282 results about "Support vector machine algorithm" patented technology

The invention relates to the technical field of intelligent fault diagnosis, and discloses a fault diagnosis method for new energy power generation equipment, and the method comprises the steps: obtaining a multi-physical-quantity time dynamic data set; outlier elimination is carried out on the data set, and a deviation degree sequence of each physical quantity is extracted; based on the deviation degree sequence, normalization processing is completed, and a correlation matrix among multiple physical quantities is constructed; generating a preliminary network structure through correlation screening and symmetry completion; calculating a path weight based on the initial network structure and performing topology reconstruction to obtain a final network topology structure; performing deviation propagation analysis according to the final network topology structure, and determining potential fault position distribution; based on the fault position distribution, performing fault grade classification by adopting a support vector machine algorithm, and outputting a fault risk grade label; and in combination with the fault level label and the network topology structure, node risk scoring and area identification are executed, and finally fault positioning is completed. According to the method, accurate positioning of the fault position in a complex system can be realized.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle battery endurance flight capability prediction system, which comprises a multi-dimensional data acquisition module, a feature mapping module, a prediction module, an optimization module and a feedback optimization module, and can be additionally provided with an early warning module. The multi-dimensional data acquisition module acquires battery data and cleans the battery data to generate standardized data; the feature mapping module maps the data to a feature space, and generates a feature sequence cluster containing a multi-dimensional association relationship by using a time sequence segmentation algorithm; the prediction module divides prediction intervals based on a support vector machine algorithm and extracts prediction indexes; the optimization module generates an endurance prediction strategy by predicting and optimizing the network model; and the feedback optimization module performs multi-source data fusion optimization and outputs a prediction instruction. The early warning module can associate the prediction instruction with the battery health degree, output a grading early warning signal and trigger a response mechanism.

The invention discloses a network abnormal state identification method and device. According to the method, the aging degree quantized value of the network object is generated by combining time sequence analysis, a mining algorithm and a support vector machine algorithm, and the importance quantized value is generated based on the network attribute. And carrying out weighted fusion on the two to generate a comprehensive score, setting an abnormal threshold according to a real-time score distribution curve, and screening out an aged object set. And identifying an aging mode and generating aging description by using a deep learning model combining a long-short-term memory network and a graph neural network. The multi-modal features of the aging object are extracted from multiple dimensions, and a dynamic feature space is constructed. And finally, in combination with the aging description and the dynamic feature space, identifying an abnormal state in the network by adopting a multi-dimensional data association algorithm and a self-adaptive visualization method. According to the process, comprehensive, dynamic and real-time identification of the abnormal state of the network is realized, and the problem that the abnormal state of the network cannot be comprehensively and accurately identified in the prior art is effectively solved.

The invention discloses a continuous attention neural feedback training method and system based on a brain-computer interface, and relates to the technical field of neural feedback, and the method comprises the steps: collecting a multi-channel electroencephalogram signal of a user in visual task training in real time; extracting power spectral density characteristics of the multi-channel electroencephalogram signals in a beta frequency band, classifying the power spectral density characteristics by adopting a support vector machine algorithm, and outputting a judgment result of an alert or non-alert state; and according to a judgment result, dynamically adjusting an information fusion proportion alpha value in the visual task through a reward-punishment mechanism, updating image information feedback in the visual task in real time, and adjusting the attention state of the user through an image information feedback result. Neural feedback and a dynamic reward and punishment system are fused, real-time excitation feedback is obtained by autonomously adjusting electroencephalogram activity, the problem of insufficient training power caused by traditional static tasks or single positive feedback is solved, and the long-term training effect is enhanced.

The invention discloses an elevator steel wire rope wear real-time monitoring system and equipment thereof, and relates to the technical field of safety monitoring, the elevator steel wire rope wear real-time monitoring system comprises an acquisition and extraction module which acquires real-time surface image data and vibration signal data of a steel wire rope through a sensor array, extracts surface damage features and vibration spectrum features by adopting an image processing algorithm, and sends the surface damage features and the vibration spectrum features to a server; fusing kernel function selection and hyperplane separation to process the preliminary wear feature set; the crack identification module is used for fusing tensile strength and corrosion resistance data in the material characteristic database according to the initial wear characteristic set, classifying potential fatigue crack types through support vector identification and crack morphological characteristics by adopting a support vector machine algorithm, and determining a fatigue crack distribution diagram; according to the elevator steel wire rope abrasion real-time monitoring system and equipment thereof, accurate abrasion evaluation and dynamic maintenance optimization are achieved, the safety of the steel wire rope is improved, and the service life of the steel wire rope is prolonged.

The invention provides a method for solving signal drift of an oxygen sensor in a high-temperature and high-pressure environment, which comprises the following steps: collecting acid-base gas concentration data and zirconium oxide material surface state information in the high-temperature and high-pressure environment, and classifying erosion sensitivity degrees by adopting a support vector machine algorithm to obtain an initial response characteristic deviation value; a neural network model is adopted to process the determined potential signal drift trend and oxygen content detection historical data, a simulation response characteristic curve is generated, and a quantitative mapping relation of the pH value change to erosion sensitivity is obtained; refining the correlation between the determined long-term monitoring reliability index and the pH value change by adopting a neural network model, generating a dynamic response characteristic prediction sequence, and obtaining a signal drift suppression threshold set; and through fusion of the obtained signal drift suppression threshold set and the oxygen content detection real-time signal, if it is judged that erosion sensitivity is aggravated in a prediction sequence, a compensation model is activated to update and circulate, and optimized sensor stable output is obtained.

The invention relates to an intelligent integrated warehouse logistics management method based on an electronic tag, and the method comprises the steps: obtaining real-time humidity monitoring data of a warehouse environment, and generating an environment parameter matrix in combination with temperature and ventilation conditions; according to the environment parameter matrix, a mapping relation model of humidity and label performance is trained, and the influence weight of the humidity on the label performance is obtained; according to the label performance comprehensive score, adopting a support vector machine algorithm to judge whether the label performance is close to a failure threshold value, and generating a performance early warning signal; when the performance early warning signal is triggered, predicting a label performance change trend through time sequence analysis, and generating a performance degradation curve; according to the performance degradation curve, a Bayesian network model is adopted, label performance evaluation model parameters are updated, and model prediction precision is optimized; and applying the updated model parameters to the dynamic performance evaluation model, and outputting a label health state diagnosis result in real time in combination with the real-time environment parameter matrix.

The invention relates to the technical field of power distribution networks, in particular to a power distribution network resource self-adaptive scheduling method, system and device and a medium. Using a support vector machine algorithm to construct an agent model representing a nonlinear coupling relationship between the novel power factor real-time parameters and the initial data set of the power distribution network; decomposing a multivariable interaction effect in the agent model by using a global sensitivity analysis method to obtain a sensitivity index; adaptively correcting the sampling space of the agent model according to the sensitivity index to obtain an optimized agent model; and on the basis of the optimization agent model and the real-time monitoring data of the power distribution network, generating a power distribution network resource adaptive scheduling strategy by adopting a particle swarm optimization algorithm, thereby carrying out optimal configuration on the power distribution network resources in the novel power factor access scene. According to the method, efficient and accurate optimal configuration of the power distribution network resources in a novel power element access scene is realized, and the operation performance and stability of the power distribution network are remarkably improved.

The invention provides an oily water treatment equipment control method based on the Internet of Things, which comprises the following steps: feeding back data through an operation state, calling a multi-dimensional data analysis module, carrying out association calculation on energy consumption, treatment efficiency and adjustment parameters, predicting a subsequent operation trend by adopting a support vector machine algorithm, and obtaining a feasibility evaluation result of energy-saving mode switching; if the feasibility evaluation result of the energy-saving mode switching shows that the energy consumption optimization space is relatively large, starting an energy-saving mode switching module, reducing the power output of a non-key processing unit, maintaining the core separation efficiency, and determining new energy consumption balance point data; and through equipment state updating information, continuously monitoring an operation cost control module, comparing cleaned energy consumption data with historical records, calculating a cost saving range, and judging a potential improvement space for prolonging the service life of the equipment.

The invention relates to the technical field of electrode preparation, and discloses a dry-method electrode preparation process control method and system and a storage medium. The method comprises the steps that in the rolling forming process of a dry-method electrode material, pressure roller gap data and rotation angle data of a driving motor are monitored and collected in real time, and a support vector machine algorithm is used for recognizing the transmission ratio change trend of a pressure roller system; calculating a pitch precision offset caused by wear through Kalman filtering in combination with historical wear data; when the offset exceeds the limit, an adjustment coefficient is generated based on a simulation model, and the control precision is improved through feed-forward compensation and feedback optimization; when the adjustment precision meets the process requirement, a self-adaptive parameter set is determined through dynamic deviation residual analysis, and an actuator is driven to adjust the gap between the compression rollers; and monitoring the thickness uniformity index of the electrode plate produced after adjustment in real time, and finally forming quality closed-loop control by taking the thickness uniformity of the electrode plate as a verification standard. According to the invention, the quality precision of dry-method electrode production is improved.

The invention discloses an urban rail transit energy management method based on multi-source fusion, and the method comprises the steps: employing a support vector machine algorithm to analyze a correlation mode between train intensive operation and passenger flow surge according to an obtained energy demand fluctuation index, and determining a potential energy consumption peak value position; the determined adjustment parameters are obtained, a power supply system control instruction is updated in combination with real-time train track information, and dynamic power supply load configuration is obtained; whether the obtained dynamic power supply load configuration is matched with the current passenger flow surge data or not is judged, if yes, a mode switching signal is sent to an equipment controller, and energy use feedback data after execution is obtained; according to the obtained energy use feedback data, evaluating the response accuracy of the system integration effect to demand fluctuation by adopting a gradient boosting decision tree algorithm, and determining further trajectory optimization suggestions; and updating a train operation scheduling model through the determined trajectory optimization suggestion to obtain an integrated multi-source information linkage mechanism.

The invention provides a method for detecting a virus inactivation effect by an ELISA (Enzyme-Linked Immunosorbent Assay) method, which comprises the following steps: acquiring a to-be-detected inactivated virus sample, extracting an antigen component from the sample, and detecting an antigen structure change characteristic value through a high-throughput mass spectrometry analysis technology to obtain antigen degradation degree data; obtaining a sample subjected to preliminary inactivation, and detecting the antigen content change trend through an enzyme-linked immunosorbent assay to obtain content change curve data; detecting the residual quantity of viral nucleic acid by a real-time fluorescent quantitative PCR (Polymerase Chain Reaction) technology aiming at the immunogenicity retained sample to obtain nucleic acid degradation degree data; acquiring nucleic acid degradation degree data, classifying the relationship between nucleic acid residues and infectivity by adopting a support vector machine algorithm, and judging that the inactivation effect is completely completed if the classification result shows that the infectivity is lost; according to the antigen content change trend, the immunogenicity index and the nucleic acid degradation degree data, a weighted fusion algorithm is adopted to integrate multi-dimensional detection results, and a comprehensive inactivation effect score is obtained.

The invention relates to a safety risk prevention and control method, system and device for an external envelope structure of a building and a medium. The method comprises the steps of obtaining three-dimensional scanning data of the building and generating a three-dimensional structure model of the building; structural features of the building are extracted from the three-dimensional structural model, a corresponding environment monitoring scheme is generated in combination with a clustering analysis algorithm, and the collection position and the data type of real-time environment data are determined; collecting real-time environment data of the external envelope structure, and dynamically filtering the real-time environment data based on an adaptive filtering algorithm to obtain standard environment data; analyzing the standard environment data by using a support vector machine algorithm, and triggering a safety early warning message under the condition that a sudden structure safety risk is identified; and obtaining structure multi-source data, predicting the structure multi-source data by using a preset safety prediction model to obtain a safety risk development trend of the building structure, and estimating a potential risk based on the safety risk development trend. The method has the effect of improving the safety of the external maintenance structure of the building.

The invention discloses a method for determining pesticides in water by liquid chromatography and tandem mass spectrometry, which comprises the following steps: acquiring retention time data of different pesticide compounds in a liquid chromatography system, recording retention behavior characteristics of each pesticide compound under optimized chromatographic conditions by adjusting the composition proportion of a mobile phase and a gradient elution program, and determining the retention behavior characteristics of each pesticide compound under optimized chromatographic conditions. Obtaining a standardized retention time spectrum library data set; establishing a quantitative relation model between molecular structure parameters and chromatographic retention time by adopting a multiple linear regression algorithm through key parameters such as molecular weight, polar surface area and lipid-water partition coefficient in the molecular structure parameter matrix, and obtaining a structure-retention correlation prediction model; and constructing a classification prediction model between molecular structure parameters and environmental durability by adopting a support vector machine algorithm through activity index data in the biological activity prediction result, and determining the environmental durability grade of the pesticide compound according to molecular stability parameters and degradation half-life characteristic values.

The invention provides an intelligent operation optimization and fault processing method for a hydraulic power unit, and the method comprises the steps: collecting real-time pressure data and flow data from the hydraulic power unit through a sensor network, and carrying out the classification processing of the collected pressure data and flow data through a support vector machine algorithm, obtaining a current operation state classification result of the hydraulic power unit; through power redundancy configuration of the started hydraulic power unit, synchronous signal data are collected for the pressure regulating valve group, a neural network algorithm is adopted to conduct prediction processing on the synchronous signal data, and an expected response time sequence of pressure regulation is obtained; according to the obtained filter mechanism optimization result with the minimized oil path interruption, real-time monitoring is conducted on filter element blockage data in the high-dust environment, anomaly detection is conducted on the filter element blockage data through a support vector machine algorithm, and a predicted deviation value of pressure fluctuation is obtained.

Disclosed in the present invention is a method for rapidly testing the authenticity of degradable plastic on the basis of near-infrared technology, comprising the following steps: step S1, using packaging bag samples of known materials to collect spectra, and using a kennard-Stone method to perform division to obtain a training set and a prediction set; step S2, by means of the spectra in the training set in combination with analysis based on a normalization algorithm, using a support vector machine algorithm to perform modeling, and using sample data in the training set to train a support vector machine model; and step S3, using the prediction set and inputting same into the trained support vector machine model to obtain a prediction result of the model, and performing analysis and adjustment to obtain an optimized support vector machine model. The method of the present invention features a simple testing process and high identification accuracy, and enables rapid determination of whether a material is a degradable material and identification of the specific type of the degradable material; additionally, the method saves a large amount of manpower, material resources and financial resources, achieving high economic benefits.

The invention discloses a large recreation facility safety detection method and system based on multiple sensors, and relates to the technical field of data encryption, and the method comprises the steps: based on a multi-modal data set, extracting vibration signal time-frequency features through fast Fourier transform and wavelet analysis, and generating a fault analysis report in combination with a support vector machine algorithm; based on the comprehensive characteristic spectrum, carrying out recreation facility health state quantitative evaluation through a Bayesian inversion algorithm, and generating a health state diagnosis report; based on a differential detection scheme, multi-source information fusion decision making is carried out through a D-S evidence theory, and graded safety early warning is generated. According to the method, the time-frequency characteristics of the vibration signals are extracted through fast Fourier transform and wavelet analysis, and a fault analysis report is generated in combination with a support vector machine algorithm, so that accurate analysis of the vibration state of the recreation facility is realized; and the accuracy of fault diagnosis is improved.

The invention relates to a historical trajectory big data-based ship berth arrival time prediction method and system. Firstly, AIS data of a target ship are obtained in real time and preprocessed, and preprocessed navigation data are output; then collecting historical AIS data of different ships, establishing a model library containing various navigation trajectories, and segmenting and clustering the data trajectories in the library to obtain an initial training set; based on the training set, training by using a support vector machine algorithm to obtain a ship ETA prediction model, and outputting predicted arrival time; meanwhile, collecting port area geographic information in real time, training an ARIMA model in combination with a training set to obtain a ship ETAB prediction model, and outputting predicted port area boundary to berth time; and finally, calculating the final berth arrival time of the ship based on the two output times. Compared with the prior art, the method has the advantages of high prediction accuracy, strong model adaptability and generalization ability, high calculation efficiency and the like.

The invention provides a self-adaptive feeding speed control method and system for a bus-type linear cutting numerical control system, and the method comprises the steps: dividing the states of a machining process into a short circuit state, a normal discharge state and a no-load state through building a frequency reference benchmark in a no-load state and adopting a two-parameter fusion recognition method based on a current stability parameter and a frequency deviation parameter; based on state recognition, a staged adaptive strategy is adopted to calculate a speed adjustment coefficient: a preset parameter mode is adopted to perform adjustment and data collection in the initial stage, and a support vector machine algorithm is adopted to intelligently calculate an optimal feeding speed adjustment coefficient according to historical data in the later stage; through an improved time division method interpolation algorithm and a bus communication technology, dynamic adjustment of interpolation step length and real-time control instruction transmission are carried out; according to the invention, the machining precision, efficiency and stability are improved, and an intelligent control solution is provided for a bus type medium-speed wire cutting numerical control system.

The invention discloses a tidal flat terrain inversion method and system, and the method comprises the following steps: 1, obtaining a multi-source remote sensing image and ICESat-2 satellite altimeter data under the condition of less clouds or no clouds, and carrying out the preprocessing of the multi-source remote sensing image and ICESat-2 satellite altimeter data; 2, classifying the preprocessed multi-source remote sensing images by using a support vector machine algorithm, and extracting submerging frequency information of the tidal flat; 3, analyzing photon topographic features of ICESat-2 satellite altimeter data, and accurately extracting prior elevation information of the tidal flat by adopting a multi-stage denoising fusion method; step 4, enabling the tidal flat submerging frequency to be in one-to-one correspondence with the prior elevation information, constructing a tidal flat terrain inversion universal model, and mapping the submerging frequency into the tidal flat terrain; and 5, performing precision evaluation on the inverted tidal flat terrain by using the high-precision airborne LiDAR data. The relation between the tidal flat elevation and the submerging frequency is established, the method can be used for inverting the interannual tidal flat terrain, and high-frequency monitoring of the tidal flat terrain is achieved.

The invention provides an electric vehicle charging fire early warning method and system based on arc detection, and the method comprises the steps: carrying out the time-frequency domain analysis of an arc signal through employing a wavelet analysis method, extracting the time-domain and frequency-domain features of the arc signal, carrying out the classification and recognition of the arc signal through combining with a support vector machine algorithm, and carrying out the early warning of the electric vehicle charging fire based on the arc detection. Judging whether arc discharge occurs or not; modeling analysis is carried out on distribution of wires and connectors in an electric vehicle charging system to obtain propagation paths and attenuation characteristics of arc signals at different positions, and a finite element simulation method is combined to establish an accurate arc signal propagation model for guiding sensor deployment and positioning algorithm design; during sensor deployment, factors such as arc positioning precision, system cost and convenience are comprehensively considered, the number and positions of sensor nodes are optimized through a genetic algorithm, and the sensor deployment cost is minimized while the arc positioning performance requirement is met.

The invention relates to a terahertz spectrum detection technology, in particular to a pericarpium citri reticulatae producing area identification method based on graph regularization sparse principal component analysis and a support vector machine algorithm, and belongs to the field of pericarpium citri reticulatae producing area quality detection. The method comprises the following steps: grinding and tabletting a dried orange peel sample to be detected, firstly detecting the dried orange peel sample in a nitrogen environment by adopting a terahertz time-domain spectroscopy system in a transmission mode to obtain a terahertz time-domain spectroscopy signal of the sample, and performing Fourier transform on the time-domain spectroscopy signal to obtain a frequency-domain spectrum of the sample; and obtaining a corresponding terahertz absorption spectrum according to the frequency domain spectrum. Savitzky-Golay smoothing preprocessing is carried out on the obtained terahertz absorption spectrum, the terahertz absorption spectrum is divided into a training set and a test set, and then feature extraction is carried out on data by utilizing sparse principal component analysis in combination with graph regularization. And taking the processed data as the input of a classification model. And finally, a combined parameter of an optimal regularization coefficient c and a kernel function parameter g of the support vector machine is obtained through a particle swarm optimization algorithm, so that an optimal pericarpium citri reticulatae producing area classification model is established. The method provided by the invention is convenient in sample preparation and simple in operation, can effectively realize rapid and accurate identification of dried orange peel from different producing areas, and provides a new method for identification of dried orange peel in high-value producing areas.

The invention relates to the technical field of kiln combustion control, and discloses a dynamic tar blending combustion proportion optimization control method and system. Comprising the following steps: collecting kiln system data to obtain a standardized working condition data set; inputting the multi-layer perceptron model to obtain a combustion stability score; when the score is lower than a stable threshold value, triggering risk assessment: extracting kiln load micro fluctuation characteristics from the data set, classifying shutdown risk levels by using a support vector machine algorithm model, and determining a high-risk early warning signal; based on the signal correlation current working condition, a historical optimal blending combustion proportion in a corresponding historical adjustment record is called, a deviation value is calculated in combination with the current blending combustion proportion, and a preliminary proportion adjustment suggestion value is obtained; and iterative correction is started, real-time fuel characteristic change and a combustion state prediction result are fused for step-by-step adjustment, a correction proportion is input into a multi-layer sensor to calculate a stability score, and an optimization control scheme is output after the stability score reaches the standard. According to the method, the tar blending combustion proportion is dynamically optimized, the combustion stability of the kiln is effectively improved, and the shutdown risk is reduced.

The invention discloses an intelligent monitoring and management system for urban garden irrigation. Initial multi-dimensional garden data are collected through a sensor and meteorological equipment; extracting associated features in the initial multi-dimensional garden data by using a machine learning algorithm, establishing a Bi-LSTM garden dynamic water demand prediction model based on a Bi-LSTM neural network, and optimizing hyper-parameters of the prediction model by using a PSO particle swarm optimization algorithm to obtain a target Bi-LSTM garden dynamic water demand prediction model; and inputting the multi-dimensional associated garden data into the target B i-LSTM garden dynamic water demand prediction model for training, optimizing a water-fertilizer ratio through an SVM support vector machine algorithm to obtain a garden zoning irrigation strategy, and irrigating the garden based on the garden zoning irrigation strategy. The future water demand of the garden can be accurately predicted, the growth of plants is prevented from being affected by water shortage or excessive water, and healthy growth of the garden plants is guaranteed.

The invention discloses a flammable and explosive gas anti-explosion safety early warning method and system based on artificial intelligence. The method comprises the following steps: acquiring a data set acquired by a multi-modal sensor; performing dimension reduction processing on the data set by adopting a principal component analysis algorithm to obtain a target feature set; extracting an independent component set related to the gas concentration from the target feature set to generate an initial concentration feature value; classifying environmental parameter fluctuations by adopting a support vector machine algorithm according to the initial concentration characteristic value, and outputting a classification result; and judging whether the classification result exceeds a preset environment fluctuation threshold value or not, and if the classification result exceeds the preset environment fluctuation threshold value, adjusting the initial concentration characteristic value through a self-adaptive filter to generate a corrected concentration characteristic value. The method effectively eliminates the influence of environmental fluctuation on gas concentration measurement, improves the measurement precision and reliability, and is suitable for gas concentration monitoring scenes in various complex environments.

According to the machining method and system for the stainless steel rectangular groove plate, the material thickness and elastic modulus parameters of a 316L stainless steel thin plate are obtained, the stress distribution state in the machining process is monitored in real time through a sensor, and if the stress value exceeds a preset threshold value, the cutting force and machining speed parameters are adjusted; a support vector machine algorithm is adopted to analyze the nonlinear relation between the material thickness and the elastic deformation, and an optimized mechanical parameter control model is obtained; according to stress control data output by the mechanical parameter control model, the machining temperature and vibration frequency information of a contact area of a tool and a workpiece is obtained, if the vibration frequency exceeds a stable machining range, the rotating speed and the feeding amount of a spindle are adjusted in real time, and the influence of thermal deformation on the groove depth precision is corrected through a temperature compensation algorithm; and determining a dynamic processing parameter combination adapted to the characteristics of the thin plate. Intelligent control over the thin plate precision machining process is achieved, and the machining precision and efficiency are effectively improved.

The invention provides an ultra-thin wing anti-bending performance quantitative evaluation method which comprises the following steps: acquiring geometric parameters and material characteristic data of an ultra-thin wing, constructing a digital twin model by adopting a finite element analysis method, and simulating stress distribution under boundary conditions to obtain initial mechanical response characteristics; the obtained risk assessment matrix is adopted to calibrate the limitation of a static test, and boundary condition constraints are fused to obtain a calibrated anti-bending energy index set; constructing an evaluation framework of a quantitative system through the calibrated anti-bending energy index set, classifying performance levels in different load scenes by adopting a support vector machine algorithm, and determining an overall structure reliability score; and iteratively updating the parameter setting of the digital twin model according to the obtained anti-bending performance improvement scheme, simulating the optimized flight performance, and obtaining the final quantitative evaluation system output.

The invention provides an intelligent switch cabinet abnormal operation detection method and system, and relates to the technical field of electrical equipment monitoring, and the method comprises the steps: S1, employing an electrical parameter sensor to collect the multi-dimensional operation data of the current, voltage, temperature, mechanical vibration and partial discharge of a switch cabinet, and carrying out the data preprocessing; s2, performing feature extraction on the multi-dimensional operation data after data preprocessing; s3, performing intelligent classification and anomaly detection on the multi-dimensional operation data after feature extraction by adopting a support vector machine algorithm of particle swarm optimization; s4, fault positioning is carried out on the switch cabinet according to the intelligent classification and anomaly detection results; according to the invention, real-time data processing can be realized, comprehensive monitoring, fault early warning and accurate positioning of the operation state of the switch cabinet are realized, and the operation safety and reliability of the switch cabinet are improved.

The invention provides an ultrasonic probe sterile sleeve intelligent matching and image optimization method, which comprises the following steps: acquiring probe surface geometric data and examination part anatomical feature data through a three-dimensional scanning technology, generating a probe shape model and a part adaptation model, and obtaining probe shape adaptation parameters and examination part adaptation parameters; according to the shape adaptation parameters of the probe, adopting a finite element analysis method to simulate the fitting state of the sterile sleeve on the surface of the probe, calculating the fitting degree score of the sterile sleeve, and obtaining a fitting degree quantification result; a support vector machine algorithm is adopted, classification training is carried out on the multi-dimensional matching feature vectors, an intelligent recommendation model is generated, and sterile sleeve recommendation lists for different probes and examination parts are obtained; and if the fitness score of the matching feature vector is lower than a preset threshold value, optimizing geometric parameters of the sterile sleeve through a genetic algorithm, generating improved design parameters of the sterile sleeve, and obtaining an optimized matching precision result.

The invention relates to an underwater robot target detection method based on multi-sensor information fusion, and the method comprises the steps: obtaining the sonar echo, visual image, surrounding water temperature and chemical information of a target through the sonar, visual, temperature and chemical sensors of an underwater robot, adding a timestamp to each piece of information, aligning the information to a unified time axis through an interpolation method, and carrying out the detection of the target. Extracting sensor feature information of the target, fusing the sensor information based on a credibility model, adjusting the credibility of each sensor in real time, performing similarity matching on the fused feature information and a target feature library to identify a potential target object, and within a confirmed target potential range, identifying the target in the target potential range. A probability density function detection algorithm is adopted to judge the existence of a target, temperature and chemical sensor feature information are combined, and a support vector machine algorithm is used to determine the type of the target, so that the problems that the underwater target is relatively high in similarity with the surrounding environment during detection, the features are not obvious enough and accurate detection is difficult are solved.