract local features insensitive to illumination variation, and using a deep convolutional neural network to extract global features; introducing a multi-illumination data set; the Euclidean distance is used for comparing the extracted feature vectors, the similarity between the extracted feature vectors and known faces in a database is recognized, according to the similarity score, threshold judgment is adopted for AQW to obtain a final recognition decision, and the method has the effect of being capable of accurately adapting to facial features under different illumination conditions in real time.

The invention provides a chromatography-mass spectrometry automatic integration method, system, equipment and medium for multiple sample items, and the automatic integration method comprises the following steps: converting an original spectrogram of a chromatography-mass spectrometry to generate a standardized spectrogram picture; performing peak recognition on the standardized spectrogram picture by using a trained deep learning peak recognition algorithm to obtain a candidate peak classification result; screening the candidate peak classification results based on the spectrogram classification result to obtain a preliminary integration result; performing peak clustering on the multi-sample items; carrying out peak recall on samples without peak reservation; and carrying out consistency correction on all reserved peaks. According to the method, deep learning model prediction is carried out on the chromatography-mass spectrometry image, peak clustering, peak recall and peak boundary inconsistency correction are carried out in combination with multi-sample data, manual judgment on the peak position and the peak shape of the chromatography-mass spectrometry can be replaced, and the method is high in accuracy, high in specificity and high in accuracy. And the false positive of the conventional automatic integration method is effectively reduced.

The invention provides an anonymization processing method and system for emotion data in a vehicle. The anonymization processing method comprises the steps of collecting multi-mode emotion data; correspondingly carrying out local preprocessing and multi-modal alignment processing on the multi-modal emotion data; corresponding sensitive emotion feature information in the preprocessed multi-modal emotion data is extracted through a lightweight recognition algorithm, and the sensitive emotion feature information at the recognized position is packaged in a unified mode; performing desensitization processing on various types of emotion modal data, and encapsulating and synchronizing desensitization results with labels; deep emotional feature extraction is performed on the image, the voice and the physiological signal through a multi-modal fusion model, and the extracted three types of deep features are fused to obtain an emotional representation vector; and inputting the emotion representation vector into a pre-trained emotion recognition model, and outputting the current emotion state of the passenger, including the specific emotion category and the corresponding confidence coefficient. According to the method, emotion analysis and transmission are performed after data anonymization is realized, and accurate judgment of the system on the emotion state is not influenced while privacy security of the user is ensured.

The invention discloses a circuit breaker service life prediction system and method based on multi-source heterogeneous data fusion and dynamic weight correction, and the method comprises the steps: collecting the current, pressure, displacement, main loop current, voltage, contact temperature, environment temperature and partial discharge of a circuit breaker, and carrying out the multi-source heterogeneous data fusion algorithm, thereby achieving the prediction of the service life of the circuit breaker. Carrying out fusion analysis on the collected data; a dynamic weight correction algorithm is executed according to an analysis result, and different types of fault weights are corrected and adjusted, so that the life prediction model is more accurate; and executing a fault identification algorithm after each action of the circuit breaker, and calculating the service life of the circuit breaker according to the weight and the health index corresponding to each fault. According to the invention, the prediction accuracy of the service life of the short-circuiter is greatly improved, and the use safety of power equipment is improved.

The invention relates to the field of energy consumption analysis, and discloses an AI-based energy consumption data analysis and prediction system, which comprises the steps of collecting environmental parameters and running states of equipment, dynamically identifying the current system working condition by using a working condition identification algorithm combining incremental clustering and historical mode matching, and predicting the energy consumption data. Collected data is divided according to time, space and working condition dimensions, multi-scale features are extracted, normalization parameters can be dynamically adjusted along with changes of working conditions, a drift index is calculated through comparison of a drift threshold value and historical distribution, an optimal normalization updating strategy is selected according to the drift index, the normalization parameters are dynamically updated, and energy consumption trend prediction is conducted through a statistical model. A prediction result is combined with a working condition label to carry out weighted correction, error analysis and deviation detection are carried out in combination with a drift index, working condition prediction and historical error data, and an analysis result is fed back to a working condition sensing module, a feature adaptive module and a normalization control module. The method has the advantage of improving the stability and reliability in a dynamic environment.

The invention discloses a dark light enhancement method under the view angle of an unmanned aerial vehicle, and relates to the technical field of image processing and enhancement, and the method comprises the steps: collecting a continuous frame dark light image sequence of a target when the unmanned aerial vehicle flies, carrying out the preprocessing operation including denoising and normalization, and forming a dark light image set; and estimating the motion between adjacent frames by using an image recognition algorithm. According to the invention, through dynamic range compression and detail enhancement processing, the image definition and visibility in a dark light environment are improved, the brightness difference of the image is balanced by adopting a dynamic range compression algorithm, local overexposure or underexposure is avoided, the image can keep a good visual effect under different illumination conditions, and the image quality is improved. And the detail enhancement processing highlights texture and edge information in the image through multi-scale gradient fusion and adaptive sharpening, and improves the detail definition in dark light, so that the unmanned aerial vehicle can recognize a target more clearly when executing a task at night or in a low-light environment, and the task execution efficiency and accuracy are improved.

The invention relates to the technical field of computer vision security and protection, and discloses a helmet normative wearing identification method and system considering head key point positions, and the method comprises the steps: constructing a human body detection model and a helmet normative wearing classification model, and carrying out the training; collecting a monitoring video, performing frame extraction on a video stream according to a specific time interval to obtain a video frame, and positioning a human body in the video frame by using the trained human body detection model to output a human body image; reasoning each human body image through a human body key point recognition algorithm, positioning a plurality of key point positions of a human body, judging a head deflection posture by taking mutual positions among key points in a head region, and cutting a head region image according to the key points in the head region; and inputting the head region image into the standard helmet wearing classification model for reasoning and recognition, judging the head posture through head key point recognition, and judging whether the helmet is worn in a standard manner through the classification model, so that the method has relatively high generalization ability and recognition precision.