CN-121983264-A - Time sequence data processing method and system for predicting blade life of nursing equipment
Abstract
The invention discloses a time sequence data processing method and a time sequence data processing system for predicting the service life of a blade of nursing equipment, which relate to the technical field of data processing and analysis and comprise the steps of carrying out data screening processing on all completed tasks of the nursing equipment and determining the critical temperature of the blade of the nursing equipment. The invention firstly needs to determine the critical temperature arrival time of each task-completed nursing equipment blade, because the blade becomes dull with long-time use, the friction is increased, the temperature rise is accelerated, and the operation of electronic components of the nursing equipment is influenced by the too high temperature of the blade, so that the service life of all the completed tasks of the nursing equipment is predicted by the critical temperature arrival time of each task-completed nursing equipment blade, and whether the blade can be used continuously is determined.
Inventors
- GE TING
- CHEN ZHENGHUA
Assignees
- 宁波霍德智能科技有限公司
Dates
- Publication Date
- 20260505
- Application Date
- 20260113
Claims (9)
- 1. A method of time series data processing for blade life prediction of a care facility, comprising: Acquiring all completed tasks of the nursing equipment, performing data screening processing on all completed tasks of the nursing equipment, and determining the critical temperature of a blade of the nursing equipment; Based on the critical temperature of the nursing equipment blade, performing data comparison processing on all completed tasks of the nursing equipment, and acquiring the critical temperature reaching time of each completed task of the nursing equipment blade; based on the critical temperature reaching time of each care equipment blade with completed tasks, carrying out data calculation processing on all the completed tasks of the care equipment to obtain the heating rate increase rate of the care equipment blade; And carrying out service life prediction processing on all completed tasks of the nursing equipment based on the temperature rise rate of the blade of the nursing equipment, and determining the residual service life of the blade of the nursing equipment.
- 2. The method for processing time series data of life prediction of a blade of a nursing device according to claim 1, wherein the step of acquiring all completed tasks of the nursing device, performing data screening processing on all completed tasks of the nursing device, and determining the critical temperature of the blade of the nursing device specifically comprises the following steps: Data reading processing is carried out on a data storage module of the nursing equipment, and all completed tasks of the nursing equipment are obtained; data reading processing is carried out on all completed tasks of the nursing equipment, and task duration time of all completed tasks is obtained; based on the maximum function, sequencing all task duration of the completed tasks to obtain the maximum value of the task duration; And carrying out data screening processing on the completed task of the nursing equipment corresponding to the maximum value of the task duration, and determining the critical temperature of the blade of the nursing equipment.
- 3. The time series data processing method for predicting the service life of a blade of a nursing device according to claim 2, wherein the data screening process is performed on the completed task of the nursing device corresponding to the maximum value of the duration time of the task, and the determining the critical temperature of the blade of the nursing device specifically includes the following steps: Performing data reading processing on the completed task of the nursing equipment corresponding to the maximum value of the task duration, and acquiring temperature change data corresponding to the maximum value of the task duration; Based on the temperature acquisition time intervals, calculating and processing temperature change data corresponding to the maximum value of the task duration, and acquiring the temperature change rate of each temperature acquisition time interval; Counting the temperature change rate of each temperature acquisition time interval, and determining the occurrence number of each temperature change rate; based on the maximum function, sequencing the occurrence times of each temperature change rate, and determining the maximum value of the occurrence times of the temperature change rate; And setting temperature data corresponding to the maximum value of the occurrence times of the temperature change rate as the critical temperature of the blade of the nursing equipment.
- 4. The method for processing time series data of life prediction of a blade of a nursing device according to claim 3, wherein the step of comparing data of all completed tasks of the nursing device based on the critical temperature of the blade of the nursing device to obtain the critical temperature arrival time of the blade of the nursing device for each completed task specifically comprises the following steps: constructing a rectangular coordinate system, setting an X axis by taking time as a parameter, and setting a Y axis by taking temperature as a parameter; reading data of all completed tasks of the nursing equipment, and acquiring temperature change data of each completed task; Drawing a curve of the temperature change data of each completed task in a rectangular coordinate system to obtain a temperature change curve of each completed task; Drawing a curve of the critical temperature of the blade of the nursing equipment in a rectangular coordinate system to obtain a critical temperature curve; screening the intersection points of the critical temperature curve and the temperature change curve of each completed task, and determining the position of the first intersection point; and setting the X-axis parameter corresponding to the position of the first intersection point as the critical temperature arrival time of each care equipment blade which has completed the task.
- 5. The time series data processing method for predicting service life of a blade of a nursing device according to claim 4, wherein the step of calculating data of all completed tasks of the nursing device based on the critical temperature arrival time of the blade of the nursing device for each completed task to obtain the rate of increase of the temperature rise rate of the blade of the nursing device specifically comprises the following steps: Calculating the critical temperature reaching time of each care equipment blade with the completed tasks and the critical temperature of the care equipment blade, and obtaining the heating rate of each care equipment blade with the completed tasks; performing data reading processing on all completed tasks of the nursing equipment to obtain time stamps of all completed tasks; And calculating the heating rate of each task-completed nursing device blade based on the time stamp of each task-completed, and determining the heating rate increase rate of the nursing device blade.
- 6. The method for processing time series data of life prediction of a nursing equipment blade according to claim 5, wherein the calculating the heating rate of each completed nursing equipment blade based on the time stamp of each completed task, and determining the heating rate increase rate of the nursing equipment blade specifically comprises the following steps: Based on the time stamp of each completed task, sequencing all the completed tasks of the nursing equipment, and acquiring the sequence of the completed tasks of the nursing equipment; Based on the arrangement sequence of the completed tasks of the nursing equipment, sequencing the heating speeds of the nursing equipment blades of each completed task to obtain an ordered heating speed set of the nursing equipment blades; Carrying out difference calculation processing on adjacent data in the ordered heating speed set of the nursing equipment blade to obtain a heating speed difference value set; And carrying out average value calculation processing on all data in the temperature rise speed difference value set to obtain the temperature rise speed increase rate of the nursing equipment blade.
- 7. The method for processing time series data of life prediction of a blade of a nursing device according to claim 6, wherein the step of predicting the life of all completed tasks of the nursing device based on the rate of increase of the temperature rise rate of the blade of the nursing device, and determining the remaining life of the blade of the nursing device specifically comprises the steps of: Performing average value calculation processing on the task duration time of all completed tasks to obtain the average working time of the nursing equipment blade; based on the time stamp of each completed task, carrying out data screening processing on the temperature rising speed of each nursing equipment blade with completed task, and obtaining the temperature rising speed of the latest nursing equipment blade with completed task; and carrying out service life prediction processing on the temperature rising speed of the nursing equipment blade which is used for finishing the task recently based on the temperature rising speed increasing rate of the nursing equipment blade, and determining the residual service life of the nursing equipment blade.
- 8. The method for processing time series data of life prediction of a nursing equipment blade according to claim 7, wherein the step of performing life prediction processing on the temperature rising speed of the latest nursing equipment blade for completing tasks based on the temperature rising speed increasing rate of the nursing equipment blade, and determining the remaining life time of the nursing equipment blade specifically comprises the following steps: Based on the increasing rate of the heating rate of the nursing equipment blade, calculating the heating rate of the latest nursing equipment blade completing the task, and obtaining the time length required for the next task of the nursing equipment blade to reach the critical temperature; judging and processing the time required by the next task of the nursing equipment blade to reach the critical temperature and the average working time of the nursing equipment blade; If the time required for the next task of the nursing equipment blade to reach the critical temperature is far longer than the average working time of the nursing equipment blade, continuously carrying out service life prediction processing on the nursing equipment blade, and determining the residual service time of the nursing equipment blade; If the length of time required by the next task of the nursing equipment blade to reach the critical temperature is longer than the average working time of the nursing equipment blade, and the length of time required by the next task of the nursing equipment blade to reach the critical temperature is not longer than the average working time of the two times of the nursing equipment blade, setting the length of time required by the next task of the nursing equipment blade to reach the critical temperature as the remaining use time of the nursing equipment blade; if the time required for the next task of the nursing equipment blade to reach the critical temperature is less than the average working time of the nursing equipment blade, the nursing equipment blade cannot be used continuously.
- 9. A time series data processing system for predicting the blade life of a nursing device, which is used for realizing the time series data processing method for predicting the blade life of the nursing device according to any one of claims 1 to 8, and is characterized by comprising: the intelligent analysis terminal is used for controlling each module to perform data screening, data comparison, data calculation and life prediction on all completed tasks of the nursing equipment and determining the remaining use time of the nursing equipment blade, and the intelligent analysis terminal is used for controlling each module to perform data transmission and information interaction; the data storage module is used for storing all completed tasks of the nursing equipment; the data screening module is used for sorting the task duration time of all completed tasks through a maximum function and determining the maximum value of the task duration time; the temperature determining module is used for carrying out data calculation and data comparison on the task completed by the nursing equipment corresponding to the maximum value of the task duration time, and determining the critical temperature of the blade of the nursing equipment; The time determining module is used for screening the intersection point of the critical temperature curve and the temperature change curve of each completed task and determining the critical temperature arrival time of the blade of the nursing equipment for each completed task; the increase rate determining module is used for carrying out average value calculation processing on all data in the temperature rise speed difference value set to obtain the temperature rise speed increase rate of the nursing equipment blade; and the life prediction module predicts the service life of the nursing equipment blade according to all completed tasks of the nursing equipment and determines the residual service time of the nursing equipment blade.
Description
Time sequence data processing method and system for predicting blade life of nursing equipment Technical Field The invention relates to the technical field of data processing analysis, in particular to a time sequence data processing method and system for predicting the service life of a blade of nursing equipment. Background Nursing equipment refers to the generic name of various instruments, apparatuses and devices used in nursing work, and is mainly used in the medical nursing fields of nursing, medical monitoring, treatment and the like of patients. The equipment has the characteristics of high precision, high stability, easy operation, safety, reliability and the like, and can meet the requirements in different medical scenes. The nursing equipment blade can become dull along with the increase of service time, and when the blade becomes dull, cutting speed will become slow, will increase with the friction of object, and the temperature rise will become fast, after the blade temperature rise is too fast, will influence the operation of nursing equipment inside electronic components, when the blade temperature is too high, will shorten the life of nursing equipment inside electronic components. Disclosure of Invention In order to solve the technical problems, the technical scheme provides a time sequence data processing method and a time sequence data processing system for predicting the service life of a blade of nursing equipment, which are provided by the technical scheme, along with the increase of the service time, the blade becomes blunt, when the blade becomes blunt, the cutting speed becomes slow, the friction with an object becomes high, the temperature rise becomes fast, the operation of electronic components in the nursing equipment is affected after the temperature rise of the blade is too fast, and the service life of the electronic components in the nursing equipment is shortened when the temperature of the blade is too high. In order to achieve the above purpose, the invention adopts the following technical scheme: A method of time-series data processing for care equipment blade life prediction, comprising: Acquiring all completed tasks of the nursing equipment, performing data screening processing on all completed tasks of the nursing equipment, and determining the critical temperature of a blade of the nursing equipment; Based on the critical temperature of the nursing equipment blade, performing data comparison processing on all completed tasks of the nursing equipment, and acquiring the critical temperature reaching time of each completed task of the nursing equipment blade; based on the critical temperature reaching time of each care equipment blade with completed tasks, carrying out data calculation processing on all the completed tasks of the care equipment to obtain the heating rate increase rate of the care equipment blade; And carrying out service life prediction processing on all completed tasks of the nursing equipment based on the temperature rise rate of the blade of the nursing equipment, and determining the residual service life of the blade of the nursing equipment. Preferably, the step of acquiring all completed tasks of the nursing device, performing data screening processing on all completed tasks of the nursing device, and determining the critical temperature of the blade of the nursing device specifically includes the following steps: Data reading processing is carried out on a data storage module of the nursing equipment, and all completed tasks of the nursing equipment are obtained; data reading processing is carried out on all completed tasks of the nursing equipment, and task duration time of all completed tasks is obtained; based on the maximum function, sequencing all task duration of the completed tasks to obtain the maximum value of the task duration; And carrying out data screening processing on the completed task of the nursing equipment corresponding to the maximum value of the task duration, and determining the critical temperature of the blade of the nursing equipment. Preferably, the data screening process is performed on the task completed by the care device corresponding to the maximum value of the task duration, and the determining the critical temperature of the blade of the care device specifically includes the following steps: Performing data reading processing on the completed task of the nursing equipment corresponding to the maximum value of the task duration, and acquiring temperature change data corresponding to the maximum value of the task duration; Based on the temperature acquisition time intervals, calculating and processing temperature change data corresponding to the maximum value of the task duration, and acquiring the temperature change rate of each temperature acquisition time interval; Counting the temperature change rate of each temperature acquisition time interval, and determining the occurrence number of each temperature change