Time sequence data processing method and system for predicting service life of blade of nursing equipment
By processing data on the blades of nursing equipment, their critical temperature and heating rate are predicted, solving the problem of temperature rise caused by blade dulling and extending the service life of the equipment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NINGBO HORD INTELLIGENT TECH CO LTD
- Filing Date
- 2026-01-13
- Publication Date
- 2026-05-05
AI Technical Summary
As nursing equipment blades become duller over time, this leads to slower cutting speeds, increased friction, and higher temperatures, affecting the operation of internal electronic components and shortening the equipment's lifespan.
By acquiring data on the tasks completed by the nursing equipment, filtering, comparing, and calculating, the critical temperature and heating rate of the blades can be determined, and their remaining usage time can be predicted to avoid high temperatures affecting equipment operation.
This extends the service life of the nursing equipment, prevents the operation of internal electronic components from being affected by the high temperature of the blade, and improves the reliability and stability of the equipment.
Smart Images
Figure CN121983264A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data processing and analysis technology, specifically to a time-series data processing method and system for predicting the lifespan of blades in nursing equipment. Background Technology
[0002] Nursing equipment refers to the general term for various instruments, devices, and equipment used in nursing work, mainly for patient care, medical monitoring, treatment, and other medical and nursing fields. These devices are characterized by high precision, high stability, ease of operation, and safety and reliability, and can meet the needs of different medical scenarios.
[0003] As nursing equipment blades are used over time, they become dull. When the blades become dull, the cutting speed slows down, friction with objects increases, and the temperature rises faster. When the blade temperature rises too quickly, it will affect the operation of the electronic components inside the nursing equipment. When the blade temperature is too high, it will shorten the lifespan of the electronic components inside the nursing equipment. Summary of the Invention
[0004] To address the aforementioned technical problems, this invention provides a time-series data processing method and system for predicting the lifespan of nursing equipment blades. This technical solution resolves the issue raised in the background section where nursing equipment blades become dull over time. When a blade becomes dull, the cutting speed slows down, friction with the object increases, and the temperature rises faster. When the blade temperature rises too quickly, it affects the operation of the internal electronic components of the nursing equipment, and excessively high blade temperatures shorten the lifespan of these components.
[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: The time-series data processing method for predicting the lifespan of nursing equipment blades includes: Acquire all completed tasks of the nursing equipment, perform data filtering and processing on all completed tasks of the nursing equipment, and determine the critical temperature of the nursing equipment blades; Based on the critical temperature of the blades of the nursing equipment, data comparison processing is performed on all completed tasks of the nursing equipment to obtain the critical temperature arrival time of the blades of the nursing equipment for each completed task. Based on the critical temperature arrival time of each completed task of the nursing equipment blade, data calculation and processing are performed on all completed tasks of the nursing equipment to obtain the heating rate growth rate of the nursing equipment blade. Based on the heating rate growth rate of the nursing equipment blades, the service life of all completed tasks of the nursing equipment is predicted to determine the remaining service life of the nursing equipment blades.
[0006] Preferably, the steps of acquiring all completed tasks of the nursing equipment, filtering and processing the data of all completed tasks of the nursing equipment, and determining the critical temperature of the nursing equipment blade specifically include the following steps: The data storage module of the nursing equipment is used to read and process data to obtain all completed tasks of the nursing equipment. Data is read and processed from all completed tasks of the nursing equipment to obtain the duration of each completed task; Based on the maximum value function, sort the duration of all completed tasks and obtain the maximum duration of each task. Data filtering is performed on the nursing equipment that has completed the task corresponding to the maximum duration of the task to determine the critical temperature of the nursing equipment blade.
[0007] Preferably, the process of filtering data on the nursing equipment that has completed the task corresponding to the maximum duration of the task, and determining the critical temperature of the nursing equipment blade, specifically includes the following steps: The data of the nursing equipment that has completed the task corresponding to the maximum duration of the task is read and processed to obtain the temperature change data corresponding to the maximum duration of the task; Based on the temperature acquisition time interval, the temperature change data corresponding to the maximum duration of the task is calculated and processed to obtain the temperature change rate for each temperature acquisition time interval. The rate of temperature change for each temperature acquisition time interval is counted to determine the number of times each rate of temperature change occurs; Based on the maximum value function, the frequency of occurrence of each temperature change rate is sorted to determine the maximum frequency of occurrence of the temperature change rate. Set the temperature data corresponding to the maximum number of occurrences of the temperature change rate as the critical temperature of the nursing equipment blade.
[0008] Preferably, the step of comparing data from all completed tasks of the nursing equipment based on the critical temperature of the blade to obtain the critical temperature arrival time of the blade for each completed task specifically includes the following steps: Construct a Cartesian coordinate system, setting the X-axis with time as the parameter and the Y-axis with temperature as the parameter; The system reads and processes data from all completed tasks of the nursing equipment to obtain temperature change data for each completed task. Plot the temperature change data for each completed task in a Cartesian coordinate system to obtain the temperature change curve for each completed task; Plot the critical temperature curve of the blade of the nursing equipment in a rectangular coordinate system to obtain the critical temperature curve; The intersection points of the critical temperature curve and the temperature change curve of each completed task are screened to determine the location of the first intersection point; Set the X-axis parameter corresponding to the location of the first intersection point to the critical temperature arrival time of the blade of each nursing device that has completed its task.
[0009] Preferably, the step of performing data calculation and processing on all completed tasks of the nursing equipment based on the critical temperature arrival time of each completed task's blade, and obtaining the rate of increase in the heating rate of the nursing equipment blade, specifically includes the following steps: The critical temperature arrival time and critical temperature of each completed nursing equipment blade are calculated and processed to obtain the heating rate of each completed nursing equipment blade. The system reads and processes data from all completed tasks of the nursing equipment to obtain the timestamp of each completed task. Based on the timestamp of each completed task, the heating rate of the nursing equipment blade for each completed task is calculated to determine the heating rate growth rate of the nursing equipment blade.
[0010] Preferably, the step of calculating the heating rate of the nursing device blade for each completed task based on the timestamp of each completed task, and determining the heating rate growth rate of the nursing device blade, specifically includes the following steps: Based on the timestamp of each completed task, sort all completed tasks of the nursing equipment to obtain the order of completed tasks of the nursing equipment; Based on the order in which the nursing equipment has completed its tasks, the heating rate of the blades of each completed nursing equipment is sorted to obtain an ordered set of heating rates of the nursing equipment blades. The difference calculation is performed on adjacent data in the ordered set of heating rates of the blades of the nursing equipment to obtain the set of heating rate difference values; The mean value of all data in the set of heating rate difference is calculated to obtain the heating rate growth rate of the blades in the nursing equipment.
[0011] Preferably, the process of predicting the remaining service life of the nursing equipment blades based on the heating rate increase rate of the blades specifically includes the following steps: The average working time of the blades in the nursing equipment is obtained by averaging the duration of all completed tasks. Based on the timestamp of each completed task, the heating rate of the nursing equipment blades for each completed task is filtered and processed to obtain the heating rate of the nursing equipment blades for the latest completed task. Based on the heating rate growth rate of nursing equipment blades, the service life of the heating rate of the nursing equipment blades that have just completed their tasks is predicted to determine the remaining service life of the nursing equipment blades.
[0012] Preferably, the process of predicting the remaining service life of the nursing equipment blades based on their heating rate growth rate to determine the remaining service life of the blades specifically includes the following steps: Based on the heating rate growth rate of the nursing equipment blades, the heating rate of the nursing equipment blades that have just completed their tasks is calculated to obtain the time required for the nursing equipment blades to reach the critical temperature for the next task. The time required for the blades of the nursing equipment to reach the critical temperature for the next task and the average working time of the blades of the nursing equipment are judged and processed. If the time required for the nursing equipment blade to reach the critical temperature for the next task is much longer than the average working time of the nursing equipment blade, the lifespan prediction process for the nursing equipment blade will continue to be performed to determine the remaining usage time of the nursing equipment blade. If the time required for the nursing equipment blade to reach the critical temperature for the next task is greater than the average working time of the nursing equipment blade, and the time required for the nursing equipment blade to reach the critical temperature for the next task is no more than twice the average working time of the nursing equipment blade, the time required for the nursing equipment blade to reach the critical temperature for the next task is set as the remaining usage time of the nursing equipment blade. If the time required for the nursing equipment blade to reach the critical temperature for the next task is less than the average working time of the nursing equipment blade, the nursing equipment blade cannot continue to be used.
[0013] Furthermore, a time-series data processing system for predicting the lifespan of nursing equipment blades is proposed, used to implement the time-series data processing method for predicting the lifespan of nursing equipment blades as described above, including: The intelligent analysis terminal is used to control various modules to perform data filtering, data comparison, data calculation, and life prediction on all completed tasks of the nursing equipment, and to determine the remaining usage time of the nursing equipment blades. The intelligent analysis terminal is also used to control data transmission and information interaction between various modules. Data storage module, which stores all completed tasks of the nursing device; The data filtering module sorts the duration of all completed tasks using a maximum value function to determine the maximum duration of each task. A temperature determination module is used to perform data calculation and data comparison on the nursing equipment that has completed the task corresponding to the maximum duration of the task, and to determine the critical temperature of the nursing equipment blade. The time determination module filters the intersection of the critical temperature curve and the temperature change curve of each completed task to determine the critical temperature arrival time of the blade of the nursing device for each completed task. The growth rate determination module is used to perform mean calculation on all data in the heating rate difference set to obtain the heating rate growth rate of the nursing equipment blade. The lifespan prediction module predicts the lifespan of the nursing equipment blades based on all completed tasks of the nursing equipment, and determines the remaining usage time of the nursing equipment blades.
[0014] Compared with the prior art, the present invention provides a time-series data processing method and system for predicting the lifespan of nursing equipment blades, which has the following beneficial effects: This invention analyzes the task content of all completed tasks of a nursing device to determine the critical temperature reaching time of each completed task's blade. As blades become dull with prolonged use, friction increases, leading to a faster temperature rise. Excessive blade temperature can affect the operation of the nursing device's electronic components. Therefore, by using the critical temperature reaching time of each completed task's blade, the lifespan of all completed tasks is predicted to determine whether the blades can continue to be used. This method determines the remaining usage time of the nursing device blades, preventing high blade temperatures from affecting the operation of the nursing device and extending its service life. Attached Figure Description
[0015] Figure 1 This is a flowchart illustrating steps S100-S400 in the time-series data processing method for predicting the lifespan of nursing equipment blades proposed in this invention. Figure 2 This is a structural block diagram of the time-series data processing system for predicting the lifespan of nursing equipment blades proposed in this invention. Detailed Implementation
[0016] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.
[0017] Reference Figure 1 As shown, the time-series data processing method for predicting the lifespan of nursing equipment blades includes: S100: Obtain all completed tasks of the nursing equipment, perform data filtering and processing on all completed tasks of the nursing equipment, and determine the critical temperature of the nursing equipment blade. S200: Based on the critical temperature of the blade of the nursing equipment, perform data comparison processing on all completed tasks of the nursing equipment to obtain the critical temperature arrival time of the blade of the nursing equipment for each completed task. S300: Based on the critical temperature arrival time of each completed task of the nursing equipment blade, perform data calculation and processing on all completed tasks of the nursing equipment to obtain the heating rate growth rate of the nursing equipment blade. S400: Based on the heating rate growth rate of the nursing equipment blades, perform lifespan prediction processing on all completed tasks of the nursing equipment to determine the remaining service life of the nursing equipment blades. Those skilled in the art will understand that the sharpness of a nursing device blade decreases with use. As the blade's sharpness decreases, its cutting speed also decreases, leading to increased friction time between the blade and the object. This increased friction time causes the blade surface temperature to rise more rapidly. Since temperature is conductive, excessively high blade temperatures can affect the operation of internal electronic components in the nursing device. Therefore, to avoid this situation, the lifespan of the blade is predicted to determine its continued usability and how much longer it can be used. This prevents the blade from generating excessive heat that could affect the operation of the nursing device and extends its lifespan.
[0018] Example 1 Step S100: Obtain all completed tasks of the nursing equipment, perform data filtering on all completed tasks of the nursing equipment, and determine the critical temperature of the nursing equipment blade. This specifically includes the following steps: S101. Perform data reading and processing on the data storage module of the nursing equipment to obtain all completed tasks of the nursing equipment; S102. Read and process data for all completed tasks of the nursing equipment to obtain the duration of all completed tasks. S103. Based on the maximum value function, sort the duration of all completed tasks and obtain the maximum duration of each task. S104. Perform data filtering on the nursing equipment that has completed the task corresponding to the maximum duration of the task, and determine the critical temperature of the nursing equipment blade. It is understandable that the tasks completed by the nursing equipment may have different content, so the usage time of the nursing equipment will also be different. Some tasks are too short, and the temperature of the nursing equipment blade will not reach the critical temperature. Therefore, it is necessary to screen all the completed tasks of the nursing equipment. The temperature of the nursing equipment blade must have reached the critical temperature in the task with the longest duration. Therefore, it is necessary to screen all the completed tasks of the nursing equipment and extract the critical temperature of the blade from the data of the task with the longest duration.
[0019] Specifically, step S104, which involves filtering data on the completed tasks of nursing equipment corresponding to the maximum task duration, and determining the critical temperature of the nursing equipment blades, includes the following steps: S1041. Read and process the data of the nursing equipment that has completed the task corresponding to the maximum duration of the task, and obtain the temperature change data corresponding to the maximum duration of the task. S1042. Based on the temperature acquisition time interval, calculate and process the temperature change data corresponding to the maximum value of the task duration to obtain the temperature change rate for each temperature acquisition time interval. S1043. Count the rate of temperature change for each temperature acquisition time interval to determine the number of times each rate of temperature change occurs; S1044. Based on the maximum value function, sort the occurrence frequency of each temperature change rate and determine the maximum value of the occurrence frequency of the temperature change rate. S1045. Set the temperature data corresponding to the maximum number of occurrences of the temperature change rate as the critical temperature of the blade of the nursing equipment. It's understandable that once the temperature of the nursing equipment blade reaches a critical value, the temperature will stop rising and remain constant, resulting in a zero rate of temperature change. However, if the temperature hasn't reached the critical value, it will continue to rise, and the rate of temperature change will fluctuate. Therefore, the frequency of occurrences of this zero rate of temperature change can be used to determine whether the nursing equipment blade has reached its critical value. Because once the temperature reaches the critical value, it will remain constant, resulting in a zero rate of temperature change, and as time passes, the frequency of these occurrences increases. Thus, the critical temperature of the nursing equipment blade can be determined by observing the frequency of these occurrences.
[0020] Example 2 Step S200: Based on the critical temperature of the nursing equipment blade, perform data comparison processing on all completed tasks of the nursing equipment to obtain the critical temperature arrival time of the nursing equipment blade for each completed task. This specifically includes the following steps: S201. Construct a rectangular coordinate system, setting the X-axis with time as the parameter and the Y-axis with temperature as the parameter; S202. Read and process the data of all completed tasks of the nursing equipment to obtain the temperature change data of each completed task; S203. Plot the temperature change data of each completed task in a rectangular coordinate system to obtain the temperature change curve of each completed task; S204. Plot the critical temperature curve of the blade of the nursing equipment in a rectangular coordinate system to obtain the critical temperature curve. S205. Filter the intersection points of the critical temperature curve and the temperature change curve of each completed task to determine the location of the first intersection point; S206. Set the X-axis parameter corresponding to the location of the first intersection point to the critical temperature arrival time of the blade of each nursing device that has completed the task. It is understandable that once the temperature of the nursing device blade reaches the critical temperature, the temperature of the nursing device blade will not change further. Therefore, in order to determine the time when the critical temperature of the nursing device blade is reached, it is necessary to determine the time when the nursing device blade first reaches this critical temperature. The first intersection of the two curves is the time when the nursing device blade first reaches this critical temperature. Therefore, the time when the critical temperature of the nursing device blade is reached can be determined by the intersection of the two curves, and thus the heating rate of the nursing device blade can be determined.
[0021] Example 3 Step S300: Based on the critical temperature arrival time of the nursing equipment blade for each completed task, perform data calculation and processing on all completed tasks of the nursing equipment to obtain the heating rate growth rate of the nursing equipment blade. This specifically includes the following steps: S301. Calculate and process the critical temperature arrival time and critical temperature of each completed nursing equipment blade to obtain the heating rate of each completed nursing equipment blade. S302. Read and process all completed tasks of the nursing equipment to obtain the timestamp of each completed task; S303. Based on the timestamp of each completed task, calculate the heating rate of the nursing equipment blade for each completed task and determine the heating rate growth rate of the nursing equipment blade.
[0022] Specifically, step S303, which calculates the heating rate of the nursing equipment blade for each completed task based on the timestamp of each completed task, and determines the heating rate growth rate of the nursing equipment blade, includes the following steps: S3031. Based on the timestamp of each completed task, sort all completed tasks of the nursing equipment to obtain the sorting order of the completed tasks of the nursing equipment. S3032. Based on the order in which the nursing equipment has completed its tasks, sort the heating rate of the blades of each completed nursing equipment to obtain an ordered set of heating rates of the nursing equipment blades. S3033. Perform difference calculation on adjacent data in the ordered heating rate set of the nursing equipment blades to obtain the heating rate difference set; S3034. Perform mean calculation on all data in the set of heating rate difference values to obtain the heating rate growth rate of the nursing equipment blade. Understandably, as the usage time of nursing equipment blades increases, the time it takes for the blades to reach the critical temperature will shorten, meaning the heating rate will increase. The heating rate of the nursing equipment blades increases in a predictable manner, meaning it increases at a certain rate of change each time. Therefore, by calculating the difference in the heating rate of the nursing equipment blades after each completed task, the difference in heating rate can be determined. After averaging these differences, the growth rate of the heating rate of the nursing equipment blades for each cycle can be obtained. Subsequently, based on the growth rate of the heating rate of the nursing equipment blades, the lifespan of the nursing equipment blades can be predicted, thus determining whether the nursing equipment blades can continue to be used.
[0023] Example 4 Step S400: Based on the heating rate increase rate of the nursing equipment blades, perform lifespan prediction processing on all completed tasks of the nursing equipment to determine the remaining service life of the nursing equipment blades. This specifically includes the following steps: S401. Calculate the average duration of all completed tasks to obtain the average working time of the blades of the nursing equipment. S402. Based on the timestamp of each completed task, perform data filtering on the heating rate of the nursing equipment blade for each completed task to obtain the heating rate of the nursing equipment blade for the latest completed task. S403. Based on the heating rate growth rate of the nursing equipment blades, perform lifespan prediction processing on the heating rate of the nursing equipment blades that have just completed their tasks, and determine the remaining service life of the nursing equipment blades.
[0024] Specifically, step S403, which involves predicting the lifespan of the nursing equipment blades based on their heating rate growth rate and determining the remaining usage time of the blades, includes the following steps: S4031. Based on the heating rate growth rate of the nursing equipment blade, calculate the heating rate of the nursing equipment blade that has just completed the task, and obtain the time required for the nursing equipment blade to reach the critical temperature for the next task. S4032. The time required for the blade of the nursing equipment to reach the critical temperature for the next task and the average working time of the blade of the nursing equipment are judged and processed. S4033. If the time required for the nursing equipment blade to reach the critical temperature for the next task is much longer than the average working time of the nursing equipment blade, continue to perform lifespan prediction processing on the nursing equipment blade to determine the remaining usage time of the nursing equipment blade. It is understandable that increasing the heating rate of the nursing equipment blade by the rate of increase of the heating rate of the nursing equipment blade after the latest task is the heating rate of the nursing equipment blade for the next task. Therefore, when the time required for the nursing equipment blade to reach the critical temperature for the next task is much greater than the average working time of the nursing equipment blade, it means that the nursing equipment blade can still be used. Then, the above calculation steps are repeated to determine the future heating rate of the nursing equipment blade until the time required for the nursing equipment blade to reach the critical temperature for the next task is less than the average working time of the nursing equipment blade. By summing up the heating times in the calculation steps, the remaining usage time of the nursing equipment blade can be obtained. S4034. If the time required for the next task of the nursing equipment blade to reach the critical temperature is greater than the average working time of the nursing equipment blade, and the time required for the next task of the nursing equipment blade to reach the critical temperature is not greater than twice the average working time of the nursing equipment blade, the time required for the next task of the nursing equipment blade to reach the critical temperature is set as the remaining usage time of the nursing equipment blade. S4035. If the time required for the blade of the nursing equipment to reach the critical temperature for the next task is less than the average working time of the blade of the nursing equipment, the blade of the nursing equipment cannot continue to be used. It is understandable that the tasks performed by the nursing equipment are different each time. However, in order to predict the lifespan of the nursing equipment blades, a reference usage time needs to be set. Therefore, by averaging the duration of all completed tasks, the average working time of the nursing equipment blades is obtained. If the time it takes for the nursing equipment blades to reach the critical temperature is less than the average working time of the nursing equipment blades, it means that the friction time between the nursing equipment blades and the object is very long and the sharpness is very low. If continued use is made, the temperature of the nursing equipment blades will affect the operation of the internal electronic components of the nursing equipment. Therefore, the nursing equipment blades need to be replaced.
[0025] Reference Figure 2 As shown, the time-series data processing system for predicting the lifespan of nursing equipment blades is used to implement the time-series data processing method for predicting the lifespan of nursing equipment blades as described above, including: The intelligent analysis terminal is used to control various modules to perform data filtering, data comparison, data calculation, and life prediction on all completed tasks of the nursing equipment, and to determine the remaining usage time of the nursing equipment blades. The intelligent analysis terminal is also used to control data transmission and information interaction between various modules. Data storage module, which stores all completed tasks of the nursing device; The data filtering module sorts the duration of all completed tasks using a maximum value function to determine the maximum duration of each task. A temperature determination module is used to perform data calculation and data comparison on the nursing equipment that has completed the task corresponding to the maximum duration of the task, and to determine the critical temperature of the nursing equipment blade. The time determination module filters the intersection of the critical temperature curve and the temperature change curve of each completed task to determine the critical temperature arrival time of the blade of the nursing device for each completed task. The growth rate determination module is used to perform mean calculation on all data in the heating rate difference set to obtain the heating rate growth rate of the nursing equipment blade. The lifespan prediction module predicts the lifespan of the nursing equipment blades based on all completed tasks of the nursing equipment, and determines the remaining usage time of the nursing equipment blades.
[0026] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.
Claims
1. A time-series data processing method for predicting the lifespan of blades in nursing equipment, characterized in that, include: Acquire all completed tasks of the nursing equipment, perform data filtering and processing on all completed tasks of the nursing equipment, and determine the critical temperature of the nursing equipment blades; Based on the critical temperature of the blades of the nursing equipment, data comparison processing is performed on all completed tasks of the nursing equipment to obtain the critical temperature arrival time of the blades of the nursing equipment for each completed task. Based on the critical temperature arrival time of each completed task of the nursing equipment blade, data calculation and processing are performed on all completed tasks of the nursing equipment to obtain the heating rate growth rate of the nursing equipment blade. Based on the heating rate growth rate of the nursing equipment blades, the service life of all completed tasks of the nursing equipment is predicted to determine the remaining service life of the nursing equipment blades.
2. The time-series data processing method for predicting the lifespan of nursing equipment blades according to claim 1, characterized in that, The process of acquiring all completed tasks of the nursing equipment, filtering and processing the data of all completed tasks, and determining the critical temperature of the nursing equipment blades specifically includes the following steps: The data storage module of the nursing equipment is used to read and process data to obtain all completed tasks of the nursing equipment. Data is read and processed from all completed tasks of the nursing equipment to obtain the duration of each completed task; Based on the maximum value function, sort the duration of all completed tasks and obtain the maximum duration of each task. Data filtering is performed on the nursing equipment that has completed the task corresponding to the maximum duration of the task to determine the critical temperature of the nursing equipment blade.
3. The time-series data processing method for predicting the lifespan of nursing equipment blades according to claim 2, characterized in that, The process of filtering data on the nursing equipment that has completed its task based on the maximum duration of the task, and determining the critical temperature of the nursing equipment blade, specifically includes the following steps: The data of the nursing equipment that has completed the task corresponding to the maximum duration of the task is read and processed to obtain the temperature change data corresponding to the maximum duration of the task; Based on the temperature acquisition time interval, the temperature change data corresponding to the maximum duration of the task is calculated and processed to obtain the temperature change rate for each temperature acquisition time interval. The rate of temperature change for each temperature acquisition time interval is counted to determine the number of times each rate of temperature change occurs; Based on the maximum value function, the frequency of occurrence of each temperature change rate is sorted to determine the maximum frequency of occurrence of the temperature change rate. Set the temperature data corresponding to the maximum number of occurrences of the temperature change rate as the critical temperature of the nursing equipment blade.
4. The time-series data processing method for predicting the lifespan of nursing equipment blades according to claim 3, characterized in that, The process of comparing data from all completed tasks of the nursing equipment based on the critical temperature of the blade to obtain the critical temperature arrival time of the blade for each completed task includes the following steps: Construct a Cartesian coordinate system, setting the X-axis with time as the parameter and the Y-axis with temperature as the parameter; The system reads and processes data from all completed tasks of the nursing equipment to obtain temperature change data for each completed task. Plot the temperature change data for each completed task in a Cartesian coordinate system to obtain the temperature change curve for each completed task; Plot the critical temperature curve of the blade of the nursing equipment in a rectangular coordinate system to obtain the critical temperature curve; The intersection points of the critical temperature curve and the temperature change curve of each completed task are screened to determine the location of the first intersection point; Set the X-axis parameter corresponding to the location of the first intersection point to the critical temperature arrival time of the blade of each nursing device that has completed its task.
5. The time-series data processing method for predicting the lifespan of nursing equipment blades according to claim 4, characterized in that, The process of calculating and processing data on all completed tasks of the nursing equipment blades based on the critical temperature arrival time of each completed task, to obtain the rate of increase in the heating rate of the nursing equipment blades, specifically includes the following steps: The critical temperature arrival time and critical temperature of each completed nursing equipment blade are calculated and processed to obtain the heating rate of each completed nursing equipment blade. The system reads and processes data from all completed tasks of the nursing equipment to obtain the timestamp of each completed task. Based on the timestamp of each completed task, the heating rate of the nursing equipment blade for each completed task is calculated to determine the heating rate growth rate of the nursing equipment blade.
6. The time-series data processing method for predicting the lifespan of nursing equipment blades according to claim 5, characterized in that, The process of calculating the heating rate of the nursing equipment blade for each completed task based on the timestamp of each completed task, and determining the heating rate growth rate of the nursing equipment blade, specifically includes the following steps: Based on the timestamp of each completed task, sort all completed tasks of the nursing equipment to obtain the order of completed tasks of the nursing equipment; Based on the order in which the nursing equipment has completed its tasks, the heating rate of the blades of each completed nursing equipment is sorted to obtain an ordered set of heating rates of the nursing equipment blades. The difference calculation is performed on adjacent data in the ordered set of heating rates of the blades of the nursing equipment to obtain the set of heating rate difference values; The mean value of all data in the set of heating rate difference is calculated to obtain the heating rate growth rate of the blades in the nursing equipment.
7. The time-series data processing method for predicting the lifespan of nursing equipment blades according to claim 6, characterized in that, The process of predicting the remaining service life of the nursing equipment blades based on the rate of increase in the heating rate of the blades includes the following steps: The average working time of the blades in the nursing equipment is obtained by averaging the duration of all completed tasks. Based on the timestamp of each completed task, the heating rate of the nursing equipment blades for each completed task is filtered and processed to obtain the heating rate of the nursing equipment blades for the latest completed task. Based on the heating rate growth rate of nursing equipment blades, the service life of the heating rate of the nursing equipment blades that have just completed their tasks is predicted to determine the remaining service life of the nursing equipment blades.
8. The time-series data processing method for predicting the lifespan of nursing equipment blades according to claim 7, characterized in that, The process of predicting the remaining service life of nursing equipment blades based on their heating rate growth rate, and using this method to determine the remaining service life of the blades includes the following steps: Based on the heating rate growth rate of the nursing equipment blades, the heating rate of the nursing equipment blades that have just completed their tasks is calculated to obtain the time required for the nursing equipment blades to reach the critical temperature for the next task. The time required for the blades of the nursing equipment to reach the critical temperature for the next task and the average working time of the blades of the nursing equipment are judged and processed. If the time required for the nursing equipment blade to reach the critical temperature for the next task is much longer than the average working time of the nursing equipment blade, the lifespan prediction process for the nursing equipment blade will continue to be performed to determine the remaining usage time of the nursing equipment blade. If the time required for the nursing equipment blade to reach the critical temperature for the next task is greater than the average working time of the nursing equipment blade, and the time required for the nursing equipment blade to reach the critical temperature for the next task is no more than twice the average working time of the nursing equipment blade, the time required for the nursing equipment blade to reach the critical temperature for the next task is set as the remaining usage time of the nursing equipment blade. If the time required for the nursing equipment blade to reach the critical temperature for the next task is less than the average working time of the nursing equipment blade, the nursing equipment blade cannot continue to be used.
9. A time-series data processing system for predicting the lifespan of nursing equipment blades, used to implement the time-series data processing method for predicting the lifespan of nursing equipment blades as described in any one of claims 1-8, characterized in that, include: The intelligent analysis terminal is used to control various modules to perform data filtering, data comparison, data calculation, and life prediction on all completed tasks of the nursing equipment, and to determine the remaining usage time of the nursing equipment blades. The intelligent analysis terminal is also used to control data transmission and information interaction between various modules. Data storage module, which stores all completed tasks of the nursing device; The data filtering module sorts the duration of all completed tasks using a maximum value function to determine the maximum duration of each task. A temperature determination module is used to perform data calculation and data comparison on the nursing equipment that has completed the task corresponding to the maximum duration of the task, and to determine the critical temperature of the nursing equipment blade. The time determination module filters the intersection of the critical temperature curve and the temperature change curve of each completed task to determine the critical temperature arrival time of the blade of the nursing device for each completed task. The growth rate determination module is used to perform mean calculation on all data in the heating rate difference set to obtain the heating rate growth rate of the nursing equipment blade. The lifespan prediction module predicts the lifespan of the nursing equipment blades based on all completed tasks of the nursing equipment, and determines the remaining usage time of the nursing equipment blades.