160 results about "Local outlier factor" patented technology

A fault detection method for refrigeration units based on an improved deep learning model is provided, including the following steps: S1: obtaining operating parameters of a refrigeration unit in a normal operating state and in states with different fault types as data sets; S2: detecting local outliers in the data set by using a local outlier factor algorithm and removing the local outliers, and then expanding the data set by using adaptive synthetic sampling; S3: normalizing the data set; S4: constructing a fault detection model; and S5: inputting the parameters of the tested refrigeration unit into the fault detection model, and judging whether the tested refrigeration unit has a fault and the type of the fault.

A method for monitoring temperature-induced deformation of components in an intelligent moxibustion robot based on infrared spectroscopy includes the following steps. Infrared spectral images and frequency spectra of a target component of the intelligent moxibustion robot at different detection points are acquired. Motion influence confidence factors for each detection position are constructed based on frequency differences between peaks and troughs in the frequency spectrum. The box-counting method is used to obtain scale-relationship graphs of infrared spectra for all detection positions. The overall light absorption difference index of the target component is determined according to the scale-relationship graphs. By combining the overall light absorption difference index with motion influence confidence factors, local outlier factors (LOF) for each detection position in the thermal data sequence are calculated using a LOF anomaly detection algorithm. Finally, a temperature deformation risk of the target component is evaluated based on a thermal alarm threshold.

The invention provides an industrial anomaly detection and root localization method and system based on data driving, and the method comprises the steps: carrying out the cleaning, feature extraction and normalization processing of original data collected in an industrial production process, and constructing a feature space; based on a local anomaly factor LOF and a mahalanobis distance MD method, jointly detecting local anomaly and global anomaly, and identifying an abnormal working condition; extracting space and time correlation characteristics of the abnormal variables through Pearson correlation weighting and Granger causal test to form a space-time correlation matrix; constructing an abnormal causal network based on the matrix, and tracing an abnormal root and a propagation path through depth-first search and abnormal propagation intensity evaluation; and finally, dynamic optimization of the anomaly detection and diagnosis method is realized based on parameter self-adaption and model incremental learning. According to the method, the anomaly detection accuracy and the anomaly traceability interpretation capability can be effectively improved, and the intelligent level and the self-adaptive capability of data processing are enhanced.

The invention relates to the technical field of electrical digital data processing, in particular to an airborne navigation data cleaning method based on multi-source sensing data, which comprises the following steps: acquiring parameter data of airborne navigation at a plurality of historical time nodes, constructing a parameter sequence, and constructing a multi-scale window by taking any parameter data in the parameter sequence as a center; and calculating an abnormal score of any parameter data under any scale window by using an improved local outlier factor algorithm, judging the parameter data of which the mean value of the abnormal scores of any parameter data under all scale windows is greater than a set threshold value as abnormal data, and cleaning the abnormal data. The problem of misjudgment or missed judgment in the data cleaning process is solved.

The invention provides an electronic current transformer operation data calculation system, and relates to the technical field of data processing, and the system comprises a data processing module which is used for carrying out the sliding time window scanning of an equipment dependence adjacency matrix, calculating the local reachable density under a dynamic neighbor topological distance through a local outlier factor algorithm, if it is detected that the harmonic total distortion rate gradient enters a statistical hypothesis test rejection domain or the winding temperature rise rate exceeds a material thermal aging critical threshold, extracting a multi-dimensional abnormal feature vector set; the risk quantitative index generation module is used for generating a risk quantitative index group from the multi-dimensional abnormal feature vector set through a pre-trained fault propagation probability model; and the processing instruction set generation module is used for establishing a multi-objective optimization function based on the risk quantification index group, performing non-dominated sorting evolution on a processing instruction chromosome by adopting an NSGA-II genetic algorithm, screening a dynamic optimization instruction sequence matched with a real-time load fluctuation coefficient through a Pareto frontier solution set, and generating a structured processing instruction set. The operation reliability of the power system is improved.

The invention discloses a data optimization storage method based on artificial intelligence, and relates to the field of data storage, and the method comprises the steps: collecting the storage rate data of a storage unit and the transmission rate data of a data transmission unit; carrying out anomaly detection on the storage rate data by adopting a density-based local anomaly factor LOF algorithm; adopting a multi-model combination prediction method based on SARIMA and LSTM to obtain a predicted transmission rate sequence at the next moment; calculating a residual error between the acquired transmission rate data and the predicted transmission rate sequence; constructing a deep reinforcement learning model based on near-end strategy optimization PPO; and constructing a multi-target reward function according to the storage rate abnormal point sequence and the transmission rate residual sequence, training a deep reinforcement learning model by maximizing the target reward function, and generating an optimal data transmission rate control strategy. Aiming at unbalanced utilization of storage resources in the prior art, the application balances the utilization of the storage resources.

The invention discloses an abnormal traffic flow detection method and system based on a dynamic graph, and is applied to the field of abnormal traffic flow detection. The method comprises the following steps: dividing a city into a plurality of sub-regions through city road network data, and obtaining vehicle trajectory data to construct a traffic flow tensor; constructing an approximate graph based on the traffic flow tensor and performing data enhancement to generate a local view and a global view of the traffic flow tensor; designing a comparative learning model with a time graph encoder to perform joint training on the multi-view data, and capturing space-time dependency features; and acquiring a real-time traffic flow embedding vector by adopting a sliding window, identifying an abnormal traffic mode through a local abnormal factor algorithm, and finally realizing the positioning of an urban abnormal traffic area. According to the method, through fusion of the time graph encoder and comparative learning, the accuracy of traffic anomaly detection in a complex road network environment is effectively improved, and reliable technical support is provided for urban traffic intelligent management.

The invention discloses a battery consistency detection method, which analyzes voltage time sequence data of battery cells in a battery cluster by introducing a local outlier factor algorithm, and can accurately identify outlier battery cells with abnormal voltage behaviors in charging and discharging processes. Compared with a traditional index threshold value judgment method, due to the fact that the local outlier factor algorithm and the reinforcement learning mechanism are combined, the optimal algorithm parameters are automatically searched, the method does not depend on manually-set experience threshold values any more, and the detection stability and reliability are improved. Meanwhile, compared with a statistical feature method, the method can more effectively capture local abnormal features in the battery cell voltage data, is not limited by overall data distribution hypothesis, has higher tolerance to abnormal data, and has smaller dependence on a judgment threshold. The method can effectively improve the precision of consistency detection of the battery cells in the battery cluster, provides a more reliable basis for subsequent battery cell charging, and further remarkably improves the overall capacity utilization rate of the battery cluster, prolongs the service life, and reduces the operation and maintenance cost.

The invention discloses a millimeter wave water surface flow velocity measurement radar CFAR detection method and system, and the method comprises the steps: eliminating an outlier in advance through a local outlier factor, carrying out the estimation of the quartile distance of a sample through the screened data, obtaining a cut-off threshold value of the sample, and finally obtaining a background clutter power estimation value through the maximum likelihood estimation. According to the method, the selection of the quartile optimization truncation threshold is introduced, and the reference window sample is screened in advance by using the local outlier factor, so that the robustness of the quartile and the truncation threshold is enhanced; according to the method, the environmental adaptability of the algorithm is enhanced, the target detection performance of the algorithm in a multi-target and clutter edge environment is effectively improved, and a feasible new scheme is provided for target detection of the millimeter wave water surface flow velocity measurement radar.

The invention discloses a power distribution switch terminal communication state analysis method and system, and belongs to the technical field of power distribution network communication. The method comprises the following steps: acquiring dual-source parameters of a terminal and a communication link, and preprocessing to form a time sequence data sequence; an improved local outlier factor algorithm is combined with spatio-temporal clustering to identify abnormal communication events; extracting an analysis sequence containing event context based on the dynamic window; respectively performing communication state evaluation on a terminal side and a link side through a multi-task deep learning model, and outputting state classification and severity score; and when the evaluation results on the two sides conflict, intelligent decision making is performed by adopting a multi-level arbitration mechanism based on an evidence theory and integrating factors such as confidence difference, a historical trend and topological association, so that accurate responsibility determination of a fault source is realized. The method solves the problems that the traditional threshold alarm is high in false alarm rate and the fault source cannot be positioned, and remarkably improves the accuracy and operation and maintenance efficiency of communication state diagnosis.

The invention discloses a water quality monitoring intelligent early warning method and system based on multi-dimensional data analysis, and belongs to the technical field of water quality monitoring. The method comprises the following steps: processing missing values and noise through a dynamic weighted data filling algorithm to obtain complete water quality data; carrying out multi-parameter collaborative dimensionality reduction by adopting an improved t-SNE algorithm, and mapping high-dimensional water quality parameters to a three-dimensional feature space; constructing a dynamic watershed partition model based on a Delaunay triangulation network and DEM data; calculating a spatial anomaly score through a weighted local outlier factor; performing time anomaly detection in combination with STL (Standard Template Library) decomposition and a Transform model; multi-scale fluctuation detection is realized by using wavelet packet decomposition; fusing space, time and parameter dimensions to construct a three-dimensional judgment matrix, and generating a comprehensive anomaly score; and triggering a multi-level early warning mechanism according to a scoring result. The system can monitor the water quality change in real time, and gives out early warning in time when abnormity occurs, so that the water quality safety is guaranteed.

The invention discloses a bacterial liquid capsule production control method and system based on the Internet of Things, and belongs to the technical field of production control. Abnormal values are recognized and eliminated in a first data set by combining a local outlier factor algorithm and a K-means outlier threshold value, the abnormal values in the data are effectively recognized, the real effectiveness of the data is improved, and the production efficiency of the bacterial liquid capsule is improved. And fuzzy division is carried out on a historical normal data set through a clustering algorithm to obtain a clustering center value of each clustering cluster, each data in the second data set is clustered, and a corresponding abnormal value is repaired through a mean value in the corresponding cluster, so that data missing after the abnormal value is removed is avoided, the continuity of the data is ensured, and the production efficiency is improved. And the effectiveness of production control is further improved.

The invention discloses a method for repairing reliability of a whole fracturing pump based on dynamic fault analysis, which comprises the following steps of: S1, acquiring operation data of the whole fracturing pump, and preprocessing and storing the operation data by utilizing edge computing equipment; s2, extracting short-term features and long-term features of the operation data by adopting an adaptive segmentation method of a dynamic window, and carrying out anomaly detection by combining an isolated forest and a local anomaly factor method; s3, constructing a fracturing whole pump fault propagation model, updating the fault propagation probability by using a Markov chain Monte Carlo method, and generating a fault propagation influence matrix; s4, evaluating a fault mode by adopting a dynamic fault tree-Markov chain conjoint analysis method to form a fault influence degree matrix; s5, potential failure point prediction is carried out, and a fault evolution trend chart is generated; and S6, constructing a three-layer optimal scheduling framework by adopting an optimal scheduling method of a game theory, and generating an optimal repair strategy. According to the method, dynamic fault analysis, game theory optimization and the like are combined, and accurate prediction and intelligent repair of the whole fracturing pump are achieved.

The invention is suitable for the technical field of virtual power plants, and provides a virtual power plant data processing method and system. According to the method, data acquisition is carried out on a plurality of application ends, and data preprocessing is carried out through a plurality of edge data nodes, so that a plurality of pieces of effective acquisition data are obtained; transmitting the plurality of effective acquisition data to a cloud end, and performing data integration and standardization through the cloud end to generate standard integrated data; neglecting defect attribute data, and adopting a C4.5 decision tree algorithm to perform classification mining on defect-free attribute data to obtain classification mining data; and performing outlier detection of data points on the classified mining data, calculating a plurality of local outlier factors, and performing abnormity checking and processing. According to the method, cloud edge collaboration can be carried out, data preprocessing is carried out through edge data nodes, the computing pressure of a cloud end is effectively reduced, the data processing efficiency is improved, a C4.5 decision tree algorithm is adopted, data classification mining is carried out, outlier detection and anomaly checking and processing are carried out, and accurate use of data is ensured.

The invention relates to the technical field of monitoring and alarming, in particular to an online monitoring method and system for a coking coal detection chamber, and the method comprises the steps: collecting parameter data of a current time node in the coking coal detection chamber and parameter data in a plurality of historical set time windows; calculating an abnormal score of the parameter data of the current time node under any historical set time window by using an improved local outlier factor algorithm to obtain abnormal scores of the parameter data of the current time node under all historical set time windows, and when the mean value of the abnormal scores in all the historical set time windows is greater than a set abnormal threshold value, determining that the current time node is abnormal, and triggering an alarm device to perform early warning. The problem that an existing algorithm is not high in detection accuracy is solved.

The invention discloses a mobile phone USIM (Universal Subscriber Identity Module) side channel analysis method based on an outlier. The method comprises the following steps: S1, collecting a power consumption signal generated in the process of executing an AES (Advanced Encryption Standard) algorithm by a USIM card; s2, filtering and aligning the acquired power consumption signal to generate standardized side channel curve data; s3, for each key byte position participating in S box operation in the first round of AES, constructing a candidate key byte value, and calculating an S box output value; s4, recognizing recovered and uncertain key bytes by adopting a correlation energy analysis method; s5, deriving S box output byte positions influenced by the uncertain bytes in the second round; s6, calculating the correlation of the candidate value at the position, and generating a correlation feature vector; s7, inputting a local outlier factor algorithm, and calculating an outlier value; and S8, selecting the key byte with the maximum outlier as a final key byte to form a key byte set. According to the invention, the accuracy and robustness of USIM key recovery are improved in a low signal-to-noise ratio scene.

The invention discloses a project cost prediction method fusing MDS-LOF and GBRT. The method comprises the steps of S1, selection and primary processing of project cost data; s2, performing project feature analysis and reserving core data; s3, performing dimension reduction processing by using a multi-dimensional scaling analysis (MDS) method; s4, using a local outlier factor (LOF) algorithm to identify abnormal values and removing the abnormal values; s5, training is carried out by using the GBRT prediction model and the processed data, and a trained model is obtained; and S6, outputting and verifying the trained prediction model. According to the invention, by constructing the adaptive prediction model, the influence caused by sudden policy regulation and control is weakened, so that the project cost prediction result is more in line with the market law of the building construction cost, and scientific basis and reference value are provided for project early-stage decision making and cost prediction of investment subjects such as enterprises and governments in a complex policy environment period.

An infrared-spectroscopy-based method for monitoring temperature-induced deformation of a component of an intelligent moxibustion robot. The method comprises: collecting an infrared spectrum and a frequency spectrum of each detection position on a target component of a moxibustion robot; on the basis of frequency differences between peaks and troughs in the frequency spectrum, constructing a motion influence confidence factor of each detection position; using a box-counting method to acquire a scale relationship graph of the infrared spectrum of each detection position; on the basis of scale relationship graphs of all the detection positions, determining an overall absorbance difference index of the target component; on the basis of the overall absorbance difference index and the motion influence confidence factor, using a local outlier factor (LOF) outlier detection algorithm to calculate an LOF of each detection position in a thermal data sequence; and on the basis of a thermal alarm threshold value, determining a temperature-induced deformation risk of the target component. The method can eliminate spectral difference variations caused by shadows and reflectance properties, thereby improving the precision of determining high-temperature deformation of components.

The invention discloses a real-time anomaly detection method for a lithium ion battery module in an energy storage system, and aims to solve the problems of complex battery operation state, insufficient anomaly detection real-time performance and strong dependence on an abnormal sample, the method combines the space-time characteristics of battery operation data, and takes a local anomaly factor algorithm as a core to construct a detection framework. And a detection threshold calculation method based on data features is designed. Through analyzing local density difference and distribution characteristics of battery operation data, a threshold value is calculated according to actual characteristics of the data, and accurate identification and positioning of abnormal points are realized. The abnormal lithium ion battery in the energy storage system can be rapidly and accurately detected in combination with the local abnormal factor algorithm and the optimized detection threshold value, the method adapts to complex abnormal modes possibly occurring in the battery operation process, the false alarm rate and the missing report rate are remarkably reduced, and important technical guarantee is provided for safety and stability of the energy storage system.

The invention provides a personnel trajectory studying and judging method and system based on a local outlier factor detection algorithm, and the method comprises the steps: collecting the trajectory data of an operator, carrying out the data preprocessing of the collected trajectory data, and obtaining a standardized trajectory data sequence; based on the standardized trajectory data sequence, extracting spatial features, time features and context features of the trajectory points; calculating a local outlier factor value of each track point by adopting a local outlier factor algorithm based on the extracted features, and generating an outlier sequence; a sliding window technology is adopted to analyze the outlier sequence, when all local outlier factor values in a window are larger than 1, an abnormal track is judged, and corresponding alarm information is generated for track points judged to be abnormal and pushed in real time; and receiving the alarm information and pushing the alarm information to a manager. According to the invention, through multi-feature fusion and real-time monitoring, accurate identification of abnormal tracks is realized, and the operation safety management level is effectively improved.

The invention relates to the technical field of concrete structure monitoring, in particular to a concrete structure service entity durability key index monitoring and analyzing method. Comprising the following steps: performing multi-source data acquisition on a concrete structure through a sensor network, including temperature, humidity, resistivity, steel bar electrochemical parameters, cracks, porosity and the like; acquired data is subjected to synchronous preprocessing and multivariable empirical mode decomposition, and key evolution factors such as temperature and humidity collaboration and electrochemical activity are extracted. Based on the factors, a space-time graph neural network model is constructed, and multi-dimensional features and evolution laws among nodes of the structure are deeply analyzed. And then, a local outlier factor algorithm is adopted to detect structural abnormal nodes and distribution thereof, and finally, historical and environmental data are combined to perform adaptive classification and classification abnormity, and a comprehensive monitoring analysis report is output. According to the invention, the intelligent, precise and dynamic level of concrete structure key index monitoring is comprehensively improved, and the safety guarantee and operation and maintenance efficiency of the concrete structure are improved.

The invention discloses a power consumer group intelligent identification method, system and device based on a PSO-KMeans algorithm, and a medium, and belongs to the technical field of power big data analysis, and the method comprises the steps: collecting original load data of power consumers, carrying out the preprocessing of the original load data, obtaining standardized load data, extracting multi-dimensional features, and constructing a weighted feature matrix based on an entropy weight method; performing coarse clustering by utilizing spectral clustering to generate an initial clustering center set, taking the initial clustering center set as an initial particle position of an improved adaptive inertia weight PSO algorithm, optimizing a K-Means clustering center, and outputting a global optimal clustering center; and performing clustering by taking the result as a K-Means initial center to obtain a final user group, and identifying abnormal power utilization suspected users in combination with a contour coefficient and a local outlier factor. According to the method, high-quality and high-stability user grouping and accurate anomaly detection are realized, and efficient technical support is provided for power user management and demand side response.

The invention relates to the technical field of electric digital data processing, in particular to a real-time monitoring method and system for sludge solidification stirring equipment, and the method comprises the steps: obtaining multi-dimensional parameter data of the sludge solidification stirring equipment at a plurality of historical time nodes; constructing the parameter data of the same dimension into a parameter sequence in a set time window; acquiring the local fluctuation degree of the parameter data of any time node in the parameter sequence to which the parameter data belong, and the linear correlation degree between the parameter data and other parameter sequences; determining the parameter data greater than a linear correlation degree threshold as strong cooperation parameter data; and respectively calculating first abnormal scores of the parameter data and the strong cooperation parameter data of any time node by using an improved local outlier factor algorithm, averaging the first abnormal scores to obtain a second abnormal score, and responding to the parameter data of which the second abnormal score is greater than a set threshold value as abnormal parameter data. The problem that an existing algorithm is not high in detection precision is solved.

The invention discloses a road slope deformation early warning and monitoring system for geological data analysis. The system comprises a parameter acquisition module, a parameter association mapping module, a feature extraction enhancement module, a model training reasoning module, an early warning threshold judgment module and an early warning response module. The parameter acquisition module acquires data such as slope displacement and the like and transmits the data to the parameter association mapping module, the parameter association mapping module analyzes a parameter coupling relationship and then transmits a feature tensor to the feature extraction enhancement module, and the module disassembles and fuses features and transmits the features to the model training reasoning module. The model training reasoning module utilizes an optimized multi-head attention mechanism and a road slope local outlier factor model to calculate an anomaly degree, the anomaly degree is transmitted to the early warning threshold value judgment module to be matched with an early warning level, and then the early warning response module triggers a corresponding response. And the feature extraction enhancement module and the model training reasoning module comprise a plurality of units which play roles respectively. The system comprehensively captures the deformation characteristics of the slope, and improves the early warning accuracy and reliability.

The invention discloses an information data processing system based on brain wave signals, and particularly relates to the field of medical signal processing and brain science application, and the system comprises a data importing and filtering module which supports the input of an original electroencephalogram file and carries out band-pass filtering on the original electroencephalogram file; the bad track detection module is used for automatically detecting and marking bad tracks based on a local outlier factor algorithm, and comparing local density differences between each channel and surrounding channels; the artifact suppression module is used for correcting non-engraving plate transient artifacts in the electroencephalogram; the data restoration and interpolation module is used for carrying out interpolation on the detected bad track and restoring the overall channel layout; the frequency band and ratio characteristic module is used for calculating energy, relative power and ratio of delta, theta, alpha, SMR, beta and betaH frequency bands; the spectrum-space-time characterization module is used for constructing the features into two-dimensional tensors; and the judgment module is used for outputting the mental state category and the attention score. The mental state can be identified based on the juvenile electroencephalogram.

The invention discloses a new energy operation multi-temporal-spatial-scale situation awareness method. The method comprises the steps of multi-source heterogeneous data acquisition and preprocessing, multi-temporal-spatial-scale feature extraction and fusion, uncertainty quantification and probability prediction, edge cloud collaborative real-time situation assessment and multi-target collaborative optimization and feedback control. The invention belongs to the technical field of smart power grids, and particularly relates to a new energy operation multi-temporal-spatial-scale situation awareness method, which adopts multi-source heterogeneous data acquisition and preprocessing, clarifies data sources, preprocesses data through temporal-spatial alignment and anomaly detection, reconstructs an error to capture a complex anomaly mode by using a variational auto-encoder, and achieves the multi-temporal-spatial-scale situation awareness. Detecting a hidden equipment fault; edge cloud is adopted to cooperate with real-time situation assessment, a lightweight anomaly detection model is deployed on the edge side through a knowledge distillation technology, a local outlier factor algorithm is adopted to carry out millisecond anomaly detection, update parameters are issued through cloud digital twin modeling, and the communication efficiency is optimized.

The invention provides an energy storage equipment detection method and device, and electronic equipment, and relates to the technical field of power system energy storage. The method comprises the following steps: acquiring charging and discharging data of each single battery in the energy storage equipment; constructing a multi-dimensional feature vector based on the charging and discharging data of each single battery; processing the multi-dimensional feature vector to obtain a local outlier factor value of each single battery; and determining whether each single battery is abnormal or not based on the local outlier factor value of each single battery. The method solves the technical problems that an existing energy storage equipment abnormal behavior detection method is insufficient in mass data processing capacity and limited in detection algorithm efficiency.

The invention discloses a power grid operation abnormal area identification method and system, and the method comprises the steps: calculating new energy output data based on meteorological data, constructing a unit combination model through combining a generator set parameter and a power grid structure parameter, carrying out the whole-year time sequence production simulation considering a power flow network frame, and generating power grid operation time sequence data; performing feature extraction on the power grid operation time sequence data by using a convolutional neural network to obtain a deep feature vector, extracting a time-space correlation feature in the deep feature vector, outputting an abnormal probability, and screening an area of which the abnormal probability exceeds a preset threshold value as a candidate abnormal area; for candidate abnormal regions, constructing an electrical association neighborhood for each candidate region, calculating local reachable density and a local outlier factor value based on a deep feature vector, and performing abnormal degree quantitative sorting; and performing electrical logic verification, equipment state association verification and time trend verification on the sorted abnormal regions, and performing risk grade division based on a local outlier factor value and an influence range.

The invention belongs to the technical field of authority management, and discloses a unified account authority management platform based on cloud computing, and the platform comprises a data collection module which is used for collecting user parameter data, user behavior data, platform parameter data and cloud environment parameter data; the data processing module influences local reachable density by adjusting the number of nearest neighbor sample points and a sensitivity coefficient based on a local outlier factor, and further detects and rejects outliers of the user parameter data, the user behavior data, the platform parameter data and the cloud basic environment data; obtaining a parameter feature data set, a behavior feature data set, a platform feature data set and a cloud environment feature data set; performing correlation analysis and fusion on the parameter feature data set, the behavior feature data set, the platform feature data set and the cloud environment feature data set to obtain a comprehensive feature data set; the accuracy of risk assessment is improved, the account risk level is quickly judged, and measures can be taken in time.

The invention provides a neurosurgery craniocerebral postoperative drainage monitoring method and system, and relates to the technical field of medical instruments, and the method comprises the steps: collecting intracranial pressure data, body position change data, drainage bag height data and liquid flow rate data; the intracranial pressure data, the drainage bag height data and the liquid flow rate data are preprocessed based on a local outlier factor and a Z-score standardization algorithm; performing coupling analysis on the preprocessed intracranial pressure data and the preprocessed liquid flow rate data based on a machine learning algorithm to obtain a dynamic drainage dynamic index; performing dynamic correction analysis on the dynamic drainage dynamic index and the preprocessed drainage bag height data based on a logistic regression algorithm to obtain a height correction drainage efficiency index, and further adjusting the height of the drainage bag; based on the neural network model, constructing a drainage bag automatic adjustment model, and then obtaining a height automatic adjustment command; automatically adjusting the height of the drainage bag according to the automatic height adjusting command; the nursing workload is greatly reduced.