1154results about How to "Accurate data support" patented technology

The invention discloses a bridge damage detection method and system, and belongs to the technical field of bridge engineering.The method comprises the steps that image data, point cloud data and spectral data of a to-be-detected bridge are obtained; inputting the obtained image data, point cloud data and spectrum data of the to-be-detected bridge into an input module of the constructed bridge damage detection model, and determining feature vectors of the image, the point cloud and the spectrum; and a feature fusion module of the constructed bridge damage detection model fuses feature vectors of images, point clouds and spectrums, interacts and fuses obtained fused feature representations of all modals on different levels to obtain fused feature vectors, and outputs a bridge damage detection type through a classifier in a DNN classification layer. The method can improve the accuracy of bridge damage detection.

The invention provides a source-network-storage integrated power distribution network operation optimization method, which relates to the technical field of power distribution networks and comprises the steps of setting an intelligent sensor, collecting multi-dimensional power distribution operation parameters, performing semantic extraction and automatic labeling, determining logic association after conflict correction, obtaining a power distribution knowledge graph, constructing a dynamic prediction model and performing power distribution index prediction. Obtaining a prediction result and generating a key index prediction map; constructing a multi-temporal-spatial-scale collaborative optimization model, setting an objective function, constructing a physical optimization sub-model and a market game optimization sub-model, carrying out interactive coupling, and carrying out collaboration, interactive feedback and iterative convergence to obtain a multi-scale collaborative optimization scheme; and carrying out distributed evaluation, generating an evaluation result, if the evaluation result does not reach the standard, packaging the multi-temporal-spatial-scale collaborative optimization model into an optimization contract, triggering the optimization contract, generating a candidate optimization scheme, carrying out repeated evaluation until the standard is reached, and taking the candidate optimization scheme as a consensus scheme and executing the candidate optimization scheme.

The invention provides a new energy station operation intelligent management method and system, and relates to the technical field of new energy station operation, and the method comprises the steps: collecting various data in real time through a sensor and monitoring equipment in a new energy station; transmitting the collected data to a central data center, carrying out iterative optimization processing, and determining final data classification, cleaning and compression parameter combination so as to construct a new energy station big data platform; and according to the constructed big data platform of the new energy station, constructing a digital twinborn model of the new energy station so as to reflect the operation state, the equipment health condition and the environment change of the station in real time. Through real-time data acquisition, digital twinborn model construction and simulation analysis, automatic generation and closed-loop management of an operation optimization scheme are realized, and the operation efficiency, prediction accuracy and fault handling capacity of the new energy station are effectively improved.

The invention discloses an industrial data acquisition and management method based on the Internet of Things, and relates to the technical field of data management, and the method comprises the following steps: carrying out the demand analysis of an industrial environment and a production process, and determining to-be-monitored industrial equipment indexes in the industrial environment and the production process, namely an equipment performance index and a production performance index; according to the industrial data acquisition and management method based on the Internet of Things, equipment performance indexes and production performance indexes are monitored in real time, so that the reliability of equipment is remarkably improved, the accidental downtime is shortened, and through comprehensive analysis of an equipment state evaluation index, a fault analysis evaluation index and a production performance evaluation index, the reliability of the equipment is improved. The method can predict possible faults and performance reduction of the equipment, so as to take preventive maintenance measures in advance, not only can reduce production interruption caused by equipment faults, but also can improve the efficiency and effect of maintenance work by optimizing a maintenance plan.

The invention discloses a test system and method for an artificial heart valve implant, and relates to the technical field of medical instrument detection. The system comprises a ventricle simulator, an atrium simulator, a circulation loop, a high-speed camera and a laser generator, wherein an artificial heart valve is arranged in the high-transparency compliance valve testing cavity, and the circulation loop is composed of a blood vessel compliance simulator and a blood vessel damping simulator. The circulation loop simulates a blood flowing environment for the artificial heart valve, and a flowing medium is added with tracer particle turbid liquid; the ventricle simulator, the atrium simulator, the vascular damper and the vascular compliance simulator respectively provide power, storage space, resistance and pulse pressure for the flowing medium; and the laser generator and the high-speed camera act together to obtain a particle image behind the artificial heart valve. The particle image is uploaded to a computer for heart valve pulsating flow field visualization analysis based on the digital particle image velocity measurement technology, and therefore quantitative analysis of the risk after the heart valve is implanted into the expected position is achieved.

The invention provides a ship remote real-time early warning system based on cloud computing, and relates to the technical field of data processing, and the system comprises a data collection module which is used for collecting ship operation data, environment state data and external interaction data; the data preprocessing module is used for generating standard processing data; the data grouping module is used for dividing a priority data group and a common data group; the data transmission module is used for transmitting the data to the cloud analysis module; the cloud analysis module is used for carrying out multi-source data aggregation processing on the priority data set and the common data set, carrying out real-time evaluation on the ship operation state, identifying abnormal behaviors and predicting emergencies, and generating corresponding final early warning instructions including an avoidance operation instruction and an air route adjustment suggestion; the early warning feedback module is used for sending the early warning instruction to the ship terminal; the local response module is used for executing instant triggering operation on the instruction classification data; according to the invention, autonomy and accuracy of remote real-time early warning of the ship are improved.

The invention relates to the technical field of building engineering, in particular to a steel structure construction quality monitoring system based on an unmanned aerial vehicle and three-dimensional scanning, which comprises a data acquisition module, a data preprocessing module, a construction defect identification and risk assessment module and a quality assessment module, the data acquisition module acquires quality monitoring data of a construction site in real time; the data preprocessing module is used for preprocessing the acquired quality monitoring data; the construction defect identification and risk assessment module identifies steel structure defects in the construction process and assesses the severity of the steel structure defects; and the quality evaluation module performs comprehensive quality evaluation on the preprocessed quality monitoring data and provides corresponding repair suggestions. According to the method, key problem areas can be quickly identified, a construction resource allocation strategy is optimized, and comprehensive and accurate data support is provided for steel structure quality evaluation in a complex construction environment.

The invention relates to a light control method and system based on a holographic interactive projection technology, and the method comprises the steps: projection region recognition, user interaction detection, light parameter calculation and light real-time regulation and control, the projection region recognition, scanning of a target region through a holographic projection device and a depth induction sensor, and generation of a three-dimensional space model of an environment; a motion capture device and a deep learning algorithm are used for carrying out real-time recognition on gestures or motions of a user, interaction intentions and priority ranking are generated, a holographic projection device is used for generating a three-dimensional environment model, the motion capture device recognizes the intentions of the user, and an illumination optimization model is used for calculating regulation and control parameters; and the output of the light equipment is dynamically adjusted, so that high-precision and intelligent light control is realized, and the problems that a traditional light control method is insufficient in environment perception capability, lacks a user interaction mode, is limited in flexibility and adaptability of light regulation and control, and lacks a multi-module cooperative working mechanism are solved.

The invention provides an intelligent forest fire prediction and early warning system which integrates satellite remote sensing, unmanned aerial vehicle patrol, ground monitoring and other technologies and intelligently identifies ecological behaviors by using a deep learning algorithm. The system is composed of four core modules: a multi-source data acquisition module, an ecological behavior identification module, a machine learning prediction model module and an intelligent early warning and emergency response module. The forest environment is comprehensively monitored, ecological data is analyzed, the fire risk is accurately predicted, early warning information is automatically generated, and the system is closely linked with an emergency system. The system effectively solves the problems of single traditional early warning means, poor timeliness and insufficient precision, significantly improves the accuracy and response speed of forest fire early warning, and provides powerful technical support for forest resource protection and ecological environment safety.

The invention relates to the technical field of micro-grid distributed control, in particular to a micro-grid cluster distributed control method and system based on information interconnection. The method comprises the following steps: identifying a micro-grid cluster in a region, and extracting a plurality of micro-grid nodes; obtaining a plurality of microgrid operation state parameters; performing filtering standardization processing on the plurality of micro-grid operation state parameters, and constructing a micro-grid time sequence load change curve; discrete wavelet transform is carried out on the micro-grid time sequence load change curve, load fluctuation frequency trend mining is carried out, and micro-grid load fluctuation frequency trend characteristics are generated; carrying out data stream encapsulation on the load fluctuation frequency trend characteristics of the micro-grid, and constructing a cloud environment distributed computing framework; carrying out dynamic load demand calculation on the plurality of micro-grid nodes based on a cloud environment distributed calculation framework so as to obtain multi-period load demand prediction data of each micro-grid; according to the invention, high-efficiency microgrid cluster control with high energy utilization rate is realized.

The invention relates to the technical field of police service management, discloses a police service management system and method based on an image recognition technology, and aims to improve the police service management efficiency and accuracy. The system comprises a data receiving module, a data preprocessing module, a target detection module, a behavior analysis module and an information integration module. A monitoring video stream is received, data is preprocessed to form an image data set, and then a specific target is detected by using a deep convolutional neural network and a track sequence is output. And carrying out behavior analysis on the target track by adopting a long-short-term memory network, and identifying an abnormal mode. And finally, integrating the detection result and the behavior analysis data, and generating a police service management report. According to the invention, intelligent processing of monitoring videos is realized, police service departments are assisted to respond to abnormal conditions in time, and urban public security is maintained. Through the deep learning technology, the accuracy of target detection and behavior recognition is improved, and powerful support is provided for modern police service management.

The invention discloses an alloy functional material fatigue performance prediction model building method, and relates to the technical field of material scientificity, the method comprises the following steps: installing a sensor group on an alloy surface, using an instrument to collect alloy performance influence data, and transmitting the data to a data processor through a wireless network for preprocessing, data storage and analysis; the method comprises the following steps: constructing a load performance prediction model, calculating a thermal coupling fatigue damage coefficient and a multi-axial load fatigue damage coefficient, summarizing and calculating to obtain a composite fatigue damage coefficient, and carrying out preliminary evaluation on the composite fatigue damage coefficient and a safety fatigue damage threshold value. The environment corrosion influence coefficient is calculated by combining the corrosion influence data set and the precipitation object integral number, corrosion degree prediction risk assessment is carried out on the environment corrosion influence coefficient and the safety fatigue damage threshold value, finally, the comprehensive fatigue damage coefficient is obtained, comprehensive corrosion degree assessment and fatigue damage prediction are carried out, real-time early warning and optimization suggestions are provided, and the safety fatigue damage risk is improved. The safe use of the alloy material is ensured; the service life is prolonged.

The invention relates to the technical field of heart data analysis, in particular to an AI (artificial intelligence)-based heart disease prediction system, which comprises a feature relationship modeling module for mapping each type of information into a multi-dimensional feature space based on collected heart data, analyzing the condition correlation degree between feature nodes, connecting the feature nodes through correlation information, and establishing a model; and quantifying a linkage effect between the features to obtain a feature condition dependency relationship analysis result. According to the method, various types of heart health data are noded by constructing a multi-dimensional feature space, and a correlation structure is established according to condition correlation among data features. And a synergistic effect and an interdependence relationship among the features are disclosed. By analyzing the joint state probability of the feature nodes, the linkage effect of the feature combination on the overall risk is quantified, and the analysis depth of the disease risk is enhanced. In addition, by comparing the independent state probability of a single feature with the joint state probability of the feature combination, a feature combination that makes a significant contribution to the risk can be identified.

The invention provides a distributed photovoltaic output power estimation method, and the method comprises the steps: installing a load sensor and a photovoltaic output sensor at an access point, and collecting original data including load data and photovoltaic output data in real time; processing the collected original data; carrying out time synchronization on the load data and the photovoltaic output data; key features are extracted from the cleaned original data, and photovoltaic output is predicted through a time sequence analysis model; inputting load data and photovoltaic output data which are acquired in real time into the trained time sequence analysis model; the time sequence analysis model performs real-time operation according to the load data and the photovoltaic output data, and estimates the photovoltaic output power at the current moment; and based on the estimated photovoltaic output power and the real-time load data, the intelligent coordination controller performs dynamic adjustment, so that the photovoltaic output is matched with the load power. According to the invention, efficient estimation and optimal control of photovoltaic output are realized.

The invention discloses a plant disease and insect pest detection device and method based on unmanned aerial vehicle high-altitude texture imaging, and belongs to the technical field of intelligent agriculture. The plant disease and insect pest detection device comprises an unmanned aerial vehicle body, a holder, a high-altitude texture camera and a small host; the high-altitude texture camera is arranged below the holder, the holder is arranged below the small host, the small host is arranged below the unmanned aerial vehicle main body, the unmanned aerial vehicle main body is provided with a front high-definition camera, a battery and an illumination sensor, and the high-altitude texture camera is wirelessly provided with a handle and a flight remote controller through Bluetooth; according to the plant disease and insect pest detection method, high-precision recognition of disease and insect pests is achieved on the basis of high-altitude texture feature fusion in combination with an environment perception driven data enhancement module, a self-adaptive multi-scale image convolution feature extraction and fusion module and a disease and insect pest area attention fusion module. The technology effectively supports precise detection of diseases and insect pests in agricultural production, and provides a scientific basis for agricultural management and pesticide application.

The invention discloses an attack trapping method and system based on network attack surface adaptive conversion, and the method comprises the steps: dynamically converting vulnerabilities and weaknesses in a system, so that an attacker is difficult to determine an entrance of a real system; threat intelligence and a network environment are monitored and analyzed in real time, and abnormal traffic and potential attack behaviors are identified; constructing a false target and monitoring attack behaviors, and obtaining behavior characteristics and attack means of an attacker; attack behavior data are analyzed by using artificial intelligence and a machine learning algorithm, potential threats are quickly identified and responded, a threat intelligence library and a defense strategy are updated, and the method better adapts to constantly changing attack means and threat forms. The method not only can trap attack behaviors, but also can collect and analyze behavior characteristics of attackers, and provides more accurate data support for network security decision and threat prediction.

The invention discloses an optical perspective calibration method and system, electronic equipment and a storage medium, and relates to the field of augmented reality. In the method, the position and posture of a virtual calibration object on a display screen are adjusted according to received first user voice, so that the virtual calibration object coincides with an actual calibration object in a physical space; obtaining an image of an actual calibration object according to the received second user voice, obtaining a first two-dimensional pixel coordinate of the first target corner point from the image, and determining a three-dimensional coordinate of the first target corner point according to the first two-dimensional pixel coordinate; acquiring a second two-dimensional pixel coordinate of a second target angular point of the virtual calibration object on the display screen, and calculating a projection matrix according to the three-dimensional coordinate and the second two-dimensional pixel coordinate; the projection matrix is used to transform the points in the virtual space onto the screen of the augmented reality headset. By implementing the technical scheme provided by the invention, personalized optical perspective calibration is realized in a low-cost manner.

An underwater in-situ sea cucumber volume measurement method based on binocular vision and electronic equipment belong to the field of image processing, are used for solving the problem of keeping in-situ sea cucumber volume measurement, and are technically characterized in that a sea cucumber image is detected in underwater images collected by a binocular camera to obtain a sea cucumber image detection frame; segmenting a sea cucumber image detected in the image according to the sea cucumber image detection frame, and calculating mask information of the sea cucumber on an image plane; mapping the mask information to a global point cloud constructed by binocular information, and obtaining point cloud data of the sea cucumber; performing grid reconstruction on the point cloud data of the sea cucumber to obtain a continuous triangular grid; according to the method, the continuous triangular grids are converted into voxel representation, the number of non-zero elements in the voxel grids and the volume of each element are counted, the volume of the grids is estimated, and the volume of the sea cucumbers is obtained, so that the volume of the in-situ sea cucumbers can be accurately evaluated, and more accurate data support is provided for scientific cultivation of the sea cucumbers.

The invention discloses an anti-direct-current component nanocrystalline current transformer and a using method thereof. The anti-direct-current component nanocrystalline current transformer comprises a current transformer body, and a magnetic core hole penetrating front and back is formed in the middle of the front side of the current transformer body. The device is provided with a secondary side open circuit detection and protection mechanism, so that imbalance of a magnetic field in the mutual inductor caused by disconnection of a secondary loop is effectively prevented, iron core magnetic flux saturation and potential high electromotive force generation are avoided, the safety of the mutual inductor and peripheral equipment is protected, the risk of power system faults such as fire disasters or short circuits is reduced, and meanwhile, the safety of the mutual inductor is improved. A live wire centering fixing mechanism is arranged, so that the stable centering position of a wire in a magnetic core hole of the mutual inductor is ensured, magnetic field distribution is optimized, the magnetic leakage phenomenon is reduced, the accuracy and stability of current measurement are remarkably improved, meanwhile, measurement fluctuation caused by wire shaking is reduced, and the measurement precision is improved. And more accurate data support is provided for harmonic analysis, fault detection and electric energy quality monitoring of a power system.

The invention relates to a non-signaling test-based frequency offset calibration method, system and device and a medium, and the method comprises the steps: carrying out the parallel non-signaling frequency offset test of a plurality of target chips, collecting the output waveforms of the target chips, and determining the frequency offset values of the target chips according to the output waveforms; performing deviation analysis on the frequency deviation value, and calculating a corresponding frequency deviation calibration value based on the frequency deviation value and a preset frequency calibration coefficient under the condition that the frequency deviation value reaches a preset frequency limit range; writing the frequency offset calibration value into a corresponding target chip, and performing parallel frequency offset calibration; and carrying out calibration degree verification on the target chip after frequency offset calibration so as to confirm whether the frequency offset value of the target chip accords with a preset standard frequency. The method and the device have the effect of improving the frequency offset calibration efficiency of the chip.

The invention relates to the technical field of measurement and testing, in particular to a humanoid robot joint module performance testing system which comprises a testing frame and a top frame fixed to the surface of the testing frame and further comprises a compression resistance testing assembly arranged above the testing frame. The stress condition in an actual working state can be better simulated, accurate data support is provided for performance test and optimization of the joint module, the required pressure can be accurately applied under different test conditions by adjusting the output pressure of the loading hydraulic cylinder, the joint module is positioned by the positioning claw and the positioning block, and the test efficiency is improved. The pressure resistance and stability of the joint module in actual use can be better known, accurate data support is provided for design and optimization of a robot, deviation of measured data such as pressure and deformation caused by deviation of the joint module in the testing process is avoided, and therefore accurate performance testing data are obtained.

The invention relates to the field of computer vision and ophthalmology medicine, and provides an anterior segment parameter measurement method and device, electronic equipment and a storage medium, and the measurement method comprises the steps: repairing a panoramic AS-OCT image based on a generative adversarial network, and eliminating a central axis and artifacts in the panoramic AS-OCT image; based on an image segmentation model, segmenting each structure of an anterior segment in the restored panoramic AS-OCT image to obtain corresponding segmented images; performing key structure positioning on the repaired panoramic AS-OCT image by using a preset positioning model to obtain a corresponding positioning result; and based on the segmented image and the positioning result, measuring anterior segment parameters related to the panoramic AS-OCT image to obtain a corresponding biological parameter measurement result. According to the invention, the automation degree, accuracy and efficiency of the measurement of the biological parameters of the anterior segment in the AS-OCT image can be greatly improved, and powerful support is provided for the diagnosis and treatment of ophthalmic diseases.

The invention discloses a combustion chemical reaction kinetic data processing method and system for an ADN-based liquid propellant, and belongs to the technical field of data processing. According to the method, firstly, key information in the combustion process can be comprehensively captured by obtaining a combustion chemical reaction kinetic vector and an element state linkage vector; structured semantic mapping is carried out on the primitive state linkage vector to obtain a reaction state structured vector and a strategy association demand vector, which is helpful for accurate classification and positioning of combustion reaction related information. And the integration confidence weights of the first data and the second data are determined, so that the association degree of the element reaction data and the visual association labels under different integration strategies can be quantified, and the analysis of the combustion process is more scientific and meticulous. The finally determined visual monitoring result can visually present the characteristics of the element reaction data under different strategies, and clear and accurate data support is provided for deeply understanding the combustion mechanism of the ADN-based liquid propellant, optimizing the performance of the propellant and ensuring safe use.

The invention relates to the technical field of electric wheelchair control, in particular to a multi-mode navigation control method and system of an electric wheelchair controller. The method comprises the following steps: acquiring a real-time environment monitoring video based on an electric wheelchair high-definition camera; video jitter distortion compensation and dynamic object frame-by-frame continuous optical flow tracking are carried out on the real-time environment monitoring video, and a moving track of each dynamic object is generated; performing environment static obstacle accurate identification on the real-time environment monitoring video, and performing dynamic environment perception modeling based on a moving track of each dynamic object so as to generate a dynamic environment map; a navigation mode switching decision is made based on a real-time wheelchair operation instruction of the user to obtain a current navigation mode of the user, and the current navigation mode of the user comprises a rocker control mode and an intelligent navigation mode. According to the invention, efficient and intelligent multi-mode control of the electric wheelchair is realized, so that the electric wheelchair can more flexibly and safely cope with a complex environment.

The invention relates to the technical field of hydraulic engineering operation prediction, in particular to a hydraulic engineering operation trend prediction method and system. The method comprises the following steps: acquiring multi-source hydraulic engineering data, and generating a hydraulic engineering three-dimensional model through multi-source space fusion processing and three-dimensional model simulation; performing geometric complexity identification and stress path analysis on the three-dimensional model to determine key nodes in the water conservancy project; historical water conservancy data are obtained, time sequence conversion and potential mode mining are carried out, and potential modes related to engineering are obtained; performing stress superposition comparison on the key nodes of the water conservancy project and the potential mode to generate stress superposition comparison data, and iteratively adjusting the key nodes according to a result to obtain adjusted stress nodes; constructing a prediction model based on the adjusted stress node and the potential mode, performing operation trend prediction, and generating an operation trend prediction result of the water conservancy project; the high-precision and high-adaptability water conservancy project operation trend prediction method is realized.

The invention provides a geological disaster monitoring multi-dimensional visual early warning system and method based on big data, and relates to the technical field of geological disaster monitoring, and the method comprises the steps: collecting ground surface displacement, slope stability and underground water level in real time; analyzing and processing the collected data to generate a surface risk coefficient, a slope risk coefficient and a water level risk coefficient; establishing a risk level evaluation model of which the input quantity is an earth surface risk coefficient, a slope risk coefficient and a water level risk coefficient and the output quantity is a geological risk level, obtaining historical earth surface displacement parameters, slope stability parameters and underground water level parameters, and defining corresponding geological risk level coefficients; and comparing the risk grade coefficient analyzed by the evaluation model with a threshold value, and sending out a corresponding early warning signal. According to the method, the accuracy of disaster early warning and risk assessment can be improved, model parameters are verified by using historical disaster data in big data, and the accuracy and reliability of a risk grade evaluation model are improved, so that the geological disaster risk is predicted more accurately.

The invention discloses a real-time laser coal checking system and method, and relates to the technical field of material management and inventory checking, and the system comprises the following components: a laser detection module, a data processing module, a communication module, a monitoring module and a server module. The laser detection module is adopted to perform high-frequency scanning on the coal pile to obtain the three-dimensional point cloud data of the coal pile, the crown block displacement data measured by the laser range finder and the material and density information of the material pile are combined, the volume and weight of the coal pile are calculated through an algorithm, the outer contour of the coal pile can be determined more accurately, and the accuracy of the coal pile is improved. According to the shape characteristics and the density distribution condition of the coal pile, the coal pile is divided into a plurality of small geometries for volume calculation, and compared with traditional manual measurement or simple geometric shape approximation, the method has the advantages that the measurement precision of the volume and the weight of the coal pile can be remarkably improved; and more accurate data support is provided for material management and inventory checking of enterprises.

The invention relates to structural health monitoring intelligent concrete for airports and a preparation method thereof, and the method comprises the following steps: adding a polycarboxylic acid water reducing agent into water, and mixing to obtain a water reducing agent solution; adding carboxylated multi-walled carbon nanotubes, silica fume and fly ash into the water reducing agent solution, and mixing to obtain a carbon nanotube suspension; performing ultrasonic treatment on the carbon nanotube suspension to obtain a carbon nanotube dispersion liquid; and adding the carbon nanotube dispersion liquid into the dry material mixture, and mixing to obtain the structural health monitoring intelligent concrete for the airport. Compared with the prior art, the conductivity of the concrete can be effectively improved, so that the concrete has the function of monitoring structural health.

The invention provides a wound nursing intelligent decision-making method and system based on high-precision image recognition. The method comprises the following steps: in response to a received wound monitoring request, extracting a patient identifier, wound image information corresponding to a multi-angle high-precision image, wound background information and multi-modal sensor information from the wound monitoring request; a pre-trained three-dimensional convolutional neural network model is used for automatically recognizing the wound state, and a wound recognition result is obtained; based on the wound image information and the wound background information, assisting in positioning through an augmented reality technology; performing wound healing environment assessment based on the information of the multi-modal sensor; retrieving matched nursing strategy information from a preset nursing scheme database based on patient historical information and personalized treatment preference information corresponding to the patient identifier, the wound identification result, the wound positioning information and the wound healing environment evaluation information; and pushing the nursing strategy information. According to the scheme, the wound nursing quality is improved.

The invention discloses a power load prediction and optimization method based on artificial intelligence, particularly relates to the field of power load prediction and optimization, is used for solving the problem of system stability caused by a nonlinear resonance effect, and comprises the following steps: preliminarily extracting key features of a power load signal through a multi-scale decomposition and mode recognition technology; and identifying a load mode which may cause system resonance. Two key indexes of frequency resonance intensity and time phase difference are emphatically utilized, the resonance risk of a load mode is comprehensively analyzed through a Bayesian network, the weight configuration of a high-risk mode is dynamically optimized in combination with a genetic algorithm, and the sensitivity and adaptability of the model to different resonance effects are improved. And then, the classified load modes are input into a deep neural network for multi-mode prediction fusion, and the model weight is adjusted in real time through a particle swarm optimization algorithm, so that the prediction precision and the operation stability of the system under a complex load condition are ensured, and a powerful guarantee is provided for the reliability of the power system.