1060results about How to "Accurately reflect" patented technology

The invention provides a method for constructing a chemical-plastic industry chain knowledge graph by using a graph convolutional network, and relates to the technical field of knowledge graphs, and the method comprises the steps: obtaining the multi-source heterogeneous data of a chemical-plastic industry chain through a data collection system, and forming an initial data set; preprocessing the initial data set to generate a standardized data set; performing knowledge modeling based on the standardized data set to form a domain knowledge model; constructing an initial knowledge graph according to the domain knowledge model; and optimizing the initial knowledge graph by using a graph convolutional network, including constructing a graph convolutional network model, training the model, and performing automatic completion and error correction on the knowledge graph through the trained model to obtain an optimized chemical and plastic industry chain knowledge graph. Through systematic data processing, knowledge modeling and graph convolutional network optimization methods, efficient expression, automatic complementation and error correction of the knowledge of the chemical and plastic industry chain are realized, so that an accurate and reliable chemical and plastic industry chain knowledge graph with an automatic optimization capability is constructed.

The invention discloses an electric power facility visual monitoring system and method, and relates to the technical field of electric power facility operation and maintenance, the system comprises the following components: a multi-source heterogeneous data acquisition module, a multi-source heterogeneous data processing module, a real-time risk assessment and maintenance strategy adjustment module and a visual display module; according to the method, the electric parameters, the environment data, the image video data, the audio data and other multi-source heterogeneous data are integrated, the intelligent fusion algorithm and the association analysis model are utilized, the potential association relationship among different types of data is deeply mined, and therefore the operation condition of the electric power facility can be reflected more comprehensively and accurately, and the operation efficiency of the electric power facility is improved. According to the method, the accuracy and integrity of monitoring data are improved, the system can timely find and pre-judge potential risks, powerful decision support is provided for operation and maintenance personnel, and safety risks and maintenance cost increase caused by incomplete or inaccurate monitoring are effectively avoided.

The invention discloses a mountainous area high pier cast-in-place safety detection method based on multi-source data fusion, and relates to the technical field of intelligent building safety monitoring, and the method comprises the steps: deploying a sensor network, a multi-angle holder video monitor and an unmanned aerial vehicle, covering a mountainous area high pier for comprehensive monitoring, and collecting multi-source data; transmitting the multi-source data to an edge computing node in real time through a wireless network, and preprocessing the multi-source data to form a standardized multi-source data set; applying a spatio-temporal data fusion method, combining historical data and a comprehensive data model to predict a security risk, and generating a risk factor; threshold and rule abnormity identification is carried out on the risk factors, the safety condition is evaluated in real time, early warning is triggered in real time, and disposal suggestions are provided. According to the method, the safety detection efficiency and accuracy in the mountainous area high pier cast-in-place process are remarkably improved through multi-source data fusion and a quantum enhancement multi-modal algorithm, the triggering speed of safety early warning is effectively increased, and the safety guarantee of a construction site is remarkably enhanced.

The invention provides an electric power multi-modal data perception and digital twinborn simulation cooperative interaction method and system, and relates to the field of electric power equipment management and maintenance, and the method comprises the steps: carrying out the electric power multi-modal data perception of data collected by edge equipment, and obtaining the state characteristics of single equipment; constructing a digital twinborn model based on the state features of all devices in the to-be-monitored power system; constructing a power equipment graph knowledge base by using a digital twinborn model, and extracting global state features of the equipment from the power equipment graph knowledge base through a graph neural network so as to construct a global state feature time sequence of the equipment; inputting the global state feature time sequence into the trained time sequence prediction model, and predicting the equipment state at the future moment; according to the multi-modal data, parameters of the digital twin model are adjusted in real time, environmental changes are flexibly adapted, time sequence modeling and fault trend analysis functions are combined, the running state of the equipment is predicted, and the comprehensiveness and foresight of equipment monitoring are further improved.

The invention relates to the technical field of artificial intelligence, in particular to a road surface automatic inspection robot path planning method and device based on artificial intelligence, and the method comprises the steps: collecting road surface environment data in real time through various sensors, carrying out the feature extraction through a convolutional neural network based on the road surface environment data, and constructing a dynamic environment model through a graph convolutional network. According to the method, the change trend of potential road surface damage areas and obstacles is predicted, path planning and optimization are performed by using a deep reinforcement learning algorithm, after path planning is completed, the robot autonomously travels according to a planned path, and real-time adaptive adjustment is performed on the path through an online learning method so as to cope with environmental changes. Collected data and path planning information are fed back to the central control unit in real time to be stored, and a road surface health condition report is generated. According to the invention, the efficiency and the precision of the inspection task are improved, and the method has high environmental adaptability and is suitable for complex and dynamically changing road environments.

The invention relates to the technical field of machine learning, in particular to a supervised time sequence water level data generation method and system and a storage medium. The method comprises the following steps: acquiring multi-source hydrological monitoring data; performing abnormal value identification and data preprocessing according to the hydrological monitoring data to generate standardized hydrological basic data; constructing a data generation model of the conditional generative adversarial network based on the standardized hydrological basic data; hydrological variable driven condition feature regulation and control are carried out on the data generation model, parameter optimization of the generation network is carried out, and hydrological adaptability enhanced condition input feature data are generated; and injecting the condition input characteristic data into the data generation model to generate initial water level time sequence data. According to the method, the rare extreme hydrological events are oversampled by using a hierarchical importance sampling and self-service method (Bootstrap), so that the performance of the generative model in rare event prediction is remarkably enhanced.

The invention provides a wind turbine generator array wake flow management method and system for Doppler laser wind measurement, and relates to the technical field of laser, and the method comprises the steps: collecting the real-time wind speed data, wind direction data and turbulence intensity data of each generator in the front and back direction through deploying a Doppler laser radar in a wind turbine generator array; establishing a wake flow influence model containing a wake flow influence coefficient, an attenuation coefficient and a diffusion coefficient based on the data, and calculating to obtain a wake flow influence range, loss power and a recovery distance; according to the method, the wind turbine generator array is cooperatively controlled, and the power generation efficiency of the wind turbine generator array is optimized by adjusting the yaw angle or the pitch angle of the upstream unit and the rotating speed of each unit. The wind speed data acquisition precision of the wind field is improved, the wake flow influence evaluation accuracy is enhanced, and the overall power generation efficiency of the wind field is remarkably improved.

The invention discloses a precipitation estimation method and system based on territorial rainfall multi-data fusion and application, and relates to the technical field of weather forecast, by fusing radar, satellite and ground observation data, the accuracy and reliability of precipitation prediction are remarkably improved, the fusion of multi-source data can make up for the deficiency of a single data source, and the prediction accuracy and reliability are improved. Through space-time registration and a weighted average fusion algorithm, a macroscopic rainfall background is provided by introducing territorial rainfall data, and radar, satellite and ground observation rainfall data are combined, so that rainfall estimation is more comprehensive, ground observation stations are supplemented and fused, effective data of the stations are increased, the real time of rainfall can be reflected more accurately, and the rainfall estimation accuracy is improved. In addition, the fusion of multi-source data can make up for the deficiency of a single data source, and the advantages of different data sources can be effectively integrated through the space-time registration and weighted average fusion algorithm.

The invention, which relates to the technical field of offshore wave simulation, discloses a wave hydrodynamic numerical simulation method for a sea area with a breakwater, comprising the following steps: S1, determining a target sea area, and collecting sea chart data and water depth topographic data of the target sea area; s2, determining a shoreline position of the target sea area, and establishing a water depth terrain database of the target sea area; s3, setting grid precision, and constructing a wave hydrodynamic two-dimensional model of the target sea area; s4, setting boundary conditions for driving the wave hydrodynamic two-dimensional model, and setting initial parameters; and S5, based on the wave hydrodynamic two-dimensional model constructed in the S3, performing numerical simulation on the target sea area by using a sub-grid method to obtain the characteristic wave height and periodic data of the target sea area. According to the method provided by the invention, the calculation efficiency can be ensured, the wave transmission characteristics of the open type oyster reef are considered, and the influence of open type oyster reef construction on the wave hydrodynamic environment field can be accurately simulated.

The invention provides an energy-saving control method based on dynamic air conditioning operation data, and relates to the technical field of intelligent control and energy-saving optimization of an air conditioning system.The energy-saving control method comprises the steps that operation data, including temperature, humidity, air velocity and energy consumption data, of an air conditioning system are collected, and a comfort index, an air quality index and an energy efficiency index are generated; the method comprises the following steps of: generating an air conditioning index comprehensively, setting a control target parameter of a system, further generating an operation standard value, dynamically generating a control strategy which comprises the steps of adjusting the switching time of a cooling mode and a heating mode of a fan, optimizing the operation speed and time of the fan and adjusting the operation frequency of a compressor, and finally, if the generated air conditioning index exceeds the standard value, controlling the system to operate. And if not, control strategy adjustment is repeated until the air conditioning index is within the standard value. The control strategy is dynamically adjusted by collecting and analyzing the operation data of the air conditioning system, efficient operation of the system is achieved, and the comfort level of a user and the energy-saving effect are improved.

The invention relates to the technical field of neural networks, and further relates to a power grid topology fault positioning system based on a graph neural network. The system comprises a power grid topology feature extraction unit used for regarding a power grid as a graph network and carrying out real-time data acquisition on each node of the power grid, and each element in a feature matrix represents the electrical characteristics of a certain node of the power grid at a certain time; the multi-layer graph neural network model unit is used for taking the feature matrix as the input of a first layer as a feature extraction result; the fault identification and positioning unit is used for calculating fault probability distribution based on the feature extraction result; and calculating the credibility of the obtained fault position. According to the method, the modeling capability of the system on the complex power grid topology is improved, the real-time performance and robustness of fault detection are enhanced, and accurate positioning of the fault area and position is realized.

The invention provides a converter steelmaking endpoint carbon temperature prediction method and device based on adaptive data enhancement, and relates to the technical field of converter steelmaking. The method comprises the following steps: preprocessing data in a historical production data set in a converter steelmaking process; the preprocessed data set is processed through an adaptive SMOTE data enhancement technology; building a steelmaking endpoint carbon temperature prediction model based on the random forest; and training a steelmaking end point carbon temperature prediction model according to the processed data set, and obtaining a converter steelmaking end point carbon temperature prediction result according to the trained adaptive data enhanced steelmaking end point carbon temperature prediction model. The invention aims to improve the sample size and diversity of a data set in the converter steelmaking process by using a data preprocessing method and a self-adaptive SMOTE data enhancement algorithm, remove redundant information, reduce the labor cost of manual arrangement and data processing, and introduce a self-adaptive mechanism according to the data distribution density and different working conditions. And the generalization ability and the prediction precision of the model are improved.

The invention relates to the technical field of physical sign monitoring, in particular to a physical sign parameter monitoring method accompanying the medication process of a severe mental disease patient. According to the method, the dynamic change of the physical sign parameters is captured by acquiring the physical sign parameter time sequence curves of the to-be-monitored patient in the two stages before and after medication. For each time sequence curve, data value differences and fluctuation duration are analyzed, and abnormal fluctuation data segments are identified. Furthermore, a historical normal time sequence curve is utilized to determine a historical normal fluctuation section corresponding to the abnormal fluctuation, and the overall state of the physical sign parameters is visually reflected through comparative analysis and calculation of a stable index. Meanwhile, the physical sign condition of the patient is monitored by combining the stability index of the same physical sign parameter before and after medication and the time sequence characteristics of the abnormal fluctuation data segment. According to the method, the physical sign state of the patient is reflected more accurately by comparing the change of the physical sign parameters of the patient with the historical normal condition, and data support with higher credibility is provided for doctors to carry out medicine effect judgment or illness state analysis.

The invention relates to the technical field of data driving and landslide surge disaster prevention and control, and discloses a landslide surge height prediction method and system based on a sparrow optimization random forest model.The method comprises the steps that for a target landslide area, a corresponding landslide surge physical model is built, and test groups with different landslide volumes and water depths are set for testing; a corresponding test data set is obtained, and equal-proportion scaling is carried out according to Froude similarity criteria; the method comprises the following steps: preprocessing test data to obtain a corresponding data set, and dividing the preprocessed data set into a training set and a test set; building a sparrow optimized random forest model, setting input values of various parameters, enabling the training set to train the sparrow optimized random forest model, and predicting the maximum height of the landslide surge by using the label data set and the trained sparrow optimized random forest model. The system comprises a data acquisition module, a data division module and a landslide surge prediction module. According to the method, the maximum height of the landslide surge can be accurately predicted.

The invention discloses an equipment health assessment method and device and electronic equipment. The method comprises the following steps: acquiring running state data of the equipment, the running state data being various parameter characteristics acquired at multiple positions of the equipment; adjusting the initial weights corresponding to the various parameter features to obtain target operation state data; a health assessment model is adopted to assess the target operation state data, an equipment health degree score is obtained, and the equipment health degree score is used for quantitatively assessing the equipment health state. The technical problem that the actual health state of the equipment cannot be accurately reflected due to the fact that the static weight factor is adopted to evaluate the health state of the equipment in the prior art is solved.

The invention discloses a mining area unmanned vehicle multi-mode competitive collaborative fusion sensing method, and relates to the technical field of fusion sensing, and the method comprises the following implementation steps: employing infrared thermal imaging and visible light image fusion to forward sense an obstacle, employing an ultrasonic radar and a laser infrared distance measurement sensor to sense a near-area obstacle, and employing a laser infrared distance measurement sensor to sense the near-area obstacle; whether an obstacle approaches or not is judged according to distance measurement information of sensors at four corners and four sides; a laser radar, a depth visible light imaging head and an inertial measurement unit are fused to perceive obstacles, and six modes are fused to construct a complete environment perception domain. According to the multi-mode competitive collaborative fusion perception method for the unmanned carrying vehicle in the mining area, posture changes are detected and tracked in real time, the angle of a vehicle steering engine is adjusted in advance, it is guaranteed that an image can accurately reflect an actual scene, the precision of state prediction of the carrying vehicle is further improved, and the safety and reliability of operation of the unmanned carrying vehicle in the mining area are improved.

The invention discloses a multi-light-spot centroid extraction method and device based on an FPGA, a medium and a product, and relates to the field of digital image processing, and the method comprises the steps: firstly, caching image line data through a first-stage FIFO and a second-stage FIFO, forming three continuous groups of image line data streams, effectively solving the problems of data synchronization and processing rate matching, and improving the data processing efficiency; then, effective pixels of the light spots are accurately determined by means of a pixel threshold value, and different light spot areas are accurately divided by means of line continuity judgment and boundary marking, so that repeated recognition and error recognition of the light spots are avoided, and the accuracy of light spot recognition is greatly improved. And finally, according to the weighted average positions of the gray values of the effective pixels of all the light spots, the target centroid coordinates of the target light spots are calculated, the internal gray difference of the light spots is fully considered, and compared with a conventional method, the light spot energy distribution center is more accurately reflected, so that the calculation precision of the light spot centroid is remarkably improved.

The invention provides a multi-data fusion fire-fighting early warning method and platform, and relates to the technical field of fire fighting, and the method comprises the steps: collecting a multi-dimensional environment data set of a target region; performing noise filtering on the multi-dimensional environment data set to obtain a multi-dimensional environment de-noised data set; comprehensively analyzing the plurality of data sources based on the multi-dimensional environment de-noised data set to generate fire-fighting comprehensive data, and fusing the multi-dimensional environment de-noised data set to obtain a multi-dimensional environment fused data set; building a fire risk model according to the fire-fighting comprehensive data, synchronizing the multi-dimensional environment fusion data set to the fire risk model for evaluation according to a plurality of data sources, and obtaining a fire risk level of the target area; and generating a fire-fighting early warning instruction based on the fire risk level, and sending the instruction to the remote control terminal. According to the method, the technical problem that a traditional fire early warning method is single in monitoring means and is easily interfered by environmental factors to cause false alarm or missing alarm is solved, and the accuracy and response speed of fire early warning are remarkably improved.

The invention relates to the technical field of audio processing, in particular to an intelligent power amplifier calibration method, system and device. The method comprises the following steps: monitoring corresponding voltage and current waveforms of a power amplifier in real time and carrying out load impedance response analysis to obtain power amplifier load impedance; acquiring a power amplifier output audio signal, and performing spectrum analysis and audio distortion detection to obtain a power amplifier output audio distortion degree; performing gain self-adaptive adjustment on the power amplifier based on the power amplifier output audio distortion degree to obtain power amplifier output self-adaptive adjustment gain; performing frequency response optimization corresponding to different frequency bands on the power amplifier output audio frequency spectrum to generate power amplifier response optimization output frequencies corresponding to different frequency bands; and performing intelligent feedback calibration on the power amplifier based on the power amplifier output adaptive adjustment gain and the power amplifier response optimization output frequency corresponding to different frequency bands to generate an intelligent calibration power amplifier. The overall performance and the use experience of the power amplifier can be improved.

The invention provides a channel modeling method and device suitable for satellite-ground communication, and the method comprises the steps: generating a satellite motion track, and setting ground environment parameters and simulation parameters; converting the satellite motion trail from a global coordinate system to a local coordinate system; acquiring a satellite position from the converted satellite motion trail, and performing ray tracing calculation based on plane wave hypothesis to obtain all effective ray paths between the satellite position and the position of a ground receiver; for each effective ray path, calculating large-scale fading caused by a meteorological environment and small-scale fading caused by ground end multipath propagation and multi-end movement; and a channel model is generated by combining large-scale fading and small-scale fading. According to the method, large-scale fading caused by the meteorological environment is modeled, the influence of meteorological attenuation on the multipath receiving power is considered, small-scale fading caused by multipath propagation and multi-end movement at the ground end is modeled, and the modeling accuracy is improved.

The invention belongs to the field of intelligent control systems, and particularly relates to a shield attitude real-time intelligent control method based on machine learning, according to the method, a data acquisition module can more comprehensively and accurately reflect the actual condition of shield construction, and in the control process, key parameters such as the working state of a jack and the pressure of a hydraulic system are monitored in real time, so that the control accuracy is improved. Abnormal conditions can be found in time, an emergency plan can be started, and the safety of shield construction is guaranteed. According to the real-time monitoring and feedback optimization mechanism, the safety and reliability of shield construction are effectively improved, and the construction risk is reduced.

The invention discloses a photovoltaic power prediction method based on seasonal weather classification and an RLHF large model, and the method comprises the following steps: 1, data acquisition: obtaining power generation data and historical meteorological data of a photovoltaic power station, and constructing a data set; 2, constructing a prediction model: constructing a weather classification and photovoltaic output prediction model, dividing a year into a plurality of seasons, dividing weather into a plurality of weather types, and constructing a photovoltaic output prediction model for each weather type of each season; an RLHF large model is constructed, a deep learning and reinforcement learning combined method is used, and the photovoltaic power generation power prediction precision and interpretability are improved through pre-training and human feedback; and step 3, constructing a fusion model: fusing prediction output of the weather typing and photovoltaic output prediction model and the RLHF large model to obtain a final prediction result of the photovoltaic power generation power. The invention also discloses a photovoltaic power prediction system based on seasonal weather typing and the RLHF large model.

The invention discloses a mobile game scene interaction intelligent optimization method based on a generative adversarial network, and aims to improve the game experience of players. Comprising the following steps: S1, collecting behavior data and interaction information of a player in a mobile game, and generating a preliminary behavior feature model; s2, constructing a generative adversarial network model; s3, generating diversified game scene interaction examples according to the trained generator; s4, evaluating and optimizing the generated scene interaction example by using a discriminator; s5, real-time feedback of the player is received, and the behavior feature model of the player is updated; s6, dynamically adjusting a generation strategy based on the updated behavior feature model, and optimizing a scene interaction effect; s7, environment data are integrated, scene interaction is optimized in real time, and the immersion of players is enhanced; and S8, providing user-defined scene interaction options for the players, and allowing the players to adjust according to personal preferences. The interactive experience and game fun of players can be effectively improved.

The invention relates to the technical field of numerical control machine tools, in particular to a numerical control machine tool digital twin model updating method and system. The method comprises the following steps: acquiring actual processing technology data; performing cutting process simulation according to the actual machining process data and the digital twin model of the numerical control machine tool to obtain cutting process data; performing tool wear state feature analysis on the cutting process data to obtain tool wear state data; updating a tool model according to the tool wear state data to obtain an updated tool model; according to the updated cutter model, dynamic contact state solving is carried out, and a dynamic contact solving result is obtained; and performing contact key data analysis on a dynamic contact solving result to obtain cutter-workpiece dynamic contact information. According to the method, the modeling precision and the simulation reliability are greatly improved, so that the digital twin model can reflect complex processing conditions more truly, and a reliable decision basis is provided for process optimization.

The invention discloses an intelligent precise irrigation system based on low-altitude remote sensing data driving. The intelligent precise irrigation system comprises a low-altitude unmanned aerial vehicle, a ground sensor network module, a meteorological data integration module, a space-time self-evolution feature map network module, a space-time adaptive attention network module, a context multi-arm tiger machine algorithm module and an automatic irrigation control module. The low-altitude unmanned aerial vehicle is used for acquiring remote sensing data of crops; the ground sensor network module is used for collecting real-time environment information; the meteorological data integration module is used for multi-modal data integration; the space-time self-evolution characteristic graph network module is used for generating a moisture demand distribution graph; the space-time adaptive attention network is used for optimizing an irrigation strategy; the context dobby machine algorithm module is used for dynamically selecting an optimal irrigation strategy; and the automatic irrigation control module is used for executing the optimal irrigation strategy and implementing differential intelligent irrigation management. According to the invention, precise and intelligent irrigation is realized by using the space-time self-evolution feature map network and the context multi-arm tiger machine algorithm.

The invention relates to the crossing field of natural language processing (NLP) and information retrieval (IR), in particular to an intelligent retrieval and question answering method and device, which is based on time window optimization and comprises the following steps: S1, knowledge preprocessing and time entity identification; s2, text vector quantization; s3, storing vectors, and retaining time information; s4, proposing a problem, and identifying a time entity; s5, user question vector quantization; s6, performing vector retrieval, and filtering time information; s7, combining time attenuation and similarity score; s8, merging the prompt statements; s9, returning an answer; and S10, answering the question. Compared with the prior art, the method has the advantages that each knowledge item in the knowledge base can be endowed with a clear time label, the question containing the time information can be answered more accurately and efficiently, and more timely and accurate answers can be provided.

The invention discloses a forest carbon reserve estimation method based on artificial intelligence and multi-modal remote sensing data, and relates to the technical field of forest carbon reserve estimation, and the method integrates optical satellite images, laser radar data and hyperspectral image remote sensing data, improves the comprehensiveness and estimation precision of the data through multi-source data fusion, and improves the estimation precision of the forest carbon reserve. The method can more accurately reflect the coverage condition, the vertical structure and the biological diversity of the forest, overcomes the limitation of a single data source, divides the whole forest region into a plurality of sub-regions, independently constructs a carbon reserve estimation model based on the forest type of each sub-region, refines the carbon reserve estimation of different regions in a region-by-region estimation mode, and improves the estimation accuracy. According to the method, characteristics of different forest types are accurately reflected, a dynamic carbon reserve prediction model based on a differential equation is introduced, historical and real-time remote sensing data are combined, changes of carbon reserves in the time dimension are simulated, and influences of factors such as forest growth, climate changes, natural disasters and felling on the carbon reserves are captured.

The invention discloses a chemical adding control system for sewage treatment, which comprises a data acquisition module used for acquiring sewage inlet water quality data and inlet water flow, water quality data of each sewage treatment process and discharge water quality data in real time; the data acquisition module is used for acquiring dosing data and detection data; the data preprocessing module is used for assigning the partial water quality data Em based on a preset judgment threshold value; the dosing prediction module is used for inputting the real-time data into the self-attention dosing prediction model and outputting to obtain a next dosing strategy; the self-attention dosing prediction model corrects the attention score matrix based on the chemical reaction relation matrix E; and the dosing control module is used for dosing according to the output dosing strategy. The method can adapt to sewage component and concentration changes, and the dosing strategy of each process of sewage treatment is accurately predicted and obtained.

The invention relates to the technical field of water and soil monitoring, in particular to a water and soil loss intelligent monitoring system based on the Internet of Things, which comprises an earth surface remote sensing image data acquisition module for acquiring remote sensing image data covering a monitoring area through a remote sensing satellite; the perturbation perception compensation module is used for repairing data by adopting a compensation mechanism aiming at abnormal fluctuation of the remote sensing image caused by landslide geological disturbance, and specifically comprises a disturbance detection unit and a disturbance area compensation unit; and the disaster tracing module is used for reconstructing a loss path based on the remote sensing image data restored by the perturbation perception compensation module, simulating a diffusion path of water and soil loss and tracing the loss based on the diffusion path. According to the method, water and soil loss prevention and control guidance of the key area is provided, the accuracy and efficiency of water and soil loss tracing are improved, and powerful technical support is provided for source identification and prevention and control decision of water and soil loss.

The invention belongs to the technical field of magnetic suspension centrifugal compressor control, and particularly relates to a magnetic suspension centrifugal compressor constant flow and constant pressure control method and system.The method comprises the steps that multi-parameter data of a compressor are obtained, and a working condition analysis model is constructed through the multi-parameter data and historical operation data; training and updating the constructed working condition analysis model; adjusting a working condition demand weight according to a judgment result of the working condition analysis model, introducing a prediction control algorithm according to historical data and working condition characteristics, and performing loss compensation through a dynamic response model established by predicting a current change trend; dynamic adjustment and optimization are carried out based on the real-time working condition and the change rate of pressure and flow; and accurately controlling the output power of the compressor through a dynamic optimization result. Dynamic adjustment and optimization are carried out based on the real-time working condition and the change rate of the pressure and the flow, the precision of pressure and flow control is further improved, and adaptability is improved.