41results about How to "Improve forecast" patented technology

The invention provides a distributed computing resource smart evolution method and system based on digital twinning, and relates to the technical field of resource evolution.The distributed computing resource smart evolution method comprises the steps that a smart sensing probe is deployed at a computing node to collect operation data, a double-layer attention mechanism is used for extracting features, and a digital twinning model is constructed based on a graph neural network; a resource allocation strategy and a scheduling decision are optimized in combination with a hierarchical reinforcement learning framework, a prediction sequence is generated through an attention-enhanced sequence prediction network, and finally an optimal configuration scheme is solved by adopting a particle swarm algorithm. According to the method, the utilization rate of distributed computing resources can be improved, the energy consumption of the system is reduced, and intelligent scheduling and optimal configuration of the computing resources are realized.

The invention discloses a low earth orbit satellite Internet of Things communication method and system based on a dynamic block chain, and the method specifically comprises the steps: taking at least one ground station as a consensus node in each network partition, forming a consensus network, and selecting a main node from a plurality of consensus nodes of each consensus network through employing an RBFT algorithm; a satellite transit algorithm is adopted to predict transit information of each low-orbit satellite, and the low-orbit satellites passing through any consensus network are set as accounting nodes of the consensus network according to the transit information; the method comprises the following steps: acquiring a service request of a satellite communication terminal, forwarding the service request to a main node of a consensus network to which the service request belongs through a low-orbit satellite of a transit satellite communication terminal, performing verification and priority ranking on the service request according to the main node, and if the verification is passed, performing broadcast and consensus verification on all nodes of the current consensus network. According to the invention, the communication efficiency and safety of the low-orbit satellite Internet of Things are improved, and reliable technical support is provided for satellite Internet application.

The invention discloses a shield tunnel risk early warning method and system based on digital twinning and federated learning, and the method comprises the steps: a shield tunnel construction side client serves as an edge calculation terminal, and obtains basic parameters; a first shield digital twinning system, a second shield digital twinning system and a third shield digital twinning system are correspondingly constructed by the shield machine producer client side, the shield tunnel construction side client side and the third-party client side according to the basic parameters; a shield construction risk early warning model is arranged in each shield digital twin system; the shield machine production side client, the shield tunnel construction side client and the third side client are all participants, and each participant sends model parameters of the respective shield construction risk early warning model to the central server; the central server is used for averaging the parameters of the three models and issuing the average value of the parameters to each participant; and each participant updates the model of the participant according to the average value of the parameters, and realizes shield construction risk early warning based on the updated model.

The invention relates to the technical field of charging load prediction, in particular to an electric vehicle charging load prediction method and system combining sequence decomposition and feature extraction and a storage medium. Comprising the steps of performing variational mode decomposition on time sequence load data to obtain a plurality of intrinsic mode functions; performing zero padding expansion on each intrinsic mode function, and then converting the intrinsic mode function to a frequency domain; enhancing the frequency domain data through a multi-layer perceptron based on complex domain calculation; constructing a prediction model based on a complex field attention module and a residual module, inputting the enhanced spectrum data into the prediction model, extracting a data characteristic frequency combination through the complex field attention module and the residual module, and carrying out weighted summation on all intrinsic mode functions as the output of the prediction model; and calculating a difference value between real data and prediction data according to a mean square error of a time sequence and an average absolute value error of a frequency spectrum to obtain time domain and frequency domain mixed loss, and updating prediction model parameters. According to the invention, the problem of large prediction error under the influence of random load fluctuation is solved.

The invention provides an ionosphere TEC prediction model integrating double mechanisms of self-attention and self-correlation, and belongs to the technical field of ionosphere delay prediction, and the model comprises a trend prediction module, a correlation analysis module and a sequence fusion module. Wherein the trend prediction module predicts the overall change trends of an ionized layer TEC sequence and space environment parameters by means of an autocorrelation operation mechanism; the correlation analysis module analyzes the mutual relation among the ionized layer TEC sequence, the space environment parameters and the longitude and latitude positions by using a self-attention mechanism; and the sequence fusion module performs further integration and optimization on the overall trend of the ionized layer TEC sequence by using the correlation information obtained by the correlation analysis module, and finally obtains an ionized layer TEC prediction result fusing the space environment and the latitude and longitude position information. According to the method, the spatial environment data and the longitude and latitude position information are fully utilized, and the accuracy of model prediction is greatly improved.

The present invention discloses an intelligent control method for an electric control valve actuator for a ship. The present invention pre-processes and comprehensively evaluates the collected fluid pressure data at a ship control center, first calculates the pressure evaluation value within a set time period, then matches it with a preset range interval to obtain a pressure index, and combines it with an ideal pressure index corresponding to the operating condition, so that the valve opening adjustment can better adapt to the host's requirements for changes in fuel pressure under different working conditions. At the same time, for a gradually rising or falling pressure state, the pressure change rate is calculated and the state interval is divided, and the pressure change index is calculated in combination with weight coefficients of different intervals. The opening optimization value and the adjustment direction are determined according to the pressure change index and a preset index value range, thereby achieving precise control, being able to respond in a timely manner to changes in the operating conditions of the ship's main engine and fluctuations in the pressure of the fuel system, effectively ensuring a stable supply of fuel flow and pressure, and improving the operating efficiency and stability of the ship's main engine.

The invention relates to an Internet of Things equipment deployment and control management method, in particular to a building Internet of Things equipment deployment and control management method. Comprising the following steps: deploying various sensors including an infrared sensor, a camera and an air quality sensor in a building, and collecting personnel flow and environment change data in real time; through data analysis and scene classification, the data are converted into scenes including office peaks, night modes and emergency evacuation of the building; training a scene recognition model by using a random forest classifier; a time sequence prediction model is adopted to predict scene change in a future short period, and a corresponding equipment control instruction is generated; the method comprises the following steps: establishing an incidence matrix of use frequency, spatial position and function interaction between devices by using sensor data, and identifying a device cooperation mode; dynamically generating an equipment deployment scheme through a graph theory algorithm based on the incidence matrix; issuing the generated deployment scheme to each equipment control node; and performing fine adjustment according to real-time data feedback.

The invention discloses an aircraft adaptive interval management and control method and system, and relates to the technical field of air traffic management. An aircraft adaptive interval management and control system comprises a flight conflict basic unit module, a flight conflict scene generation module, a flight conflict resolution module and a flight conflict learning module. According to the method, the aircraft flight conflict scene training set can be constructed by constructing the basic flight unit of the aircraft and generating the flight trajectory vector set and combining the flight conflict generation model; the training set is utilized to train an aircraft autonomous interval management and control model through an improved Markov decision model, so that effective strategy output is realized when a flight conflict occurs; the model is updated through analysis of the test result, the adaptability and accuracy of the autonomous interval management and control model of the aircraft are improved, the flight safety of the aircraft in a complex environment is improved in the process, and the prediction and processing capacity of the model for flight conflicts is enhanced.

The invention relates to the technical field of logistics vehicle scheduling, in particular to a logistics vehicle scheduling method based on a visual perception industrial vehicle ADAS system. The method comprises the following steps: acquiring the speed of a logistics vehicle, and further acquiring a reference time length for a target vehicle to reach a specified position at a previous moment; predicting a target position of the non-idle logistics vehicle according to the speed of the non-idle logistics vehicle and the path length from the position of the non-idle logistics vehicle in the planned path corresponding to the reference duration to the specified position; and based on the planned advancing direction and position of the target vehicle at the current moment and the target position and speed of the non-idle logistics vehicle, obtaining a target prediction duration, correcting a cost function of the target vehicle arriving at the designated position in the A-star algorithm, and obtaining a corrected cost function to schedule the logistics vehicle. According to the method, the cost function in the A-star algorithm is corrected, so that congestion and path conflicts of the logistics vehicles are reduced, and the accuracy and efficiency of logistics vehicle scheduling are improved.

The invention discloses a power load prediction method and device based on a hybrid ELM algorithm, electronic equipment and a storage medium, and the method achieves the prediction of a power load through an ELM prediction model (MT-ELM) mixed with MINRES and hidden layer traversal, can achieve the estimation of the number of optimal hidden layer nodes, and eliminates the overfitting. Therefore, the prediction level of the power load is effectively improved. The invention discloses a power load prediction method based on a hybrid ELM algorithm. The method comprises the following steps: S1, performing variation on input and output training sets to construct a training set suitable for an ELM prediction model; s2, calculating a hidden layer output matrix H of the ELM neural network through a calculation mode of a classic ELM model; and S3, traversing and estimating the number of optimal hidden points by using the input and output test sets, eliminating ELM over-fitting through a mixed MINRES method, learning through data over the years and generating an ELM mixed model, and predicting the future short-term power load condition through the generated model.

The invention discloses a project risk dynamic management method, system and equipment based on big data, and the method comprises the steps: obtaining all material data generated in a project whole-process management process based on a constructed project management system, carrying out the data processing of original data through an information processing module, and constructing a project risk database; performing project risk prediction by applying a long short-term memory (LSTM) network based on the project risk database, and judging whether the project has a risk at the moment and a corresponding risk level; if the project has the risk, starting a project risk type identification algorithm, performing risk category identification based on project data by applying a gradient boosting decision tree (GBDT) algorithm, and determining a project risk category; and based on the risk level and type of the project, matching a project risk database to determine risk countermeasures, and pushing the risk countermeasures to related managers in real time. According to the method, the prediction model and the identification model are combined, so that the risk category can be identified more accurately, and the prediction and identification rate of the project risk is improved.

The invention provides a digital tee joint extrusion production method and equipment, and belongs to the technical field of tee joint extrusion production control. The method comprises the following steps: determining a corresponding extrusion operation parameter group sequence in real time; and inputting the extrusion operation parameter group sequence into the three-way production simulation model to determine defect prediction development information. Determining defect correction development information based on the defect prediction development information and preset standard operation regulation and control parameter information; and sending the defect correction development information and the extrusion operation parameter group sequence of the correction time period to a management terminal as defect correction information, so as to store the defect correction information and the evaluation label to a preset database based on the feedback evaluation information. And when a second extrusion material instruction is received, determining a defect correction deviation based on a preset database and the extrusion operation parameter group sequence of the second extrusion material, and updating preset standard operation regulation and control parameter information and / or sending the defect correction deviation to the user terminal based on the defect correction deviation so as to control the three-way extrusion device to produce.

The invention discloses a medical data processing method and system combining big data and smart medical treatment, and relates to the technical field of data processing, and the method comprises the steps: obtaining a historical anatomical record based on the big data, and carrying out the analysis to obtain an anatomical data set; calling a preset data processing mechanism for processing and analyzing to establish an anatomical medical database; calling a predetermined association analysis strategy to perform association analysis to obtain an association list; performing multi-dimensional feature collection on a target user of smart medical treatment to obtain target feature data; collaboratively forming target input data; and activating the intelligent prediction model for analysis to obtain a target output result and performing early warning intervention processing. The technical problems of low medical data processing efficiency, insufficient association between anatomical knowledge and patient individual features and limited intelligent early warning ability in the prior art are solved, and the technical effects of efficiently mining anatomical data values, accurately associating patient multi-dimensional features and improving intelligent medical prediction and intervention ability are achieved.

The present invention relates to the technical field of electric power system, specifically, a low-frequency off-grid automatic control method and device for a new energy electric power system, a fixed platform, and an automatic control device further comprising: an adjustment component is installed on the upper surface of the fixed platform, for actually removing the load; the adjustment component comprises a primary rotating member, a secondary rotating member and a tertiary rotating member, the primary rotating member is completely identical to the tertiary rotating member, the primary rotating member contacts one of the secondary rotating members, and the other secondary rotating member contacts the tertiary rotating member; a crossover connector is fixedly arranged at the bottom of the tertiary rotating member. It can realize the simultaneous opening and closing of multiple load switches, and can also realize the simultaneous opening of some load switches according to the actual situation of the power grid, while the rest are in a closed state. Through the setting of the crossover connector, two non-adjacent load switches can be controlled to be connected, and two non-adjacent load switches can be disconnected.

The present application provides a pore pressure prediction method based on rock physics modeling and multiple linear regression, relates to the oil and gas exploration and development technical field, and the method comprises the following steps: S1: selecting a plurality of reference wells in a secondary structural unit for overpressure analysis; S2: pre-processing the logging data of the reference wells and analyzing the overpressure causes; S3: performing fluid replacement by using the Gassmann equation, and performing solid replacement by using the Brown-Korringa theory, and calculating the rock elastic modulus; S4: selecting an anisotropic soft pore model to calculate the rock effective velocity; S5: performing sensitivity analysis on the elastic parameters and the pressure coefficient; S6: constructing a multiple linear regression model with the elastic parameters having the best correlation with the pressure coefficient, and predicting the pore pressure; and S7: comparing and verifying the prediction result with the Eaton method. The present application avoids the problem of errors caused by relying on the normal compaction trend line, fits the elastic parameters having good correlation with the pressure by using multiple linear regression, comprehensively considers the influence of multiple variables, and has higher prediction and interpretation capability.

A deformation-viewpoint dynamic coupling robot viewpoint intelligent planning method and system, the method comprising: constructing a deformation-view mapping model and training; constructing a space-time graph convolution network and training; constructing a multi-objective hierarchical decoupling optimization network, inputting the multi-modal information in the actual into the trained deformation-view mapping model, generating a coverage prediction value, generating a compensated view according to the deformation compensation layer; the motion optimization layer uses a deep reinforcement learning strategy network and combines an adaptive importance sampling strategy to screen the compensated view and generate a screened candidate viewpoint set; generating a space-time safety corridor according to the trained space-time graph convolution network, scoring the collision risk of the screened candidate viewpoint set according to the safety constraint layer, constructing a multi-objective fitness function and solving, obtaining the optimal viewpoint set. The present application is suitable for high-precision visual detection scene, which significantly improves the real-time performance and robustness of robot viewpoint planning.

The present invention discloses a mechanism-data collaboratively driven carbon dioxide mineralization process inversion method and system, the method includes: determining the mineralization storage target area, based on an ultrasonic phased array monitoring system, an acoustic emission monitoring system, and an optical fiber micro-nano multi-parameter co-mining monitoring system, using the real-time monitoring data signal of the mineralization process as input data; using the physical field models of the mineralization process such as the fracture field, seepage field, and temperature field of the mineralization process as output data. And using simulation software to obtain the physical field model of the mineralization process to further supplement the input and output data; using an LSTM neural network to construct a fusion inversion model of the mineralization process, by inputting the real-time mineralization process reaction measured monitoring data, and fusion inversion output to obtain the physical fields of the fracture field, seepage field, and temperature field in the mineralization process; the method constructs and predicts the physical field model of the complex mineralization storage process through dual-drive of mechanism and data, which is conducive to improving the fusion inversion capability of the mineralization storage process.

The application discloses a mechanical drilling speed prediction method based on micro-inclination characteristics, comprising the following steps: acquiring real drilling wellbore logging while drilling and logging data as a first data set; preprocessing the first data set and extracting the best feature and the micro-inclination feature as input features; the preprocessing comprises data cleaning and correlation analysis; training a mechanical drilling speed prediction model by using the input features; and predicting the mechanical drilling speed of a target well by using the trained mechanical drilling speed prediction model. The micro-inclination feature introduced in the application can significantly improve the ROP prediction accuracy of the model. By introducing the micro-inclination feature in the MLP neural network, the model weight is optimized, and the prediction accuracy of the ROP is improved. By introducing the micro-inclination feature in the SVR model and selecting the optimal hyperparameter, the prediction effect is significantly improved. The method provides a new idea for improving drilling parameter prediction and control by using machine learning, and can be applied to the optimization of drilling speed and efficiency.

The present invention discloses metabolic markers for predicting the risk of liver cancer and their applications. The development and utilization of such metabolic markers will provide technical support for risk prediction and early diagnosis and treatment of liver cancer. The present invention uses a non-targeted metabolomics detection method to screen and verify 44 plasma metabolites associated with the risk of liver cancer, and identifies a group of 18 plasma metabolites that can significantly improve the risk prediction level of liver cancer.

The invention relates to the technical field of laser welding, and discloses a laser welding seam tracking and quality real-time detection system and method based on OCT image guidance. According to the method, through system calibration and offline learning, a coordinate mapping and feature database is established; during welding, the coaxial OCT is used for synchronously scanning a front groove and a rear molten pool area; tracking and correcting a welding path in real time based on the front three-dimensional point cloud; meanwhile, a dynamic feature sequence of the molten pool is extracted and analyzed; inputting the features into a pre-training machine learning model, and judging quality and predicting defects in real time; according to the judgment result, parameters such as laser power and welding speed are adjusted in a self-adaptive mode, and closed-loop control is formed; and full-process data is fed back to optimize the model. According to the invention, synchronous closed-loop control of groove tracking and internal quality detection is realized, and the welding precision and the quality reliability are improved.

The application discloses an AI identification method, system and model training method for millimeter wave image overlapping targets based on a three-branch network, and solves the security target detection problem in millimeter wave images with low resolution, mutual interference or mutual overlap through collaborative work of three independent branch networks, namely, an analysis branch network, an aggregation branch network and a prediction boundary branch network. The analysis branch network saves the detailed information in a high-resolution feature map; the aggregation branch network realizes the feature position offset alignment of high and low resolution feature maps through a recursive fusion module; and the prediction boundary branch network extracts high-frequency features and enhances the perception and prediction of target edges. The multi-branch network structure effectively realizes the segmentation and identification of images through the fusion between branches. While improving the segmentation performance of the model, the model significantly reduces the calculation complexity and the detection error and missed detection probability in millimeter wave images, and finally realizes the accurate detection of overlapping objects.

The invention provides a hybrid power ship energy management method, device and equipment and a medium, and relates to the technical field of ship energy management, and the method comprises the steps: building a power system model of a series hybrid power ship; taking the ship speed data at the moment t and within a set range before the moment t as input data of a dual-channel CNN-LSTM prediction model, and outputting ship speed prediction data within the set range after the moment t through the dual-channel CNN-LSTM prediction model; determining a state variable and a control variable in a ship energy management process, and constructing a dynamic planning mathematical model according to the state variable, the control variable and the power system model; and substituting ship speed prediction data within a set range after the t moment into the dynamic planning mathematical model, and calculating to obtain an optimal ship energy management strategy. According to the method, the energy prediction model is more accurate, the energy use efficiency of the hybrid power ship is improved, and the energy management strategy is improved.

The application provides a relative velocity analysis well-free constraint relative wave impedance inversion method, which comprises the following steps: step 1, velocity analysis is carried out by using seismic data to establish the time-depth relationship of a study area; step 2, seismic facies analysis is carried out on the seismic data to determine the geological model of the study area; step 3, velocity correlation analysis is carried out to determine the velocity migration amount; step 4, the velocity curve of a seismic trace is extracted; step 5, relative velocity normalization analysis of different lithologies at the same depth point is carried out; step 6, relative velocity wave impedance inversion processing is carried out; and step 7, lithology and reservoir identification, tracking and description are carried out. The relative velocity analysis well-free constraint relative wave impedance inversion method breaks through the limitation of conventional inversion which can only be used for single target layer and time domain wave impedance inversion, eliminates the influence of different velocities in time domain and depth domain of multiple target layers, and provides relative wave impedance inversion data for lithology or reservoir prediction under complex conditions in a well-free exploration area.

The invention discloses a test design method for evaluating the service life of a typical structure of a ship, and the method comprises the steps: firstly determining an acceleration test spectrum block which is composed of a temperature and humidity exposure test, an ultraviolet radiation test and a salt spray test; then determining test parameters, wherein the test parameters of the temperature and humidity exposure test comprise temperature, humidity and temperature and humidity exposure action time; test parameters of the ultraviolet irradiation test comprise temperature, ultraviolet irradiation intensity and ultraviolet irradiation time; the test parameters of the salt spray test comprise temperature, salt spray action time, acidic salt spray composition, acidic salt spray pH value, acidic salt spray action time and salt spray settling volume; the temperature and humidity exposure test, the ultraviolet radiation test and the salt spray test are sequentially performed and circulated. The method can accelerate the service life evaluation and prediction of the typical structure of the ship, guarantees the development progress of the ship, and improves the environmental adaptability level.

The invention relates to the field of trajectory simulation, in particular to a high-precision dynamic simulation method and system for the running trajectory of an anti-interference unmanned aerial vehicle, and the system comprises an instruction issuing module which is used for issuing an execution instruction and transmitting a completion feedback signal, a response monitoring module which is used for monitoring the execution time required for completing the execution instruction, and an efficiency monitoring module. The pre-heating module is used for pre-heating execution time, forming corresponding time processing data, selecting corresponding index characteristics, learning the time processing data and generating an execution efficiency map, and the control module is used for comparing a numerical value in the execution efficiency map with an efficiency threshold value, and when the numerical value is lower than the efficiency threshold value, executing the execution time according to the execution efficiency map. The anti-interference unmanned aerial vehicle is controlled to execute the built-in action, whether the anti-interference unmanned aerial vehicle collides when executing the built-in action or not is judged, the anti-interference unmanned aerial vehicle is controlled to execute the built-in action, through machine learning, the flight safety and stability of the anti-interference unmanned aerial vehicle are improved, the operation efficiency and adaptability of the anti-interference unmanned aerial vehicle are optimized, and the operation difficulty and operation cost

The application discloses a kind of based on multi-modal data fusion's injury condition early warning method and device, method includes: obtaining continuous physiological waveform data and relevant structured data;After waveform data is preprocessed, it is converted to two-dimensional image representation by time series topology enhancement algorithm;Structured data is standardized to extract structured features;Through heterogeneous feature correlation weight mechanism, two kinds of features are semantically aligned and complementary fusion;Based on multi-window risk aggregation algorithm, output injury condition early warning result.The application solves the problems of difficulty in waveform and structured data fusion, insufficient use of time series features, and poor early warning stability in the prior art by enhanced image representation of waveform data, deep multi-modal fusion, and multi-scale risk aggregation, improving the accuracy, generalization ability, and engineering practicality of injury condition early warning. It can be widely applied in clinical first aid, intensive care and trauma treatment scenarios.

The invention relates to the technical field of data mining, in particular to an industrial internet system based on a data mining technology, which comprises a data acquisition module, a node construction module, a node quality analysis module and a model training module, and is characterized in that the data acquisition module acquires equipment data and sensor data in the industry; the node construction module is used for constructing nodes according to the acquired data and performing data enhancement on the nodes to form a positive sample pair and a negative sample pair; the node quality analysis module is used for carrying out feature difference calculation on the positive sample pair, repairing the node according to a feature difference result, and generating a complete positive sample pair; feature difference calculation is carried out on the negative samples, nodes are repaired according to the result of the feature difference, and a complete negative sample pair is generated; the model training module initializes parameters in the graph neural network model and calculates the similarity of the complete positive sample pair and the similarity of the complete negative sample pair so as to optimize the parameters in the graph neural network model.

The invention discloses a rapid expansion cloud chamber environment simulation method and system based on artificial intelligence, and the method comprises the steps: obtaining the experimental data and experimental conditions of a to-be-simulated expansion cloud chamber, carrying out the structural classification based on the experimental data according to the experimental conditions and expansion stages, and obtaining the feature vector of each stage, meta learning is carried out through a neural network according to the feature vectors, a cloud simulation model and simulation errors are obtained, a cloud chamber working condition interval is identified based on the simulation errors and experimental data, an output result of the cloud simulation model in a high-deviation interval is obtained according to the working condition interval, and an interval result is corrected through a CFD equation based on the output result. And obtaining a cloud room environment simulation result. According to the method, the cloud expansion stage is split, the fog drop growth process is fitted, CFD local correction is carried out on the simulated high-deviation interval, the prediction error control precision is improved, the prediction and collaborative analysis efficiency of the fog forming process in the complex environment is improved, and meanwhile good interpretability is achieved.

This invention discloses a method for detecting the opening and closing performance of circuit breakers based on dynamic resistance monitoring, belonging to the field of equipment performance testing technology. The method includes: acquiring resistance sensor sampling values ​​during the opening and closing processes of the circuit breaker; constructing a periodic resistance curve based on the resistance sensor sampling values ​​according to each opening and closing cycle; generating a periodic resistance data set; dividing each periodic resistance curve into multiple equally spaced fusion windows; extracting the resistance change slope value, resistance fluctuation amplitude value, and the number of second-order disturbance points in each fusion window to construct a fusion window feature sequence; constructing fitting functions for the resistance change slope value and fluctuation amplitude value based on the multiple fusion window feature sequences of the normal cycle, and superimposing a disturbance response function constructed based on the number of disturbance points; this invention achieves effective identification of performance degradation cycles throughout the entire opening and closing process of the circuit breaker, enhancing the ability to predict and intervene in advance regarding the circuit breaker's operating status.

The invention discloses an electric submersible pump working condition pressure sensor data preprocessing method. The method comprises the following steps: S1, receiving data of a working condition pressure sensor; s2, filtering the received initial data, removing high-frequency noise and interference, and retaining key fluctuation and change information; s3, performing normalization processing on the filtered data, and scaling the data to a specific range to ensure data consistency and comparability; s4, sampling is carried out on the data after linear normalization, the data dimension and storage requirements are reduced, and meanwhile key features of the data are reserved; and S5, outputting data. The method has the beneficial effects that the data quality and stability are improved by preprocessing the data of the pressure sensor; interference of noise and abnormal values is effectively reduced, working condition monitoring accuracy is improved, and prediction and diagnosis capabilities are enhanced; reliable basic data are provided for subsequent analysis and judgment of working conditions, and a solution provided for monitoring and management of an electric submersible pump system is more reliable and efficient.