57 results about "Linear regression function" patented technology

The invention relates to a flight delay prediction method based on an ARIMA model. The method comprises the following steps of stepS1, establishing a data set: flight data is collected and the flight data includes flight information, time information, airport information and delay time information; stepS2, analyzing a data set characteristic: differences of airports and airlines and an influence of weather on the flight are analyzed; stepS3, establishing the ARIMA model: establishing of the ARIMA model includes a mobile average process, an autoregressive process, an autoregressive mobile average process and an ARIMA process; stepS4, selecting an optimum ARIMA model: the established ARIMA model is verified and an optimum parameter is selected; stepS5, predicting flight delay: any flight data is acquired and then an optimum ARIMA function is selected to be a model delay function, a multiple-linear regression function is selected to be a weather delay function and a final prediction result is acquired after addition of the functions. By using the method, high prediction precision can be realized, usage time is shortened and flight delay can be effectively predicted.

The invention relates to a lithium ion battery, and further relates to a lithium ion battery service life prediction method. The method comprises the following steps of collecting the running data ofthe in-service or retired battery of the same model, and establishing a database which comprises the battery operating temperature, the battery discharge rate, the battery internal resistance and thetotal service life parameter; establishing a battery service life prediction linear regression function model: h (x) = h theta (x) = theta 0+theta 1*1+theta 2*2 + theta 3*3, and the operating temperature, the discharge rate, the internal resistance of the battery in a specified model are substituted into the regression model, and the total service life of the battery is obtained. The operating temperature, the discharge rate and the internal resistance of the battery are key factors affecting the service life of the battery, and the operating temperature, and by introducing the discharge rateand the internal resistance of the battery to be used as the influence parameters of the battery service life, the modeling prediction is effective.

The invention provides a method for evaluating the performance reliability of numerical control equipment. The method comprises the following steps of: according to collected multiple performance parameter degradation data, fitting a performance parameter distribution type by using a least square method, and judging the fitting degree; and then, training by using a multi-element support vector regression machine to obtain an optimal non-linear regression function taking time and statistical characteristics of performance parameter distribution as input and output, namely a function of the statistical characteristics of performance parameter distribution changing with time, thereby obtaining a probability density function of performance parameters so as to obtain the reliability of the numerical control equipment. Under small sample conditions, the method provided by the invention can accurately evaluate and forecast the performance reliability of the numerical control equipment, learn the probability of the numerical control equipment of completing a required function at a certain period of time, and evaluate the influence of each performance parameter on the reliability of the numerical control equipment, thereby improving the accuracy and efficiency of reliability evaluation.

Method for the estimation of at least a variable (β; νx, νy; ψ, μ) affecting a vehicle dynamics (10), including measuring dynamic variables (MQ) of the vehicle (10) during its motion, calculating in real time an estimate (Formula (I)) of said variable (β; νx, νy; ψ, μ), on the basis of said measured dynamic variables (MQ), The method includes: calculating (230) said estimate of said at least a variable (β; νx, νy; ψ, μ) by an estimation procedure (DVSβ; DVSβν; DVSβνμ) comprising taking in account a set of dynamic variables (MQ) measured during the motion of the vehicle (10) over respective time intervals (ny, nw, nψ, nx, nα) and applying on said set of measured dynamic variables (MQ) at least an optimal nonlinear regression function (ƒ*β; ƒ*x, ƒ*y; ƒ*β1, ƒ*β2, ƒ*ψ1, ƒψ2) calculated with respect to said variable (β; νx, νy; ψ, μ) to estimate to obtain said estimate of said variable (β; νx, νy; ψ, μ), said optimal non linear regression function (ƒ*β; ƒ*x, ƒ*y; ƒ*β1, ƒ*β2, ƒ*ψ1, ƒ*ψ2) being obtained by an optimal calculation procedure (220) including: on the basis of an acquired set of reference data (Dd) and of said set of dynamic variables (MQ) measured during the motion of the vehicle (10), finding, for a desired accuracy level (ε), a regression function (ƒ*β; ƒ*x, ƒ*y; ƒ*β1, ƒ*β2, ƒ*ψ1, ƒ*ψ2) giving an estimation error lower or equal than said desired accuracy level (ε) in a given set of operative conditions (OC), said acquired set of reference data (Dd) being obtained by acquiring (210) in said given set of operative conditions (OC) a set of reference data (Dd) of variables including variables corresponding to said measured dynamic variables (MQ) of the vehicle (10) and a lateral (vy) and a longitudinal velocity (vx) of the vehicle (10).

The invention discloses a method and system for detecting a microseism event. The method includes the steps that the optimal time-window length L obtained after a microseism detection sequence of a marked microseism wave is trained with the first-order liner regression function and the kriging interpolation method is obtained; a to-be-detected microseism data sequence in time order is obtained; afirst time window and a second time window following the first time window are established, wherein the lengths of the two time windows are both L; data filling is conducted on the left side and the right side of the microseism data sequence; a first piece of data, aligned with the center of the first time window, of the microseism data sequence is slid by two time windows till the center of the first time window is aligned with a final piece of data of the microseism data sequence; every time sliding of the step size of one piece of data is conducted, the data in the two time windows is subjected to chi-square testing to determine whether a microseism wave is generated at each piece of data in the microseism data sequence. By means of the method and system, the influences of noise on detection precision can be effectively avoided, and detection accuracy can be improved.

The invention discloses an image reversible information hiding method and apparatus based on multiple linear regression. The method comprises the following steps: selecting a plurality of pixel pointswithin a neighborhood of a target pixel point as prediction samples according to a certain rule, and selecting a plurality of pixel points as training samples in the neighborhood of each prediction sample to serve as training samples; establishing a multiple linear regression function relationship between the training sample pixels in a local area and the prediction sample pixels; and predictinga target pixel value by using the function relationship between the training sample pixels and the prediction sample pixels. According to the image reversible information hiding method and apparatus,the accurate prediction of the target pixel value is achieved by using the internal connection between the pixels around the target pixel, and the performance of the image reversible information hiding and embedding is further enhanced.

A soil body volumetric water content testing method based on electromagnetic wave time domain reflectometry is disclosed. The method includes preparing 3-5 soil body samples with different dry densities, measuring a series of gravimetric water content standard values, gravimetric water content equivalent values w by the electromagnetic wave time domain reflectometry and the dry densities, fitting to obtain a linear regression function about gravimetric water content relative errors [Eta] and the corresponding soil body dry densities [Rho]<di>, with the function being [Eta] = a[Rho]<di> + b, acquiring a water content standard value of a soil body and a calibration coefficient [Zeta] of an electromagnetic wave time domain reflectometry measuring value, with the [Zeta] being equal to 1-a[Rho]<d>-b, measuring a volumetric water content [Theta] of the soil body to be tested by using the electromagnetic wave time domain reflectometry, and further acquiring a calibrated volumetric water content [Theta]' of the soil body by the electromagnetic wave time domain reflectometry, wherein the [Theta]' is equal to [Zeta]*[Theta]. The method eliminates influences of different soil body types and dry density changes on volumetric water content testing by electromagnetic wave time domain reflectometry, obviously reduces test result errors and is accurate and reliable in test results, thus providing more accurate and more reliable test parameters for geotechnical engineering designing and construction.

The invention relates to a spectrum entropy prediction method, which comprises the steps of analyzing by using an autocorrelation function to obtain spectrum entropy data features; selecting a main training feature set X' from spectrum entropy data according to the obtained spectrum entropy data features; performing feature importance ranking on the main training feature set X' to obtain a featureset S; performing feature dimension reduction on the feature set S to obtain a best feature set Sbest; constructing a linear regression function according to the best feature set Sbest and a prediction value Y, and obtaining a predictor; screening feature vectors to be predicted, and inputting the screened feature vector to be predicted into the predictor to obtain a prediction result. The invention further relates to a spectrum entropy prediction system. The spectrum entropy prediction method and system can predict a frequency band with stable spectrum utilization and are high in predictionaccuracy.

The invention relates to a lithium ion battery, and further relates to a lithium ion battery service life prediction method. The method comprises the following steps of collecting the running data ofthe in-service or retired battery of the same model, and establishing a database which comprises the battery operating temperature, the battery discharge rate, the battery internal resistance and thetotal service life parameter; establishing a battery service life prediction linear regression function model: h (x) = h theta (x) = theta 0+theta 1*1+theta 2*2 + theta 3*3, and the operating temperature, the discharge rate, the internal resistance of the battery in a specified model are substituted into the regression model, and the total service life of the battery is obtained. The operating temperature, the discharge rate and the internal resistance of the battery are key factors affecting the service life of the battery, and the operating temperature, and by introducing the discharge rateand the internal resistance of the battery to be used as the influence parameters of the battery service life, the modeling prediction is effective.

Aspects model a set of different employee compensation adjustment factors from a locally weighted linear regression function of employee data. Retention probabilities are generated for retaining each of the employees, and employee retention costs modeled as a function of historic employee wage data, the modeled set of employee compensation adjustment factors and the retention probabilities. Costs are modeled for replacing employees as a function of the employee wage data and the historic market data, and revenues are modeled for employee productivity as a function of the retention probabilities and the historic business performance and strategy data. The modeled employee compensation adjustment factors are iteratively optimized to maximize a profit objective value determined as a function of the modeled costs for replacing employees and employee productivity revenues.

The invention relates to a dryer control method and device, computer equipment and a storage medium. State attribute data and the target moisture value of a to-be-dried material and the operation attribute parameters of a dryer are obtained, and based on a preset decision tree regression model, and the state attribute parameters of the to-be-dried material and the operation attribute parameters of the dryer, a linear regression function of the predicted moisture value of the to-be-dried material is determined, and optimization solving is conducted according to the target moisture value and the linear regression function of the predicted moisture value to obtain target control parameters of the dryer. And the target control parameters are adopted to control the operation of the dryer, so that the operation complexity of the dryer is lowered, the degree of dependence on experienced operation workers is reduced, and meanwhile the control precision is improved.

The invention discloses an electric appliance switch event detection method and a system. The method comprises the following steps: obtaining the optimal length of time window obtained by training thepower sequence of the marked switching events by using a first-order linear regression function and a Kriging interpolation method; acquiring a total power sequence to be detected collected in chronological order; establishing a first time window and a second time window immediately following the first time window, both lengths of the time windows being optimal lengths of the time windows; the left and right sides of the total power sequence to be detected being filled with data; sliding two time windows from the center of the first time window aligned with the first data of the total power sequence, until the center of the first time window is aligned with the last data of the total power sequence, the data within the two time windows are subjected to a chi-square test for each step of sliding one data to determine whether a switching event occurs at each data in the total power sequence. The method and the system of the invention can effectively avoid the influence of noise on the detection accuracy and improve the detection accuracy.

The invention provides a carrying collision detection method and device for a robot. The method comprises the steps that a to-be-detected image containing an object carrying table and a to-be-transported object on the object carrying table is acquired; according to the to-be-detected image, distance information between the to-be-transported object and the inner wall of the object carrying table isdetermined; the deviation of the to-be-transported object is determined according to the distance information between the to-be-transported object and each side wall in the inner wall of the object carrying table and a preset linear regression function; and when the deviation of the to-be-transported object is larger than a preset deviation threshold value, it is determined that the to-be-transported object collides. According to the detection method provided by the above scheme, the to-be-detected image containing the object carrying table and the to-be-transported object on the object carrying table is acquired in real time, and the distance information between the to-be-transported object and the inner wall of the object carrying table is determined according to the to-be-detected image so as to detect whether the to-be-transported object collides or not in real time, and improve the timeliness of a detection result.

The invention relates to a multi-source information fusion algorithm for marine biology detection and early warning in a nuclear power plant. For the data collected by many sensors in the field, the algorithm adopts decision-making fusion form, analyzes and calculates the data respectively based on linear regression model and fuzzy neural network prediction method, and obtains the early warning model of multi-source information fusion for marine biology exploration and early warning. The intrusion intensity formula, fitting error and Euclidean distance are obtained by using the least square multiparameter estimation method of a linear multiple regression model estimation method and the linear regression function of MATLAB. The fuzzy theory and neural network technology are used to fit thesensor sampling data, and a non-parametric multi-sensor data fusion algorithm is implemented to determine the invasion degree of marine organisms and the possibility of water intake blockage, which provides support for improving the reliability of nuclear power plant cold source.

The invention discloses a detection method and system for first arrival of seismic waves. The method comprises the steps that a determined first-arrival seismographic record sequence is trained by using a first-order linear regression function and a Kriging interpolation method to obtain the optimal length of a time window; a to-be-detected seismographic record sequence collected by a radiodetector is obtained; a first time window and a second time window following the first time window are established, wherein the lengths of the first and second time windows are the optimal length of the timewindow; data filling is conducted on the left and right sides of the seismographic record sequence; the two time windows are slid from a first piece of data aligned with the seismographic record sequence until the center of the first time window is aligned with the last piece of data of the seismographic record sequence, chi-square test is conducted on data in the first and second time windows every time when the sliding covering a step length of a piece of data, and therefore whether or not there is seismic wave arrived is determined at the position of each piece of data in the seismographicrecord sequence. By means of the system and the method, the influence on detection accuracy caused by noise can be effectively avoided to improve detection accuracy.

The invention provides a method and apparatus for predicting the range of motion of a target. The method comprises the steps of obtaining environment attribute information and first path length information of respective paths set in an actual area for a target to select; iteratively solving a multiple linear regression function constructed based on the environment attribute information and the first path length information by using a particle swarm method to obtain second path length information corresponding to each path, independent and dependent variables of the multiple linear regression function being the environment attribute information and the first path length information respectively; according to the respective second path length information and the starting positions and total time of motion of the target, determining the respective end positions of motion corresponding to the target, the total time of motion being the time taken by the target to move from the starting positions of motion to the end positions of motion; and obtaining a predicted range of motion of the target according to the end positions of motion. The machine learning technology is used to predict the range of motion, the prediction accuracy is high, the time and effort are saved, and the practicality is high.

The invention relates to a sea ice information extraction method, which comprises the following steps of: preprocessing acquired photon data, further classifying sea ice information in combination with a calculation result, calculating a sea ice freeboard height value according to a classification condition, optimizing an existing accumulated snow depth measurement model result, and according to other sea ice information parameters, calculating the sea ice thickness value by combining the Archimedes law, a linear regression function and other calculation formulas. Compared with the prior art, after the satellite initial data are corrected, subsequent processing and screening are carried out by using relatively accurate data, the data are further limited, and the utilization rate of the data is improved.

The invention discloses an online monitoring method and device for sulfur dioxide in wine making process and relates to the technical field of food monitoring. Based on the gas-liquid phase equilibrium and dissociation equilibrium principle in the wine making process, multiple factors influencing equilibrium are fully considered, after data in the wine making process is acquired, a gas-liquid equilibrium coefficient and an ionic equilibrium coefficient of a current wine equilibrium system are calculated through a linear regression function, the concentration of molecular-state sulfur dioxide in the wine is calculated according to a gas-liquid phase molecular-state sulfur dioxide equilibrium relationship, and then the concentration of free-state sulfur dioxide is calculated according to a dissociation equilibrium relationship between the molecular-state sulfur dioxide and free-state sulfur dioxide in liquid. By means of online monitoring method and device, the concentration of the free-state sulfur dioxide in the wine can be quickly, nondestructively and intelligently detected, convenience and real-time performance are achieved, and direct, efficient and accurate wine quality monitoring and quality control and raw material saving in the making process can be achieved easily.

The invention discloses a method and device for intelligently detecting the operation state of a transformer for electronic and electrical appliances, and the method comprises the steps: obtaining an equipment power information data set, an operation environment information data set and a vibration information data set of the transformer for electronic and electrical appliances; performing data fitting on the first electric power information data set, the first operation environment information data set and the first vibration information data set through a multivariate linear regression function to obtain a multivariate index linear regression function; obtaining a transformer operation state evaluation model according to a first cost function and the multivariate index linear regression function; and inputting the first electric power information, the first operation environment information and the first vibration information into the transformer operation state evaluation model to obtain a first transformer operation state result. The technical problems that in the prior art, transformer operation state detection is low in intelligent degree, monitoring and detection cannot be conducted in real time, the detection result is inaccurate, and the detection efficiency is low are solved.

The invention discloses a visual sense mapping method based on support vector regression. The visual sense mapping method is such a method as to estimate the corresponding continuous target values from the inputted image characteristics, for instance, the altitude estimation, age estimation and visual line tracking, etc. In the practical application, one widely existing problem faced in the training of a mapping model is that the number of training samples is limited. The visual sense mapping method based on support vector regression proposed by the invention can overcome the above problem effectively. When given a new sample, the method directly utilizes the learned linear regression function with kernel function to predict the target values. The training time of the method is short and the operation is convenient and simple.

The application provides an SF6 gas detection and quantitative analysis method based on a support vector machine. The method comprises the following steps: selecting graph peak, detection pressure intensity, detection temperature and laser intensity of Raman gas detection as input variables; selecting SF6 gas concentration as an output quantity, and acquiring historical operation data and SF6 gasconcentration test data corresponding to N groups of the input variables as training samples; selecting nonlinear transformation phi (.); mapping n-dimensional input and a one-dimensional output sample vector from an original space to a high-dimensional feature space, and constructing an optimal linear regression function f(x) being equal to omega phi (x) plus b in the high-dimensional feature space, wherein omega is a weight vector, b is a deviation and x is an input variable; predicting an SF6 gas detection and quantitative analysis result. According to the SF6 gas detection and quantitativeanalysis method based on the support vector machine provided by the application, the influence of external disturbing factors such as pressure intensity is considered, and the accuracy of a method for detecting the gas concentration through Raman spectrum is improved.

The invention relates to a single change point detection method, system and device and a storage medium. The method comprises the steps of setting a time series data set, and generating a corresponding linear regression function expression; utilizing a least square method to obtain a linear regression parameter value of the linear regression function expression; obtaining a variable point T statistic expression and a variable point D statistic expression according to the linear regression parameter value; according to the change point T statistical magnitude expression and the change point D statistical magnitude expression, obtaining a change point maximum statistical magnitude; and obtaining single change point detection information according to the distribution area of the change pointmaximum statistics in hypothesis testing. According to the invention, the change point of the time sequence with a long-term trend can be detected, and the change point detection accuracy is higher than that of the existing detection method.

The invention discloses a weighing-free AI intelligent identification truck overload estimation method, device and system. The method comprises the steps: firstly, obtaining the average deformation coefficients of rear wheel truck tires of the same type of vehicles under different load capacities, and carrying out fitting to obtain a statistical data linear regression function model that the corresponding average deformation coefficients of the tires change along with the change of the load capacities; determining a truck load upper limit value and a multiple load value according to the trucktype, and substituting the truck load upper limit value and the multiple load value into the function model to obtain a corresponding truck overload tire deformation coefficient value and a multiple overload deformation coefficient value; and when the vehicle to be detected passes through the video detection point, calculating the average deformation coefficient of the rear wheel truck tire according to the deformation horizon length of the rear wheel tire and the radius of the truck tire, comparing the average deformation coefficient with a correlation numerical value, and judging whether thevehicle is an overload or multiple overload suspected vehicle. The invention aims to solve the problems of low cost, no lane guidance, no-dead-corner deploying and controlling intelligent monitoringoverload of various levels of highway networks, especially severe overload vehicles, in the aspects of technicality and economy.

The invention provides a constrained multivariate linear regression adaptive image reversible information hiding error prediction optimization method. The method comprises: selecting a prediction sample and a training sample, replacing a maximum value and a minimum value in the training sample with average values of training sample vector elements, establishing a multiple linear regression mappingrelation function matrix between training sample pixels and prediction sample pixels in a local area, and solving a linear regression function to obtain a training coefficient; and performing replacement optimization on the prediction sample by using the multivariate linear regression coefficient obtained by using the least square method and the prediction sample prediction target pixel value, and performing prediction of the target pixel value by taking the optimized prediction sample pixel as an independent variable sample.

Method for the estimation of at least a variable (β; νx, νy; ψ, μ) affecting a vehicle dynamics (10), including measuring dynamic variables (MQ) of the vehicle (10) during its motion, calculating in real time an estimate (Formula (I)) of said variable (β; νx, νy; ψ, μ), on the basis of said measured dynamic variables (MQ), The method includes: calculating (230) said estimate of said at least a variable (β; νx, νy; ψ, μ) by an estimation procedure (DVSβ; DVSβν,-DVSβνμ) comprising taking in account a set of dynamic variables (MQ) measured during the motion of the vehicle (10) over respective time intervals (ny, nw, nψ, nx, nα) and applying on said set of measured dynamic variables (MQ) at least an optimal nonlinear regression function (ƒ*β; ƒ*x, ƒ*y; ƒ*β1, ƒ*β2, ƒ*ψ1, ƒψ2) calculated with respect to said variable (β; νx, νy; ψ, μ) to estimate to obtain said estimate of said variable (β; νx, νy; ψ, μ), said optimal non linear regression function (ƒ*β; ƒ*x, ƒ*y; ƒ*β1, ƒ*β2, ƒ*ψ1, ƒ*ψ2) being obtained by an optimal calculation procedure (220) including: on the basis of an acquired set of reference data (Dd) and of said set of dynamic variables (MQ) measured during the motion of the vehicle (10), finding, for a desired accuracy level (ε), a regression function (ƒ*β; ƒ*x, ƒ*y; ƒ*β1, ƒ*β2, ƒ*ψ1, ƒ*ψ2) giving an estimation error lower or equal than said desired accuracy level (ε) in a given set of operative conditions (OC), said acquired set of reference data (Dd) being obtained by acquiring (210) in said given set of operative conditions (OC) a set of reference data (Dd) of variables including variables corresponding to said measured dynamic variables (MQ) of the vehicle (10) and a lateral (vy) and a longitudinal velocity (vx) of the vehicle (10).

The invention discloses an optimization design method for layout information of airport landside traffic signs, includes such steps as designing a logo optimization design test questionnaire based onSP survey method, wherein, the logo optimization design test questionnaire comprises a plurality of layout design schemes, each layout design scheme is provided with a plurality of options, and each layout design scheme is a combination of text spacing, text size, logo color and arrow position; Information collection based on logo optimization design test questionnaire; Scoring the collected databased on the Likert Scale; Based on regression analysis model, the scoring results were regressed, and the linear regression functions of text spacing, text size, mark color and arrow position were obtained. Analytic Hierarchy Process (AHP) is used to analyze the regression results, and the text spacing, text size, flag color and arrow position are sorted by weights. Based on weight ranking, the layout of airport landside traffic signs is optimized.

The invention belongs to the technical field of processing of mineral resources and smelting of nonferrous metals, and particularly relates to a method for measuring content of indium in zinc concentrate. The method comprises the following steps: 1) weighing N samples to be measured with the same mass, labeling 1 to N, and adding indium into the samples (2-N) to be measured, wherein the mass of the indium added to the samples increases progressively according to an equal mass, and N is an integer more than 2; 2)adding hydrochloric acid into the N samples to be measured, and then adding nitric acid and perchloric acid, steaming till smoking into a wet salt status, and then adding warm saltpeter solution to obtain N sample solutions to be measured; 3) obtaining the light absorption value of the N sample solutions to be measured; and 4) based on the intercept of a linear regression function of indium concentration C and light absorption value A of the N sample solutions to be measured, obtaining the content of indium in the samples to be measured. The method for measuring content of indium in zinc concentrate has the advantages of high simplicity in operation, low cost, accurate result, high accuracy, environment-friendly property and the like.

A method for calculating the lifting height of a scissor-type aerial work platform includes such steps as using linear regression method to fit the linear relationship between single lifting time andlifting height of scissor-type aerial work platform, using linear regression method to calculate the lifting height of scissor-type aerial work platform, calculating the lifting height of scissor-typeaerial work platform, calculating the lifting height of scissor-type aerial work platform and lifting height of scissor-type aerial work platform. The linear function relationship between the outputvoltage signal of the angle sensor and the lifting height is established. A linear regression function is used to predict the height of a single lift of a scissor-type aerial platform, and the predicted height is obtained. The observation height is obtained according to the voltage signal of the angle sensor and the linear function relationship between the voltage signal and the lifting height. According to the prediction error and observation error of the lifting height, the lifting height gain of the scissor-type high-altitude platform is obtained. According to the predicted altitude, the observed altitude and the altitude gain, the optimal solution of the lifting altitude is calculated as the current actual lifting altitude. This method can reduce the influence of error and noise, and get more accurate calculation method of lifting height of scissor-type high-altitude platform.

The invention discloses an intelligent management method and system based on the safety risk of a blood purification center, and the method comprises the steps: carrying out the feature extraction of a first performance assessment result, and obtaining a first nursing index set; obtaining a first patient index set according to the various types of illness state information; performing quality evaluation on the management result information of the first blood purification center to obtain a first management index set; obtaining a security risk assessment model according to a first cost function and the multivariate index linear regression function; inputting the first nursing information, the first patient information and the first management information into the safety risk assessment model to obtain a first management safety risk; and performing safety management on the first blood purification center according to the first management safety risk. The technical problems that in the prior art, a blood purification center has many safety risks, safety risk analysis and management are not accurate, and the nursing quality of a patient is reduced are solved.