176 results about "Support vector machine algorithm" patented technology

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 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 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.

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 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 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.

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 discloses a shale reservoir dynamic monitoring method and system based on multi-source data fusion, and the method comprises the steps: collecting underground structure data and shallow change data through a multi-source detection device, and obtaining various types of detection information; classifying data characteristic differences by adopting a support vector machine algorithm according to the acquired various types of detection information to obtain a classified data set; reservoir boundary features and surrounding rock stratum features are extracted from the fused unified data set, and boundary contact interface candidate points are obtained; performing verification analysis on the obtained boundary contact interface candidate points by adopting a random forest algorithm, and judging the position of a real contact interface; if the real contact interface position is judged to be deviated from the initial boundary, adjusting the coordinates of the candidate points through iterative optimization to obtain an optimized interface model; and simulating reservoir space range change according to the optimized interface model, and determining a final reservoir boundary range. By adopting the technical scheme of the invention, the contact interface between the reservoir and the surrounding rock stratum can be accurately identified.

The invention provides an NFC information interaction and management method for an intelligent signboard of power equipment, and the method comprises the steps: extracting field problem feedback data from an obtained synchronous data flow, carrying out the mode recognition and priority sorting of the feedback data through a support vector machine algorithm, and determining a high-priority problem list; according to the determined high-priority problem list, a deep neural network algorithm is adopted to carry out prediction modeling on list data, the future equipment fault trend is judged, and a predictive maintenance suggestion data set is obtained; the obtained predictive maintenance suggestion data set is fused with the real-time interaction content, a closed-loop feedback instruction is generated in a background system, and guidance information update for field operation is obtained; and extracting verification parameters from the obtained guidance information update, performing integrity verification on the parameters through a hash function, judging the reliability of the updated data, and obtaining the finally confirmed closed-loop management information.

The invention provides a power distribution network line fault monitoring and early warning method, which comprises the following steps of: acquiring line vibration data through a sensor array, analyzing vibration frequency distribution by utilizing fast Fourier transform, extracting dominant frequency, comparing the dominant frequency with an inherent frequency database, judging a resonance risk, and further calculating an amplitude change trend by adopting a sliding window algorithm. And evaluating the fatigue accumulation degree by combining a material fatigue model. A Bayesian probability model is innovatively introduced, the influence of current load and wind action is fused, the resonance risk posterior probability is calculated, a support vector machine algorithm is adopted to classify and evaluate the risk, accurate early warning is achieved, finally, an early warning signal is transmitted through wireless communication, a vibration suppression control signal is generated to adjust a damping device, and the vibration suppression effect is achieved. And stable line operation is realized. According to the invention, accurate identification, real-time early warning and active suppression of the resonance risk of the power transmission line are realized, and the operation safety and reliability of a power grid are improved.

The invention discloses a method for measuring ammonia in domestic drinking water by a continuous flow injection method, which comprises the following steps: acquiring ammonia detection original data of different water source types, removing abnormal values and noise interference through a data preprocessing module, establishing a standardized detection data set, and recording operation parameters and environmental condition information of each detection method, the obtained clean basic data matrix is used for subsequent analysis processing; aiming at the identified deviation detection method, a nonlinear mapping relation model between the methods is constructed through a support vector machine algorithm, a kernel function is utilized to process complex method difference characteristics, a detection result conversion matrix under different water source conditions is established, and a standardized method correction parameter set is obtained; according to the corrected detection result data, key factor weights influencing the detection accuracy are analyzed through a random forest algorithm, contribution degrees of variables such as water source types, interfering substance concentrations and operation conditions to the detection accuracy are identified, and key monitoring indexes and threshold ranges of quality control are determined.

The invention relates to a flora transplantation clinical pathway standardization management method, device and equipment and a medium. The method comprises the following steps: on the basis of clinical data of a patient and screening data of a donor, executing flora suitability evaluation by using a deep learning model and selecting an adaptive donor; obtaining a flora sample of an adaptive donor, and carrying out high-throughput sequencing; generating a donor flora quality evaluation score according to a preset flora composition diversity index based on a sequencing result; screening the flora samples of which the quality evaluation scores are higher than a preset quality threshold value to prepare a flora preparation; detecting activity parameters and diversity parameters of the flora preparation, and performing colonization potential classification prediction by combining clinical data of the patient and adopting a support vector machine algorithm so as to output a potential colonization success rate; and when the potential colonization success rate exceeds a preset success rate threshold, outputting a clinical transplantation scheme through a preset transplantation scheme generation algorithm based on the flora preparation and the clinical data of the patient. According to the method, through multi-stage intelligent decision making, the safety and the effectiveness are improved.

The invention relates to the technical field of equipment control, in particular to an automatic control system and method for a pneumatic control valve, and the method comprises the steps that a displacement sensing module collects a valve element displacement signal through a magnetostrictive sensor and carries out Kalman filtering processing; the signal conditioning module fuses displacement and pressure data by adopting a fuzzy logic rule; the intelligent conversion module performs state identification by using a support vector machine algorithm; the communication gateway module realizes data protocol encapsulation and transmission; the state diagnosis module performs fault diagnosis based on a hidden Markov model; the digital twin module realizes simulation prediction through multi-domain physical modeling; the optimization decision module adopts a particle swarm optimization algorithm to generate a control instruction; and the closed-loop execution module drives the execution mechanism and feeds back displacement data. The problem that an existing pneumatic valve is insufficient in control precision due to lack of displacement feedback is solved, and real-time monitoring and high-precision closed-loop control over the position of the valve are achieved.

The invention relates to the technical field of power grids, in particular to a frame circuit breaker fault diagnosis method, and aims to solve the problem that in the prior art, an independent sensor is adopted to collect data item by item, and a data islanding effect is formed. The acceleration sensor only captures mechanical vibration and ignores the early warning value of current zero-crossing deviation to arc reignition; an infrared thermometer is insufficient in spatial resolution and cannot position micro hot spots of a contact surface of a contact; the Hall current sensor can detect the problem of early failure missing detection caused by current abnormity but unassociated vibration energy abrupt change. According to the method, the three-dimensional diagnosis model is constructed by fusing vibration, current and temperature multi-dimensional features, band-specific physical significance separation is realized in combination with a variational mode decomposition algorithm, a fault area boundary is divided by using a support vector machine algorithm, the problems of spectrum aliasing and data islands are effectively solved, and the fault diagnosis accuracy is improved. The method has the advantages that the fault diagnosis accuracy is improved, misjudgment and missing detection are avoided, and electromechanical and thermal multi-dimensional parameter collaborative analysis is realized.

The invention relates to an aptamer electrochemical biosensing system based on a support vector machine. The aptamer electrochemical biosensing system is used for detecting the content of CEA in body fluid. The system comprises an electrochemical detection module and a signal processing module for identifying and correcting a signal value of the electrochemical detection module, the electrochemical detection module is used for detecting and acquiring an electrochemical signal based on a working electrode with CEA aptamer DNA (Deoxyribose Nucleic Acid); the signal processing module comprises an acquisition unit for collecting the electrochemical signals, a feature extraction unit for extracting electrochemical features of the electrochemical signals, a model judgment unit for judging and processing the electrochemical signals, and a signal output unit for outputting final electrochemical signals; the model judgment unit is based on a support vector machine algorithm. According to the system, the detection stability and the pH anti-interference capability can be remarkably improved, high-precision concentration judgment can be realized, and the system has intelligent compensation and engineering practicability.

The invention provides a ransomware detection method and device based on eBPF and machine learning, and relates to the technical field of information security, the method comprises the following steps: mounting an eBPF program to a key system of a target system for calling, and capturing file operation behavior data such as a process identifier and an operation type in real time through an event-driven mechanism; extracting features based on a preset time window, counting process file operation cumulative times, screening out abnormal operation behaviors through threshold filtering, and constructing context features at the same time; utilizing a support vector machine algorithm to train feature data to generate a classification model, and optimizing kernel function parameters to improve small sample scene classification accuracy; and continuously collecting real-time file operation data through an eBPF program, inputting the real-time file operation data into the model, carrying out dynamic classification, and triggering alarm and blocking when the ransomware is detected. According to the method, the system performance overhead is effectively reduced, the detection real-time performance and accuracy are improved, and the model stability is enhanced.

The application relates to an artificial intelligence-based multi-water-source dynamic allocation system and method. The system comprises a data acquisition module, which collects equipment operation data and environmental factor data of multiple water source points through a sensor network and aggregates the data into water source characteristic data; a data classification module, which classifies the water source characteristic data using a support vector machine algorithm to obtain a parameter set; a trend analysis module, which analyzes the change trend of the water source characteristic data using a random forest algorithm; a weight adjustment module, which dynamically adjusts the parameters according to the trend to generate a weighted parameter set; a scheme generation module, which generates a water resource allocation scheme by simulating a multi-water-source coordination scenario based on the weighted parameter set through a pre-trained neural network model; an effect verification module, which verifies the effect of the scheme by using a simulation method; and a scheme optimization module, which optimizes the dispatching parameters according to the verification result to obtain an optimized water resource allocation scheme. The method can realize efficient dynamic allocation of water resources and optimization of overall operation efficiency.

The application relates to a human-machine collaborative control method and system based on an intelligent steering wheel, wherein the method comprises the following steps: generating an electric signal corresponding to a driving operation based on the intelligent steering wheel; establishing a driver's internal intention prediction and psychological state monitoring model according to the electric signal by using a support vector machine algorithm; obtaining a driver's expectation based on the internal intention prediction result and the driver's psychological state; establishing a kinematics and dynamics model of a vehicle; obtaining road information, vehicle state and model constraints including road safety constraints and stability comfort constraints based on a vehicle-road perception module; constructing a human-machine collaborative controller according to the driver's expectation and the model constraints by using a model predictive control algorithm; solving an MPC controller optimization problem to obtain a current optimal front wheel steering angle and longitudinal acceleration, and outputting the current optimal front wheel steering angle and longitudinal acceleration to a vehicle bottom controller to realize human-machine collaborative control. Compared with the prior art, the application has the advantages of accurate driver intention prediction and strong human-machine collaboration capability.

The invention discloses a TGV glass substrate double-axis synchronous stable scanning detection method, which comprises the following steps: firstly, importing hole site coordinate data of a TGV glass substrate, dividing a dense sub-region and a sparse sub-region according to hole site space distribution density, generating a variable density scanning path, and realizing dynamic adaptation of detection speed and resolution; then the double-shaft synchronous driving mechanism drives the substrate to move, and the multi-degree-of-freedom visual scanning mechanism automatically switches the light source state and the camera rotation angle according to the type of the detected target and synchronously collects images; the adsorption pressure is monitored in real time in the whole detection process, and when the pressure is lower than a safety threshold value, automatic speed reduction or alarm is performed to guarantee substrate safety; and finally, defect identification and classification are completed through multi-frame image noise reduction, feature extraction and a support vector machine algorithm. The problems that in traditional detection, efficiency and precision are difficult to consider at the same time, the substrates are prone to damage, and the automation degree is low are solved, efficient, high-precision and low-damage batch detection of the TGV glass substrates is achieved, and the method is suitable for online detection scenes of mass production lines.

Provided are a method for regulating a wind-photovoltaic-storage power station based on electricity, green certificate, and carbon price prediction, a device, a medium, and a product. The method includes: inputting acquired historical price data into a price prediction model, and outputting a predicted price; determining a deviation vector of price data based on the historical price data and the predicted price, and generating an uncertainty set of the predicted price by using a multi-kernel-based one-class support vector machine algorithm; classifying the uncertainty set of the predicted price by using a neural network classifier, to obtain multiple types of price scenarios; solving, based on predicted prices under the multiple types of price scenarios, a joint clearing model by using a Pied Kingfisher Optimization (PKO) algorithm, to obtain an operation strategy for the wind-photovoltaic-storage power station; and regulating the wind-photovoltaic-storage power station based on the operation strategy for the wind-photovoltaic-storage power station.

The invention relates to the technical field of postoperative neck deformation monitoring, in particular to a multi-modal fusion postoperative neck deformation monitoring and grading alarm system which comprises a multi-modal data acquisition module, a data processing module, a grading alarm module, a display module and a power management module. The system acquires data through multi-mode sensors such as an ultrasonic detection array and a pressure sensing matrix, and combines the functions of real-time correction, abnormal data restoration and dynamic adjustment, so that the monitoring precision and the alarm accuracy are remarkably improved. The grading alarm module generates an alarm signal by adopting a support vector machine algorithm, and the display module realizes multi-view synchronous display. According to the application, the safety and efficiency of postoperative neck deformation monitoring can be improved, and reliable technical support is provided for clinical nursing.

This invention provides a method and apparatus for predicting the risk of credit bond investment transactions, relating to the field of artificial intelligence technology. The method includes: collecting transaction data during the credit bond investment transaction process, the transaction data including training set data and prediction set data; performing supervised training based on the training set data and a support vector machine algorithm to obtain a credit bond risk prediction model; performing risk prediction on the prediction set data based on the credit bond risk prediction model and determining the prediction accuracy; using the prediction accuracy as the fitness value of a cuckoo search algorithm, iteratively optimizing the penalty factor and kernel parameters in the credit bond risk prediction model using the cuckoo search algorithm to obtain the risk prediction model; and performing risk prediction on target transaction data during the target credit bond investment transaction process based on the risk prediction model. This invention improves the prediction accuracy of the risk prediction model and reduces learning costs, thereby enhancing the accuracy of credit bond investment transaction risk prediction.

The invention discloses an intelligent recognition method for website tampering, and the method comprises the following steps: S1, website data collection: carrying out the full-amount real-time collection of the source code data of all levels of pages of a target website through a computer system, and enabling the collection depth to be configurable; s2, website data preprocessing: carrying out privacy protection and desensitization processing on the collected data, and sending the processed data to a message queue for decoupling caching; s3, website data feature extraction: extracting key features used for identifying abnormal changes of website contents from the preprocessed data; and S4, intelligent identification of website tampering: the extracted features are classified by using the trained support vector machine model, whether the website is tampered is identified, and the support vector machine model constructs a decision function by maximizing a classification interval. Detection is carried out depending on a support vector machine algorithm, and the website tampering problem is quickly responded; the detection process is self-optimized and self-learned, and the adaptive capacity of dynamic changes of the website is improved.