CN117789954A - A data management method and platform for rehabilitation nursing equipment - Google Patents

Abstract

The invention relates to the technical field of rehabilitation and nursing equipment data management, in particular to a rehabilitation and nursing equipment data management method and a platform. The method comprises the following steps: collecting rehabilitation and nursing equipment data from different data sources, and updating the equipment state of the rehabilitation and nursing equipment data to obtain equipment state updating data; performing frequency of use feature extraction and load state feature extraction on the rehabilitation and nursing equipment data according to the equipment state updating data to obtain frequency of use feature data and load state feature data; performing equipment gap calculation on the using frequency characteristic data and the load state characteristic data according to the rehabilitation and nursing equipment data to obtain rehabilitation and nursing equipment gap data; and performing equipment calling plan generation on the rehabilitation and nursing equipment data according to the rehabilitation and nursing equipment gap data to obtain the rehabilitation and nursing equipment calling plan data. The invention optimizes equipment configuration and use, discovers equipment gaps in time, and makes an effective equipment calling plan.

Description

A data management method and platform for rehabilitation nursing equipment

Technical field

The invention relates to the technical field of rehabilitation nursing equipment data management, and in particular to a rehabilitation nursing equipment data management method and platform.

Background technique

Rehabilitation nursing equipment data management method refers to the method of effectively collecting, storing, processing and analyzing data related to rehabilitation nursing equipment (such as walkers, orthotics, etc.), aiming to improve the efficiency and quality of rehabilitation nursing to better Meet patients' rehabilitation needs. Conventional methods often simply collect, store and visualize the data of rehabilitation nursing equipment. The degree of analysis and utilization of data is low and the potential value of the data cannot be fully explored, resulting in potential waste of rehabilitation nursing equipment. and underutilization.

Contents of the invention

In order to solve the above technical problems, the present invention proposes a rehabilitation nursing equipment data management method and platform to solve at least one of the above technical problems.

This application provides a data management method for rehabilitation nursing equipment, including the following steps:

Step S1: Collect rehabilitation nursing equipment data from different data sources, update the equipment status of the rehabilitation nursing equipment data, and obtain equipment status update data;

Step S2: extracting the usage frequency characteristics and the load status characteristics of the rehabilitation nursing equipment data according to the equipment status update data, and obtaining the usage frequency characteristic data and the load status characteristic data;

Step S3: Calculate the equipment gap based on the usage frequency characteristic data and the load status characteristic data based on the rehabilitation nursing equipment data to obtain the rehabilitation nursing equipment gap data;

Step S4: Generate an equipment calling plan for the rehabilitation nursing equipment data according to the rehabilitation nursing equipment shortage data, and obtain rehabilitation nursing equipment calling plan data to perform rehabilitation nursing equipment calling operations.

In the present invention, the status information of rehabilitation nursing equipment is collected and updated in real time, including frequency of use and load status, etc., so as to understand the usage of the equipment in a timely manner and discover potential problems and abnormalities in a timely manner. Based on the frequency of use and load status characteristic data, the demand and usage of rehabilitation nursing equipment can be more accurately assessed, the configuration of equipment can be optimized, resources can be reasonably allocated, and the efficiency of rehabilitation equipment can be improved. Through step S3, the gap data of rehabilitation nursing equipment can be calculated based on actual usage, including the gap between equipment demand and actual supply, so that the shortage of rehabilitation nursing equipment can be discovered and solved in a timely manner to ensure the smooth progress of rehabilitation treatment. Based on the gap data of rehabilitation nursing equipment, an effective equipment call plan can be formulated (step S4), including replenishing shortage equipment and adjusting the equipment use plan.

Preferably, step S1 is specifically:

Step S11: Collect rehabilitation nursing equipment data from different data sources;

Step S12: extracting equipment availability, maintenance requirements and usage status of rehabilitation nursing equipment data to obtain equipment availability data, maintenance requirements data and usage status data;

Step S13: Update the equipment status according to the equipment availability data, maintenance requirement data and usage data to obtain equipment status update data.

Step S11 in the present invention ensures the comprehensiveness and diversity of data by collecting rehabilitation care equipment data from different data sources, making subsequent analysis more accurate and comprehensive. Step S12 extracts various aspects of rehabilitation nursing equipment data, including key information such as equipment availability, maintenance requirements and usage, making subsequent data processing more accurate and targeted. Step S13 updates the equipment status based on the equipment availability data, maintenance demand data and usage data, ensuring timely updating and reflection of the equipment status, and providing an accurate data basis for subsequent analysis and decision-making. By updating the status of equipment in a timely manner, we can better understand the usage and maintenance needs of rehabilitation care equipment, thereby planning resources and allocating equipment more effectively, and improving resource utilization efficiency. Timely updating of equipment status and extracting maintenance requirement data can help promptly discover and deal with equipment failures and losses, and reduce interruptions or delays in rehabilitation treatment caused by equipment problems.

Preferably, the step of extracting usage in step S12 includes the following steps:

Sort and group the rehabilitation nursing equipment data according to the equipment type data in the rehabilitation nursing equipment data to obtain the rehabilitation nursing equipment grouping data;

Calculate the usage of the grouped data of rehabilitation nursing equipment to obtain usage data, where the usage data includes usage data, usage duration data and usage frequency data.

In the present invention, by sorting and grouping data according to the equipment type in the rehabilitation nursing equipment data, personalized data processing can be performed for different types of equipment, and the usage of each type of equipment can be understood more accurately. By calculating the usage of the grouped data of rehabilitation nursing equipment, data such as the number of uses, usage time and usage frequency can be obtained, and the usage of rehabilitation nursing equipment can be accurately evaluated, including information on usage frequency, duration, etc. After understanding the usage of rehabilitation nursing equipment, resource allocation and management can be better carried out to ensure the adequate supply of commonly used equipment and improve the efficiency and quality of rehabilitation treatment. By monitoring the usage of rehabilitation nursing equipment, equipment with large usage or high frequency of use can be discovered in time, so as to give priority to the maintenance and maintenance of these equipment to ensure their normal operation and improve the quality and stability of rehabilitation services. According to the usage data, targeted equipment usage strategies can be formulated, including measures such as adjusting the equipment usage time and strengthening the maintenance of commonly used equipment to maximize the effectiveness and value of the equipment.

Preferably, step S2 is specifically:

Step S21: Generate equipment usage time window data based on rehabilitation care equipment data and equipment status update data;

Step S22: Extract usage frequency features from the rehabilitation nursing equipment data based on the equipment usage time window data to obtain usage frequency feature data;

Step S23: Extract load status features from the rehabilitation care equipment data according to the equipment status update data to obtain load status feature data.

Step S21 in the present invention generates equipment usage time window data based on rehabilitation nursing equipment data and equipment status update data, and divides the usage of rehabilitation nursing equipment into different time periods, making the analysis more targeted and accurate. By extracting usage frequency features based on the equipment usage time window data in step S22, the usage frequency of the rehabilitation nursing equipment in different time periods can be understood, and the utilization rate and demand of the equipment can be better assessed. Step S23 extracts load status features from the rehabilitation nursing equipment data based on the equipment status update data, which helps to evaluate the load status of the rehabilitation nursing equipment, including the equipment's workload, maintenance requirements and other information, and provides accurate information for subsequent resource allocation and maintenance. data support. By extracting usage frequency features and load status features, the usage and working status of rehabilitation nursing equipment can be more accurately assessed, and can provide support for more accurate resource management decisions, including equipment deployment, maintenance, and updates. By understanding the frequency of use and load status of rehabilitation care equipment in a timely manner, we can reasonably arrange the use time and maintenance plan of the equipment to improve the efficiency and quality of rehabilitation treatment equipment.

Preferably, the equipment use time window data includes first equipment use time window data and second equipment use time window data. Step S21 is specifically:

Generate time windows based on the equipment type data in the rehabilitation care equipment data to obtain primary time window data;

Performing weighted calculation on the primary time window data according to the equipment status update data to obtain the first equipment use time window data;

Rehabilitation nursing use is estimated based on the equipment type data in the rehabilitation nursing equipment data to obtain the estimated use data for the rehabilitation stage;

The event trigger window is generated based on the estimated data during the rehabilitation stage, and the second equipment usage time window data is obtained.

In the present invention, through step S21, the time window is generated according to the equipment type data in the rehabilitation nursing equipment data to obtain the primary time window data. The time window data can be customized according to different equipment types to better adapt to the use of different types of equipment. Condition. The weighted calculation in step S21 is a weighted calculation of the primary time window data based on the equipment status update data, which can more accurately reflect the actual usage of the equipment. Taking into account the usage frequency and load status in different time periods, the time window data is improved. accuracy and reliability. Estimating the use of rehabilitation equipment in the rehabilitation stage based on the data of rehabilitation care equipment, and obtaining the estimated use data in the rehabilitation stage, can help predict and plan the use of equipment according to the staged needs of rehabilitation treatment, and reduce the waste caused by improper configuration of rehabilitation treatment equipment. The event trigger window generation in step S21 can generate second equipment usage time window data based on the estimated usage data of the rehabilitation stage, triggering the use and preparation of equipment at specific events or stages, thereby improving the pertinence and efficiency of the use of rehabilitation treatment equipment. Through customized time window data and recovery stage usage estimates, we can better plan and optimize the resource allocation and use planning of rehabilitation nursing equipment, reasonably arrange the use time and maintenance plan of equipment according to actual needs and treatment stages, and improve resources. usage efficiency.

Preferably, step S22 is specifically:

Extract usage frequency features from the rehabilitation nursing equipment data based on the equipment usage time window data to obtain the first usage frequency feature data;

Perform an average calculation on the first frequency of use feature data based on the equipment type data in the rehabilitation care equipment data to obtain the second frequency of use feature data;

Perform moving average processing on the second frequency of use characteristic data according to the equipment usage time window data to obtain frequency of use characteristic data.

In the present invention, through step S22, the frequency of use feature extraction is performed on the rehabilitation nursing equipment data according to the equipment use time window data, and the first frequency of use feature data is obtained. The frequency of use of each equipment in a specific time period can be accurately understood, which provides the basis for subsequent Provide accurate data support for analysis and decision-making. In step S22, the average calculation of the first frequency of use feature data is performed based on the equipment type data in the rehabilitation care equipment data, which helps to take into account the differences between different types of equipment and obtain more objective and comprehensive usage frequency feature data. In step S22, moving average processing is performed on the second usage frequency characteristic data according to the equipment usage time window data, which can smooth the usage frequency characteristic data, reduce data volatility, and better reflect the usage trends and changing patterns of rehabilitation nursing equipment. By averaging and smoothing the usage frequency feature data, the stability and reliability of the data can be improved, errors and deviations caused by data fluctuations can be reduced, and a more reliable basis can be provided for subsequent analysis and decision-making. By accurately extracting and processing usage frequency feature data, we can better understand the actual usage and trends of equipment, help optimize resource allocation and usage planning, reasonably arrange equipment maintenance and updates, and improve resource utilization efficiency and the effect of rehabilitation treatment. .

Preferably, step S23 is specifically:

Step S231: Extract maintenance requirements from the equipment status update data to obtain maintenance requirement data, where the maintenance requirement data is one of maintenance-required data or maintenance-free data;

Step S232: When it is determined that the maintenance requirement data is maintenance data, perform load state feature extraction on the rehabilitation care equipment data according to the equipment type data in the rehabilitation care equipment data to obtain the first load state feature data;

Step S233: When it is determined that the maintenance requirement data is maintenance-free data, perform load state feature extraction on the rehabilitation nursing equipment data to obtain the second load state feature data;

Step S232 is specifically:

When it is determined that the equipment type data in the rehabilitation nursing equipment data is sports equipment type data, motion trajectory feature extraction is performed on the rehabilitation nursing equipment data to obtain equipment motion feature data;

When it is determined that the equipment type data in the rehabilitation nursing equipment data is pressure equipment type data, motion trajectory feature extraction is performed on the rehabilitation nursing equipment data to obtain equipment pressure feature data;

When it is determined that the equipment type data in the rehabilitation nursing equipment data is temperature equipment type data, motion trajectory feature extraction is performed on the rehabilitation nursing equipment data to obtain equipment temperature feature data.

In the present invention, through step S23, the corresponding load status feature extraction method is selected according to different situations of maintenance demand data, which helps to accurately understand the load status of the rehabilitation nursing equipment, including the working characteristics and usage conditions of the equipment. In step S232, corresponding load state features are extracted according to the equipment type data in the rehabilitation care equipment data, including motion features, pressure features, temperature features, etc., to process different types of rehabilitation care equipment in a targeted manner, which improves the efficiency of data processing. Personalization and accuracy. By combining maintenance demand data with load status feature extraction, the correlation between equipment maintenance needs and load status can be better assessed, potential problems can be discovered in a timely manner and corresponding measures can be taken to deal with them. Extracting corresponding load status characteristics based on different equipment types can more accurately evaluate the working status and usage of equipment, help optimize equipment maintenance plans and resource allocation, and improve the efficiency and quality of equipment maintenance. By accurately extracting load status characteristics and correlating maintenance demand data, we can better understand the operation of rehabilitation nursing equipment, help optimize the planning and execution of rehabilitation nursing services, and improve the effect and quality of rehabilitation treatment.

Preferably, step S3 is specifically:

Step S31: Obtain rehabilitation care equipment demand data;

Step S32: Perform equipment life estimation on the frequency of use characteristic data and load status characteristic data to obtain equipment life estimation data;

Step S33: dynamically labeling the rehabilitation and nursing equipment data according to the equipment life estimation data to obtain rehabilitation and nursing equipment labeling data;

Step S34: Perform gap calculation based on the rehabilitation nursing equipment demand data and rehabilitation nursing equipment labeling data to obtain rehabilitation nursing equipment gap data, which includes rehabilitation nursing equipment non-gap data, rehabilitation nursing equipment positive gap data, and rehabilitation nursing equipment negative gap data. The positive gap data of nursing equipment means that the marked data of rehabilitation nursing equipment is less than the demand data of rehabilitation nursing equipment. The negative gap data of rehabilitation nursing equipment means that the marked data of rehabilitation nursing equipment is greater than the demand data of rehabilitation nursing equipment.

In the present invention, through the acquisition and annotation of the rehabilitation nursing equipment demand data in step S3, combined with the equipment life estimation data, the demand and supply relationship of the rehabilitation nursing equipment can be comprehensively assessed, which helps to understand the actual demand for the equipment and make corresponding decisions. Adjustment and planning to ensure the smooth progress of rehabilitation nursing services. Through step S32, the equipment life expectancy is estimated based on the frequency of use characteristic data and the load status characteristic data. The life span and usage status of the rehabilitation care equipment can be evaluated, maintenance plans and update strategies can be adjusted in a timely manner, the service life of the equipment can be extended, maintenance costs can be reduced, and resources can be increased. usage efficiency. The dynamic labeling in step S33 labels the rehabilitation care equipment based on the equipment life estimation data, which is helpful for dynamic monitoring and management of the use status of the equipment. For the gap calculation in step S34, based on the demand data and annotation data of rehabilitation nursing equipment, the gap situation of equipment can be calculated, including positive gap and negative gap, and the procurement and deployment plan of equipment can be adjusted in a timely manner to ensure adequate supply of rehabilitation nursing equipment and meet the requirements. Need for rehabilitation. Comprehensive consideration of equipment demand, life expectancy and gap situation can optimize the resource allocation and use planning of rehabilitation nursing equipment, ensure the rational use and maintenance of equipment, and improve the efficiency and quality of rehabilitation nursing services.

Preferably, step S33 is specifically:

Perform equipment usage load hierarchical mapping on the rehabilitation nursing equipment data based on the rehabilitation nursing equipment demand data to obtain equipment usage load hierarchical mapping data;

Perform a weighted calculation on the equipment life estimate data based on the equipment usage load level mapping data to obtain the equipment life estimate weighted data;

Label the rehabilitation nursing equipment data based on the weighted data of the equipment life expectancy to obtain the rehabilitation nursing equipment labeling data.

In the present invention, by mapping the equipment usage load level according to the rehabilitation and nursing equipment demand data, the usage and load level of each equipment can be evaluated more accurately, making the estimation of the equipment life more accurate, avoiding the problem of treating all equipment equally in the traditional method, thereby leading to the waste problem caused by relatively rough equipment use. By weighting the equipment life estimation data according to the equipment usage load level mapping data, the equipment life estimation can be personalized according to the importance and impact of different load levels, which can better reflect the actual use of the equipment and improve the accuracy and reliability of life estimation. By marking the rehabilitation and nursing equipment data according to the weighted data of the equipment life estimation, the life status and estimation of each equipment can be clearly understood, providing more detailed information, which is helpful to better formulate equipment deployment plans and maintenance strategies, thereby improving the utilization efficiency and service quality of rehabilitation and nursing equipment.

Preferably, the present application also provides a rehabilitation nursing equipment data management platform for executing the rehabilitation nursing equipment data management method as described above, and the rehabilitation nursing equipment data management platform includes:

The equipment status update module is used to collect rehabilitation and nursing equipment data from different data sources, and update the equipment status of the rehabilitation and nursing equipment data to obtain equipment status update data;

A rehabilitation nursing equipment feature extraction module is used to extract usage frequency features and load status features of rehabilitation nursing equipment data according to equipment status update data, and obtain usage frequency feature data and load status feature data;

The equipment gap calculation module is used to calculate the equipment gap based on the frequency of use characteristic data and the load status characteristic data based on the rehabilitation nursing equipment data, and obtain the rehabilitation nursing equipment gap data;

The equipment calling plan generation module is used to generate an equipment calling plan for the rehabilitation nursing equipment data based on the rehabilitation nursing equipment gap data, and obtain the rehabilitation nursing equipment calling plan data to carry out the rehabilitation nursing equipment calling operation.

The beneficial effects of the present invention are: by collecting data from different data sources and updating the equipment status, the status of rehabilitation and nursing equipment can be monitored in real time, so that users can use existing equipment resources more effectively and avoid waste and idleness of resources. By extracting the frequency of use characteristics and load status characteristics, the actual use frequency and load status of the equipment can be deeply understood, which is helpful to accurately evaluate the use of the equipment. The application of the equipment gap calculation method can timely discover the supply and demand gap of rehabilitation and nursing equipment, including the positive and negative gaps of the equipment, which helps the organization to adjust the equipment procurement plan and deployment strategy in time to ensure the continuity and quality of the use of rehabilitation and nursing equipment. The equipment call plan is generated according to the rehabilitation and nursing equipment gap data, which can more effectively plan the use and scheduling of equipment, so that the equipment can be called on demand, avoiding poor rehabilitation treatment effects or waste of resources caused by equipment shortages or surpluses. Combining the above measures, the management and scheduling of rehabilitation and nursing equipment can be optimized, and the efficiency and quality of services can be improved. Through timely maintenance, updating and reasonable scheduling of equipment resources.

BRIEF DESCRIPTION OF THE DRAWINGS

Other features, objects and advantages of the present application will become more apparent upon reading the detailed description of the non-limiting implementation with reference to the following drawings:

Figure 1 shows a step flow chart of a rehabilitation care equipment data management method according to an embodiment;

Figure 2 shows a step flow chart of a method for generating equipment status update data according to an embodiment;

Figure 3 shows a step flow chart of a method for extracting data features of rehabilitation nursing equipment according to an embodiment;

Figure 4 shows a step flow chart of a method for generating equipment usage time window data according to an embodiment.

Detailed ways

The technical method of the patent of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the scope of protection of the present invention.

In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and/or processor methods and/or microcontroller methods.

It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and/or" used herein includes any and all combinations of one or more of the listed associated items.

Please refer to Figures 1 to 4. This application provides a rehabilitation nursing equipment data management method, including the following steps:

Step S1: collecting rehabilitation nursing equipment data from different data sources, and updating equipment status of the rehabilitation nursing equipment data to obtain equipment status update data;

Specifically, sensor equipment and data collection systems are used to collect data such as the usage and working status of rehabilitation care equipment into the system in real time. Through the set rules and algorithms, the collected data is analyzed and processed, the status information of the equipment, such as working status, availability, etc., is updated, and stored as equipment status update data.

Specifically, a series of rules are set to define the working status and availability of equipment. For example: If the running time of the device exceeds a certain threshold, it is marked as "working" status. If a device has not been used for several days, it is marked as "standby". If a device malfunctions beyond repair time, it is marked as "failed". Design algorithms to analyze and process collected data based on rules. For example: Use time series analysis to detect equipment usage patterns and trends to determine operating status and availability. Use machine learning algorithms, such as neural network algorithms and generative decision tree algorithms, to identify abnormal behavior or failure modes of equipment and predict the future state of equipment. According to the results of rule and algorithm analysis, the status information of each rehabilitation nursing equipment is updated, including working status, availability, etc.

Specifically, a medical institution has multiple rehabilitation and nursing equipment, including treadmills, exercise bikes, and tensioners. By installing sensors and data collection systems, the running time, usage frequency and fault information of each device can be collected in real time. Based on these data, the following rules and algorithms are formulated to update the status information of the equipment: Rule 1: If the running time of the equipment exceeds 8 hours per day, it will be marked as "working" status. Rule 2: If the device has not been used for 3 consecutive days, mark it as "standby". Rule 3: If a device fails and the repair time exceeds 48 hours, mark it as "Faulty" status. Based on these rules, the following algorithm is designed to update status information: time series analysis is used to monitor the running time of the equipment, and the part exceeding 8 hours is marked as "working" status. The usage frequency of the device is analyzed, and devices that have not been used for 3 consecutive days are marked as "standby". Use machine learning algorithms to identify equipment failure modes and predict whether repairs will take more than 48 hours. Finally, according to these rules and algorithms, the status information of the rehabilitation nursing equipment is updated and stored as equipment status update data to facilitate subsequent management and maintenance work.

Step S2: Extract usage frequency features and load status features from the rehabilitation care equipment data based on the equipment status update data to obtain usage frequency feature data and load status feature data;

Specifically, machine learning or statistical analysis methods are used to extract usage frequency features and load status features based on equipment status update data. Frequency of use characteristics may include indicators such as the number of daily uses and duration of use; load status characteristics may include indicators such as load size and load stability of the equipment.

Specifically, machine learning or statistical analysis methods are used to extract usage frequency features from the equipment status update data. The features include: Daily usage times: count the number of times each device is used every day. Usage time: calculate the daily usage time of each device. Usage frequency: calculate the daily usage frequency of each device, that is, the number of times used divided by the usage time.

Use machine learning or statistical analysis methods to extract load status features from equipment status update data. Features include: Load size of equipment: Estimate load size based on the working status and usage time of the equipment. For example, equipment that operates for a longer period of time is subject to a greater load. Load stability: Evaluate load stability based on changes in equipment working status. For example, equipment that has a long continuous working time and stable status has higher load stability.

Specifically, there are multiple treadmills, and the daily working status and usage time data of each treadmill are collected.

Using machine learning models or statistical analysis methods, these data are analyzed to extract features such as the number of times each treadmill is used per day, the duration of use, and the frequency of use. At the same time, based on the working status and usage time data of each treadmill, the load size and load stability of each treadmill are estimated. The resulting feature data can be used for further analysis and modeling, such as predicting the risk of equipment failure and optimizing equipment usage arrangements.

Step S3: Calculate the equipment gap based on the usage frequency characteristic data and the load status characteristic data based on the rehabilitation nursing equipment data to obtain the rehabilitation nursing equipment gap data;

Specifically, based on the usage frequency characteristic data and load status characteristic data, combined with the institution's demand for rehabilitation care equipment, the supply and demand gap of the equipment is calculated. For example, by comparing actual usage with expected demand, the gap situation of equipment can be determined, including positive gaps and negative gaps.

Specifically, the usage frequency characteristic data includes indicators such as the number of times each device is used per day and the duration of use. The load state characteristic data includes indicators such as the load size of the equipment and the load stability. According to the rehabilitation and nursing needs of the institution, determine the expected demand for each type of equipment, for example, how many treadmills, spinning bikes, etc. are needed each day. According to the usage frequency characteristic data and the load state characteristic data, calculate the actual usage, that is, the actual number of times each type of equipment is used per day, the duration of use, etc. Compare the actual usage with the expected demand, and calculate the supply and demand gap for each type of equipment. A positive gap indicates that the actual usage is lower than the expected demand, that is, the number of equipment needs to be increased to meet the demand; a negative gap indicates that the actual usage is higher than the expected demand, that is, the number of equipment needs to be reduced to avoid waste.

Specifically, a rehabilitation nursing center has 10 treadmills and 5 spinning bikes. Based on the analysis of usage frequency characteristic data and load status characteristic data, the actual usage is an average of 8 treadmills and 3 spinning bikes used every day. The center's rehabilitation care needs show that at least 10 treadmills and 5 spinning bikes are used every day. Based on the comparison between actual usage and expected demand, it is calculated that the positive gap is 2 treadmills and 2 spinning bikes, indicating that the center needs to add 2 more treadmills and 2 spinning bikes to meet demand.

Step S4: Generate an equipment calling plan for the rehabilitation nursing equipment data according to the rehabilitation nursing equipment shortage data, and obtain rehabilitation nursing equipment calling plan data to perform rehabilitation nursing equipment calling operations.

Specifically, based on the gap data of rehabilitation nursing equipment, a reasonable calling plan is designed to ensure the timely deployment and use of rehabilitation nursing equipment. For example, according to the gap situation and priority, plans for the purchase, maintenance, and update of equipment can be formulated to meet the needs of rehabilitation treatment equipment. At the same time, taking into account resource utilization efficiency and cost control, the deployment plan is optimized to ensure the rational use and management of rehabilitation care equipment.

Specifically, the shortage of rehabilitation and nursing equipment is sorted according to the urgency and importance to determine the type of equipment to be allocated first. For equipment with a positive shortage, a procurement plan is formulated, including determining the purchase quantity, purchase time and supplier, etc. According to the budget and the actual situation of the supplier, a reasonable procurement plan is formulated to ensure that the shortage is filled in time. For equipment that is aged or seriously damaged, an update plan is formulated, including determining the update time, update quantity and update method, etc. The call plan is optimized in combination with the shortage of rehabilitation and nursing equipment and the efficiency of resource utilization. By reasonably arranging procurement, maintenance and update plans, costs can be reduced as much as possible and resource utilization efficiency can be improved.

A rehabilitation center found that 10 treadmills were used frequently, but only 8 could meet the demand, so it formulated a procurement plan and planned to purchase 2 new treadmills within a month. At the same time, it was discovered that 5 spinning bicycles needed maintenance, and a maintenance plan was formulated to arrange maintenance at the beginning of next month and ensure that they were put into use in time after the maintenance was completed. For some seriously aging equipment, such as three severely aging treadmills, it was decided to update it in the next quarter to ensure the performance and safety of the equipment. Through the optimization of the call plan, the reasonable deployment and management of rehabilitation nursing equipment is ensured.

In the present invention, the status information of rehabilitation nursing equipment is collected and updated in real time, including frequency of use and load status, etc., so as to understand the usage of the equipment in a timely manner and discover potential problems and abnormalities in a timely manner. Based on the frequency of use and load status characteristic data, the demand and usage of rehabilitation nursing equipment can be more accurately assessed, the configuration of equipment can be optimized, resources can be reasonably allocated, and the efficiency of rehabilitation equipment can be improved. Through step S3, the gap data of rehabilitation nursing equipment can be calculated based on actual usage, including the gap between equipment demand and actual supply, so that the shortage of rehabilitation nursing equipment can be discovered and solved in a timely manner to ensure the smooth progress of rehabilitation treatment. Based on the gap data of rehabilitation nursing equipment, an effective equipment call plan can be formulated (step S4), including replenishing shortage equipment and adjusting the equipment usage plan.

Preferably, step S1 is specifically:

Step S11: Collect rehabilitation nursing equipment data from different data sources;

Specifically, sensor equipment and data collection systems are used to collect various data on rehabilitation care equipment from different data sources, including but not limited to information such as the equipment's working status, frequency of use, duration of use, etc. Data sources include sensors, equipment records, medical facility management systems, etc.

Step S12: Extract equipment availability, maintenance requirements and usage data on the rehabilitation nursing equipment data to obtain equipment availability data, maintenance requirement data and usage data;

Specifically, data processing and feature extraction are performed on the collected rehabilitation and nursing equipment data. For example, the availability of the equipment is extracted to determine whether the equipment is available based on the equipment's operating status, maintenance status and other information; the maintenance requirements are extracted to determine whether maintenance is required based on the equipment's working hours, operating status and other information; the usage is extracted to evaluate the actual usage of the equipment based on the frequency of use, usage duration and other information.

Step S13: Update the equipment status according to the equipment availability data, maintenance requirement data and usage data to obtain equipment status update data.

Specifically, the equipment status is updated based on the equipment availability data, maintenance requirement data, and usage data extracted in step S12. For example, if the maintenance demand data of a certain rehabilitation nursing equipment shows that maintenance is needed, then the status of the equipment is updated to "requires maintenance"; if the usage data of the equipment shows that the frequency of use is very low, then the status of the equipment is updated to "requires maintenance". Low frequency of use”. The updated equipment status data can be used for subsequent data analysis and decision-making.

Specifically, one of the treadmills was used infrequently, but sensor data showed it had been running for a long time, was malfunctioning, or needed maintenance. Based on this information, the system updates its status to "Maintenance Required". At the same time, another spinning bike is used very frequently and its status is "normal". In this way, users can allocate resources in a timely manner according to the actual situation of the equipment.

Step S11 in the present invention ensures the comprehensiveness and diversity of data by collecting rehabilitation care equipment data from different data sources, making subsequent analysis more accurate and comprehensive. Step S12 extracts various aspects of rehabilitation nursing equipment data, including key information such as equipment availability, maintenance requirements and usage, making subsequent data processing more accurate and targeted. Step S13 updates the equipment status based on the equipment availability data, maintenance demand data and usage data, ensuring timely updating and reflection of the equipment status, and providing an accurate data basis for subsequent analysis and decision-making. By updating the status of equipment in a timely manner, we can better understand the usage and maintenance needs of rehabilitation care equipment, thereby planning resources and allocating equipment more effectively, and improving resource utilization efficiency. Timely updating of equipment status and extracting maintenance requirement data can help promptly discover and deal with equipment failures and losses, and reduce interruptions or delays in rehabilitation treatment caused by equipment problems.

Preferably, the step of extracting usage in step S12 includes the following steps:

Sort and group the rehabilitation nursing equipment data according to the equipment type data in the rehabilitation nursing equipment data to obtain the rehabilitation nursing equipment grouping data;

Specifically, first, the rehabilitation nursing equipment data is sorted and grouped according to the equipment type data in the rehabilitation nursing equipment data. For example, classify the same type of rehabilitation nursing equipment data and put the same type of equipment data together to facilitate subsequent usage calculations.

Calculate the usage of the grouped data of rehabilitation nursing equipment to obtain usage data, where the usage data includes usage data, usage duration data and usage frequency data.

Specifically, the usage of rehabilitation and nursing equipment is calculated for grouped data. According to the needs, the number of times each type of equipment is used, the duration of use, and the frequency of use are calculated. For example, for the number of times each type of equipment is used within a period of time, the cumulative duration of use of each type of equipment is calculated; for the frequency of use, the number of times the number of times each type of equipment is used is calculated; for the frequency of use, the number of times ...

In the present invention, by performing data sorting and grouping processing according to the equipment types in the rehabilitation care equipment data, personalized data processing can be performed for different types of equipment, and the usage of each type of equipment can be understood more accurately. By calculating the usage of the grouped data of rehabilitation nursing equipment, we can obtain data such as the number of uses, duration of use, and frequency of use, which can accurately evaluate the use of rehabilitation nursing equipment, including information on frequency of use, duration, and other aspects. After understanding the usage of rehabilitation nursing equipment, we can better allocate and manage resources, ensure adequate supply of commonly used equipment, and improve the efficiency and quality of rehabilitation treatment. By monitoring the use of rehabilitation care equipment, equipment that is used more or more frequently can be discovered in time, so that priority can be given to maintaining and maintaining these equipment to ensure their normal operation and improve the quality and stability of rehabilitation services. Based on usage data, targeted equipment usage strategies can be formulated, including adjusting equipment usage time, strengthening maintenance of commonly used equipment, and other measures to maximize the effectiveness and value of equipment.

Preferably, step S2 is specifically:

Step S21: Generate equipment usage time window data based on rehabilitation care equipment data and equipment status update data;

Specifically, based on the rehabilitation care equipment data and equipment status update data, the equipment usage time window data is generated, the size of the time window is determined by setting the time period or according to specific needs, and then based on the equipment usage and status update data. Determine equipment usage within each time window. For example, divide a day into multiple time periods, count the usage of equipment in each time period, and obtain the usage data of each time window.

Step S22: Extract usage frequency features from the rehabilitation nursing equipment data based on the equipment usage time window data to obtain usage frequency feature data;

Specifically, based on the generated equipment usage time window data, the usage frequency features of the rehabilitation nursing equipment data are extracted. Count the number of uses of each equipment in different time windows, and calculate the average number of uses, maximum number of uses and other characteristics. These characteristics can reflect the frequency and activity of equipment and help understand the actual use of equipment.

Step S23: Extract load status features from the rehabilitation care equipment data according to the equipment status update data to obtain load status feature data.

Specifically, load status features are extracted from the rehabilitation care equipment data based on the equipment status update data. Based on the equipment's working status, load size and other information, the load status characteristics are extracted. For example, for sports rehabilitation equipment, the load state characteristics are extracted based on the equipment's movement speed, load size and other indicators; for pressure rehabilitation equipment, the load state characteristics are extracted based on the equipment's pressure size, use pressure range and other indicators.

Specifically, according to the equipment status update data, the load status characteristics of the treadmill are extracted, and the load size is calculated according to the equipment running speed, inclination angle, user weight and other information. For example, if the treadmill runs faster and the inclination is larger, and the user is heavier, then the load size is larger; if the treadmill runs slower and the inclination is smaller, and the user is lighter, then the load size is smaller.

Extract the load status characteristics of the rowing machine based on the equipment status update data. The load size is calculated based on information such as the rowing machine's stroke speed, resistance level, and user weight. For example, if a rowing machine has a fast stroke speed and high resistance, and the user weighs a lot, the load size will be larger. If a rowing machine has a slower stroke speed, less resistance, and the user weighs a lighter weight, the load size will be larger. Smaller in size.

For the pressure bed, the load state characteristics are extracted based on the pressure recorded in the equipment status update data and the pressure interval used. For example, if the operating pressure of the pressure bed is higher and the duration is longer, the load state is greater; and if the pressure bed is operating at lower pressure and the duration is shorter, the load state is smaller.

Extracting load status features of a treadmill, and analyzing the equipment status update data, it was found that the treadmill ran faster, had a larger inclination, and the user weighed more in the past week. Based on this information, the system extracts the load status feature of the treadmill as "high load".

Step S21 in the present invention generates equipment usage time window data based on rehabilitation nursing equipment data and equipment status update data, and divides the usage of rehabilitation nursing equipment into different time periods, making the analysis more targeted and accurate. By extracting usage frequency features based on the equipment usage time window data in step S22, the usage frequency of the rehabilitation nursing equipment in different time periods can be understood, and the utilization rate and demand of the equipment can be better evaluated. Step S23 extracts load status features from the rehabilitation nursing equipment data based on the equipment status update data, which helps to evaluate the load status of the rehabilitation nursing equipment, including the equipment's workload, maintenance requirements and other information, and provides accurate information for subsequent resource allocation and maintenance. data support. By extracting usage frequency features and load status features, the usage and working status of rehabilitation nursing equipment can be more accurately assessed, and can provide support for more accurate resource management decisions, including equipment deployment, maintenance, and updates. By understanding the frequency of use and load status of rehabilitation care equipment in a timely manner, we can reasonably arrange the use time and maintenance plan of the equipment to improve the efficiency and quality of rehabilitation treatment equipment.

Preferably, the equipment use time window data includes first equipment use time window data and second equipment use time window data. Step S21 is specifically:

Step S211: Generate a time window based on the equipment type data in the rehabilitation care equipment data to obtain primary time window data;

Specifically, primary time window data is generated based on the equipment type data in the rehabilitation nursing equipment data, and the time windows are divided by setting fixed time intervals, such as daily, weekly or monthly as a time window, and then the rehabilitation nursing equipment The data is classified according to time windows to obtain the usage of various equipment within each time window.

Step S212: Perform weighted calculation on the primary time window data according to the equipment status update data to obtain the first equipment usage time window data;

Specifically, a weighted calculation is performed on the primary time window data according to the primary time window data and the equipment status update data to obtain the first equipment usage time window data. For example, the equipment data in different time windows are weighted according to the maintenance requirements, frequency of use and other information in the equipment status update data to reflect the actual usage in different time windows.

In a certain time window, if the maintenance requirements of the treadmill are low and the frequency of use is high, a higher weight will be given to the treadmill data in that time window; while within the same time window, the maintenance requirements of the rowing machine are relatively high. If it is high and the frequency of use is low, then a lower weight will be given to the rowing machine data within this time window.

Step S213: performing rehabilitation nursing usage estimation according to the equipment type data in the rehabilitation nursing equipment data to obtain rehabilitation stage usage estimation data;

Specifically, the rehabilitation nursing usage is estimated based on the equipment type data in the rehabilitation nursing equipment data, and the usage in the rehabilitation stage is predicted by analyzing historical data, patient status, medical needs and other information. For example, based on the patient's rehabilitation plan and medical plan, estimate the frequency and duration of use of different equipment at different rehabilitation stages.

Step S214: Generate an event trigger window based on the estimated data used in the rehabilitation stage to obtain second equipment usage time window data.

Specifically, an event trigger window is generated according to the estimated use data of the rehabilitation stage, and the second equipment usage time window data is obtained, and the generation of the time window is triggered by setting specific events or conditions, for example, based on the patient's recovery progress, the doctor's Recommendations, changes in treatment plans, and other factors determine the event trigger window. Each time a trigger window is generated, a new time window is generated that is used to monitor and evaluate the use of the equipment under specific events or conditions.

Whenever a trigger condition is met, a new event trigger window is generated to monitor and evaluate the use of rehabilitation care equipment under specific events or conditions. For example, when a patient enters the intermediate rehabilitation stage from the primary rehabilitation stage, a new event trigger window will be generated to monitor the use of rehabilitation nursing equipment in the intermediate rehabilitation stage.

In the present invention, through step S21, the time window is generated according to the equipment type data in the rehabilitation nursing equipment data to obtain the primary time window data. The time window data can be customized according to different equipment types to better adapt to the use of different types of equipment. Condition. The weighted calculation in step S21 is a weighted calculation of the primary time window data based on the equipment status update data, which can more accurately reflect the actual usage of the equipment. Taking into account the usage frequency and load status in different time periods, the time window data is improved. accuracy and reliability. Estimating the use of rehabilitation equipment in the rehabilitation stage based on the data of rehabilitation care equipment, and obtaining the estimated use data in the rehabilitation stage, can help predict and plan the use of equipment according to the staged needs of rehabilitation treatment, and reduce the waste caused by improper configuration of rehabilitation treatment equipment. The event trigger window generation in step S21 can generate second equipment usage time window data based on the estimated usage data of the rehabilitation stage, triggering the use and preparation of equipment at specific events or stages, thereby improving the pertinence and efficiency of the use of rehabilitation treatment equipment. Through customized time window data and recovery stage usage estimates, we can better plan and optimize the resource allocation and use planning of rehabilitation nursing equipment, reasonably arrange the use time and maintenance plan of equipment according to actual needs and treatment stages, and improve resources. usage efficiency.

Preferably, step S22 is specifically:

Extract usage frequency features from the rehabilitation nursing equipment data based on the equipment usage time window data to obtain the first usage frequency feature data;

Specifically, the frequency of use features of the rehabilitation nursing equipment data are extracted based on the equipment usage time window data, and this is achieved by counting the number of uses of various equipment in each time window. For example, for each time window, count the number of times each equipment is used and use it as feature data to obtain the first frequency of use feature data, which reflects the frequency of use of the equipment in each time window.

Perform an average calculation on the first frequency of use feature data based on the equipment type data in the rehabilitation care equipment data to obtain the second frequency of use feature data;

Specifically, the first frequency of use characteristic data is averaged according to the equipment type data in the rehabilitation care equipment data, and is implemented by calculating the average of the frequency of use of the same type of equipment in all time windows. For example, for each type of equipment, average the frequency of use data in all time windows to obtain the average frequency of use of each type of equipment, and obtain the second frequency of use characteristic data, which reflects the average frequency of use of each type of equipment.

Perform moving average processing on the second frequency of use characteristic data according to the equipment usage time window data to obtain frequency of use characteristic data.

Specifically, moving average processing is performed on the second frequency of use characteristic data according to the equipment usage time window data, and is implemented by calculating the moving average of the usage frequency data in each time window. For example, the sliding window method is used to calculate the average value of usage frequency data in several adjacent time windows, and then these average values are used as new feature data to obtain a moving average of usage frequency feature data for smoothing and trend analysis. .

In the present invention, through step S22, the frequency of use feature extraction is performed on the rehabilitation nursing equipment data according to the equipment use time window data, and the first frequency of use feature data is obtained. The frequency of use of each equipment in a specific time period can be accurately understood, which provides the basis for subsequent Provide accurate data support for analysis and decision-making. In step S22, the average calculation of the first frequency of use feature data is performed based on the equipment type data in the rehabilitation care equipment data, which helps to take into account the differences between different types of equipment and obtain more objective and comprehensive usage frequency feature data. In step S22, moving average processing is performed on the second usage frequency characteristic data according to the equipment usage time window data, which can smooth the usage frequency characteristic data, reduce data volatility, and better reflect the usage trends and changing patterns of rehabilitation nursing equipment. By averaging and smoothing the usage frequency feature data, the stability and reliability of the data can be improved, errors and deviations caused by data fluctuations can be reduced, and a more reliable basis can be provided for subsequent analysis and decision-making. By accurately extracting and processing usage frequency feature data, we can better understand the actual usage and trends of equipment, help optimize resource allocation and usage planning, reasonably arrange equipment maintenance and updates, and improve resource utilization efficiency and the effect of rehabilitation treatment. .

Preferably, step S23 is specifically:

Step S231: Extract maintenance requirements from the equipment status update data to obtain maintenance requirement data, where the maintenance requirement data is one of maintenance-required data or maintenance-free data;

Specifically, the equipment status update data is analyzed and processed to determine whether the equipment needs maintenance, and the judgment is made by monitoring the equipment's working status, fault records, maintenance history and other information. If the system determines that the equipment is faulty, damaged (or similar problems have occurred in history) or has exceeded the maintenance cycle, it will be determined as data that requires maintenance; if the equipment is operating normally and within the maintenance cycle, it will be determined as data that does not require maintenance.

Step S232: When it is determined that the maintenance requirement data is maintenance data, perform load state feature extraction on the rehabilitation care equipment data according to the equipment type data in the rehabilitation care equipment data to obtain the first load state feature data;

Specifically, the type of equipment that needs maintenance is determined based on the equipment type data in the rehabilitation care equipment data. Then, load state features are extracted for these equipments. For example: For sports equipment type data (treadmills and other sports equipment): extract features such as motion trajectory, motion speed, and motion frequency. For pressure equipment type data (pressure equipment such as pressure beds): extract features such as pressure magnitude and pressure change rate. For temperature equipment type data (hot compress or cold compress device): extract features such as temperature changes and temperature fluctuations.

Step S233: When it is determined that the maintenance requirement data is maintenance-free data, perform load state feature extraction on the rehabilitation nursing equipment data to obtain the second load state feature data;

Specifically, rehabilitation and nursing equipment does not require maintenance, so the load state features are directly extracted from the rehabilitation and nursing equipment data, such as extracting the operating status, load size, load stability and other features of the equipment.

Wherein step S232 is specifically as follows:

When it is determined that the equipment type data in the rehabilitation nursing equipment data is sports equipment type data, motion trajectory feature extraction is performed on the rehabilitation nursing equipment data to obtain equipment motion feature data;

Specifically, based on the fact that the equipment type data in the rehabilitation care equipment data is sports equipment type data, motion trajectory feature extraction is performed, and the usage, motion trajectory, motion speed and other information of the equipment are analyzed to extract features. For example, for sports equipment such as treadmills, the user's movement speed, cadence, exercise duration and other characteristics are extracted.

When it is determined that the equipment type data in the rehabilitation and nursing equipment data is pressure equipment type data, motion trajectory feature extraction is performed on the rehabilitation and nursing equipment data to obtain equipment pressure feature data;

Specifically, according to the fact that the equipment type data in the rehabilitation care equipment data is pressure equipment type data, pressure features are extracted, and the features are extracted by monitoring the pressure, load, bearing capacity and other information of the equipment through sensors. For example, for equipment such as pressure plates, the user's pressure distribution, strength and other characteristics are extracted.

When it is determined that the equipment type data in the rehabilitation nursing equipment data is temperature equipment type data, motion trajectory feature extraction is performed on the rehabilitation nursing equipment data to obtain equipment temperature feature data.

Specifically, based on the fact that the equipment type data in the rehabilitation care equipment data is temperature equipment type data, temperature features are extracted, and the features are extracted by monitoring the temperature changes, heat dissipation and other information of the equipment through temperature sensors. For example, for equipment such as hot compresses, features such as temperature changes and heat distribution on the surface of the equipment are extracted.

In the present invention, through step S23, the corresponding load status feature extraction method is selected according to different situations of the maintenance demand data, which helps to accurately understand the load status of the rehabilitation nursing equipment, including the working characteristics and usage conditions of the equipment. In step S232, corresponding load state features are extracted based on the equipment type data in the rehabilitation care equipment data, including motion features, pressure features, temperature features, etc., to process different types of rehabilitation care equipment in a targeted manner, which improves the efficiency of data processing. Personalization and accuracy. By combining maintenance demand data with load status feature extraction, the correlation between equipment maintenance needs and load status can be better assessed, potential problems can be discovered in a timely manner and corresponding measures can be taken to deal with them. Extracting corresponding load status characteristics based on different equipment types can more accurately evaluate the working status and usage of equipment, help optimize equipment maintenance plans and resource allocation, and improve the efficiency and quality of equipment maintenance. By accurately extracting load status characteristics and correlating maintenance demand data, we can better understand the operation of rehabilitation nursing equipment, help optimize the planning and execution of rehabilitation nursing services, and improve the effect and quality of rehabilitation treatment.

Preferably, step S3 specifically includes:

Step S31: Obtain rehabilitation care equipment demand data;

Specifically, the demand data of rehabilitation nursing equipment is obtained through the software input interface or control, including the demand information of rehabilitation institutions, hospitals, doctors, and personal demand information of rehabilitation patients. The demand data covers information on the types, quantities, and usage frequency of rehabilitation nursing equipment.

Step S32: estimating the equipment lifespan based on the usage frequency characteristic data and the load state characteristic data to obtain equipment lifespan estimation data;

Specifically, the life span of rehabilitation nursing equipment is estimated based on the frequency of use characteristic data and load status characteristic data, and is carried out through machine learning models, statistical analysis and other methods. For example, historical data and monitoring data can be used to build predictive models to estimate the life of equipment. Based on the equipment's frequency of use, load status and other characteristics, and combined with the experience and knowledge of experts in the field, the evaluation model is designed to estimate the life span.

Collect historical data of rehabilitation nursing equipment, including frequency of use, load status, maintenance records, etc. Obtain monitoring data of rehabilitation nursing equipment from the data system of the rehabilitation institution, including real-time data such as frequency of use and load status. Integrate historical data and monitoring data, and extract usage frequency features and load status features as input features of the model. Select machine learning algorithms, such as regression models, decision trees, random forests, etc., to establish a prediction model for the life span of rehabilitation nursing equipment. Use historical data to train the model so that it can predict the life of rehabilitation nursing equipment based on frequency of use characteristics and load status characteristics. Using the established prediction model and inputting the real-time frequency of use and load status data of the rehabilitation care equipment, the lifespan of the equipment can be estimated.

Step S33: Dynamically label the rehabilitation nursing equipment data according to the equipment life estimation data to obtain the rehabilitation nursing equipment labeling data;

Specifically, the rehabilitation care equipment data is dynamically annotated based on the equipment life estimation data, and the equipment is annotated according to the life status of the equipment, for example, the equipment is divided into different statuses such as new equipment, normal use equipment, and aging equipment.

Specifically, labeling is performed based on the estimated life of the equipment, for example, the equipment is divided into different categories such as normal life expectancy, insufficient life expectancy, etc.

Step S34: Perform gap calculation based on the rehabilitation nursing equipment demand data and the rehabilitation nursing equipment marking data to obtain the rehabilitation nursing equipment gap data, which includes rehabilitation nursing equipment no gap data, rehabilitation nursing equipment positive gap data and rehabilitation nursing equipment negative gap data. The positive gap data of rehabilitation nursing equipment means that the rehabilitation nursing equipment marking data is less than the rehabilitation nursing equipment demand data, and the negative gap data of rehabilitation nursing equipment means that the rehabilitation nursing equipment marking data is greater than the rehabilitation nursing equipment demand data.

Specifically, the gap calculation is performed based on the demand data and labeled data of rehabilitation nursing equipment, and the gap situation of the rehabilitation nursing equipment is calculated by comparing the demand data and labeled data. For example, demand data is compared with annotated data to determine which equipment needs have not been met and which equipment is in excess, thereby calculating gap data, including the number and type of positive and negative gaps.

Compare demand data for rehabilitation care equipment with actual labeled data to identify gaps. For each type of rehabilitation care equipment, the difference between the actual labeled data and the demand data is calculated to determine the number of positive and negative gaps. If the actual annotated data is less than the demand data, it means there is a positive gap, and further equipment needs to be purchased or allocated to meet the demand; if the actual annotated data is greater than the demand data, it means there is a negative gap, and rehabilitation care equipment needs to be reallocated or reduced to improve resource utilization. efficiency. Summarize the gap situation of each type of rehabilitation nursing equipment and obtain the gap data of rehabilitation nursing equipment, including the number and type of positive and negative gaps. Based on the gap data, formulate corresponding allocation plans and procurement plans to ensure the timely supply and rational use of rehabilitation care equipment.

In the present invention, through the acquisition and annotation of the rehabilitation nursing equipment demand data in step S3, combined with the equipment life estimation data, the demand and supply relationship of the rehabilitation nursing equipment can be comprehensively assessed, which helps to understand the actual demand for the equipment and make corresponding decisions. Adjustment and planning to ensure the smooth progress of rehabilitation nursing services. Through step S32, the equipment life expectancy is estimated based on the frequency of use characteristic data and the load status characteristic data. The life span and usage status of the rehabilitation care equipment can be evaluated, maintenance plans and update strategies can be adjusted in a timely manner, the service life of the equipment can be extended, maintenance costs can be reduced, and resources can be increased. usage efficiency. The dynamic labeling in step S33 labels the rehabilitation care equipment based on the equipment life estimation data, which is helpful for dynamic monitoring and management of the use status of the equipment. For the gap calculation in step S34, based on the demand data and annotation data of rehabilitation nursing equipment, the gap situation of equipment can be calculated, including positive gap and negative gap, and the procurement and deployment plan of equipment can be adjusted in a timely manner to ensure adequate supply of rehabilitation nursing equipment and meet the requirements. Need for rehabilitation. Comprehensive consideration of equipment demand, life expectancy and gap situation can optimize the resource allocation and use planning of rehabilitation nursing equipment, ensure the rational use and maintenance of equipment, and improve the efficiency and quality of rehabilitation nursing services.

Preferably, step S33 is specifically:

Perform equipment usage load hierarchical mapping on the rehabilitation and nursing equipment data according to the rehabilitation and nursing equipment demand data to obtain equipment usage load hierarchical mapping data;

Specifically, according to the demand data of rehabilitation nursing equipment, the usage load levels of rehabilitation nursing equipment are mapped, and the rehabilitation nursing equipment is classified according to different levels of usage load, for example, into high load, medium load and low load levels. According to different load levels, each equipment is assigned a corresponding load level.

Based on the load parameters and demand data of rehabilitation nursing equipment, a load level division plan is formulated. The load level is divided according to load size, frequency of use, stability and other indicators, for example, it is divided into three levels: high load, medium load and low load. Each load level is specifically defined, for example: high load: equipment with high frequency of use, heavy load, and high stability requirements; medium load: equipment with moderate frequency of use, average load, and average stability requirements; low load: equipment with high frequency of use Equipment with low load, low load and low stability requirements.

Perform a weighted calculation on the equipment life estimate data based on the equipment usage load level mapping data to obtain the equipment life estimate weighted data;

Specifically, the equipment life estimation data is weighted according to the equipment usage load level mapping data, and the equipment life estimation data is weighted according to the importance and influence of different load levels. For example, high-load equipment will have a higher weight on its life estimate, while low-load equipment will have a lower weight.

There are three load levels: high load, medium load and low load, with corresponding weights of 0.4, 0.3 and 0.2 respectively. The life expectancy data of each type of equipment is weighted and calculated separately according to the load level to which it belongs. Suppose there is a piece of equipment with the following estimated life data: Life estimate under high load: 1,000 hours, Life estimate under medium load: 1,500 hours, Life estimate under low load: 2,000 hours. According to the above weights, perform a weighted calculation on the life estimate data: weighted life estimate under high load: 1000×0.4=400 hours, weighted life estimate under medium load: 1500×0.3=450 hours, low load The weighted life estimate under: 2000×0.2=400 hours. Therefore, the overall weighted life estimate of the equipment is 400 hours + 450 hours + 400 hours = 1250 hours.

Label the rehabilitation nursing equipment data based on the weighted data of the equipment life expectancy to obtain the rehabilitation nursing equipment labeling data.

Specifically, the rehabilitation nursing equipment data is annotated according to the weighted data of equipment life expectancy, and the equipment is divided into different annotation categories according to different thresholds of the weighted data, such as normal, insufficient life expectancy, excessive life expectancy, etc.

There is a batch of rehabilitation nursing equipment. According to the previous steps, the system has performed weighted calculations on their life expectancy and obtained weighted data. The system will now label rehabilitation care equipment based on these weighted data, classifying them into different labeling categories, such as normal, under-life estimate and over-life estimate. Assume that the weighted data are as follows: the weighted data of equipment A is 1100 hours, the weighted data of equipment B is 900 hours, and the weighted data of equipment C is 1300 hours. Then, these weighted data are classified and labeled according to the preset threshold. For example, set the following threshold: if the weighted data is greater than 1200 hours, mark the equipment as overestimated life. If the weighted data is less than 1000 hours, the equipment is marked as under-estimated. If the weighted data is between 1000 hours and 1200 hours, the equipment is marked as normal. Based on the threshold and weighted data set above, the following labeling is made: The weighted data of equipment A is 1100 hours, which is between 1000 hours and 1200 hours, so it is marked as normal. The weighted data of equipment B is 900 hours, which is less than 1000 hours, so it is marked as an insufficient life estimate. The weighted data of equipment C is 1300 hours, which is greater than 1200 hours, so it is marked as an excess life estimate.

In the present invention, by mapping the equipment usage load level according to the rehabilitation care equipment demand data, the usage and load level of each equipment can be more accurately assessed, making the estimation of equipment life more accurate and avoiding the need for all equipment use in traditional methods. The problem of equipment being treated equally leads to the waste problem caused by the rough use of equipment. The equipment life estimate data is weighted based on the equipment usage load level mapping data. The equipment life estimate can be personalized based on the importance and influence of different load levels, which can better reflect the actual use of the equipment. situation, improving the accuracy and reliability of life estimation. By annotating the rehabilitation care equipment data based on the weighted data of equipment life expectancy, you can clearly understand the life status and estimation of each equipment, providing more detailed information and helping to better formulate equipment deployment plans and maintenance. Strategies to improve the utilization efficiency and service quality of rehabilitation nursing equipment.

Preferably, this application also provides a rehabilitation nursing equipment data management platform for executing the rehabilitation nursing equipment data management method as described above. The rehabilitation nursing equipment data management platform includes:

The equipment status update module is used to collect rehabilitation nursing equipment data from different data sources, update the equipment status of the rehabilitation nursing equipment data, and obtain the equipment status update data;

The rehabilitation nursing equipment feature extraction module is used to extract usage frequency features and load status features from the rehabilitation nursing equipment data based on the equipment status update data to obtain usage frequency feature data and load status feature data;

The equipment gap calculation module is used to calculate the equipment gap based on the frequency of use characteristic data and the load status characteristic data based on the rehabilitation nursing equipment data, and obtain the rehabilitation nursing equipment gap data;

The equipment calling plan generation module is used to generate an equipment calling plan for the rehabilitation nursing equipment data according to the rehabilitation nursing equipment gap data, and obtain the rehabilitation nursing equipment calling plan data to perform rehabilitation nursing equipment calling operations.

The beneficial effect of the present invention is that by collecting data from different data sources and updating the equipment status, the status of the rehabilitation nursing equipment can be monitored in real time, allowing users to more effectively utilize existing equipment resources and avoid waste and idleness of resources. Through the extraction of frequency of use features and load status features, we can gain an in-depth understanding of the actual frequency of use and load status of the equipment, which helps to accurately evaluate the use of the equipment. The application of equipment gap calculation method can timely discover the supply and demand gap of rehabilitation nursing equipment, including the positive and negative gaps of equipment, which helps institutions to adjust the equipment procurement plan and deployment strategy in a timely manner to ensure the continuity and quality of the use of rehabilitation nursing equipment. Generating an equipment call plan based on the gap data of rehabilitation nursing equipment can more effectively plan the use and scheduling of equipment, so that equipment can be called on demand and avoid poor rehabilitation treatment effects or waste of resources caused by shortage or excess of equipment. Combining the above measures, the management and dispatch of rehabilitation nursing equipment can be optimized and the efficiency and quality of services can be improved. Through timely maintenance, updating and reasonable allocation of equipment resources.

Therefore, the embodiments should be regarded as illustrative and non-restrictive from any point of view, and the scope of the present invention is defined by the attached application documents rather than the above description, and it is therefore intended that those falling within the application documents All changes within the meaning and scope of equivalent elements are included in the present invention.

The above descriptions are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be practiced in other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A rehabilitation and care equipment data management method, which is characterized by comprising the following steps:

step S1: collecting rehabilitation and nursing equipment data from different data sources, and updating the equipment state of the rehabilitation and nursing equipment data to obtain equipment state updating data;

step S2: performing frequency of use feature extraction and load state feature extraction on the rehabilitation and nursing equipment data according to the equipment state updating data to obtain frequency of use feature data and load state feature data;

Step S3: performing equipment gap calculation on the using frequency characteristic data and the load state characteristic data according to the rehabilitation and nursing equipment data to obtain rehabilitation and nursing equipment gap data;

step S4: and performing equipment calling plan generation on the rehabilitation and nursing equipment data according to the rehabilitation and nursing equipment gap data to obtain the rehabilitation and nursing equipment calling plan data so as to perform rehabilitation and nursing equipment calling operation.

2. The method according to claim 1, wherein step S1 is specifically:

step S11: collecting rehabilitation and care equipment data from different data sources;

step S12: extracting equipment availability, maintenance requirement and service condition of the rehabilitation and nursing equipment data to obtain equipment availability data, maintenance requirement data and service condition data;

step S13: and updating the equipment state according to the equipment availability data, the maintenance demand data and the service condition data to obtain equipment state updating data.

3. The method according to claim 2, wherein the step of use case extraction in step S12 comprises the steps of:

according to the equipment type data in the rehabilitation nursing equipment data, carrying out data sorting and grouping processing on the rehabilitation nursing equipment data to obtain rehabilitation nursing equipment grouping data;

And carrying out use condition calculation on the grouped data of the rehabilitation nursing equipment to obtain use condition data, wherein the use condition data comprises use times data, use duration data and use frequency data.

4. The method according to claim 1, wherein step S2 is specifically:

step S21: generating equipment use time window data according to the rehabilitation nursing equipment data and the equipment state updating data;

step S22: performing frequency of use feature extraction on the rehabilitation and nursing equipment data according to the equipment use time window data to obtain frequency of use feature data;

step S23: and carrying out load state characteristic extraction on the rehabilitation and nursing equipment data according to the equipment state updating data to obtain load state characteristic data.

5. The method of claim 4, wherein the equipment usage time window data includes first equipment usage time window data and second equipment usage time window data, and step S21 is specifically:

generating a time window according to equipment type data in the rehabilitation and nursing equipment data to obtain primary time window data;

weighting calculation is carried out on the primary time window data according to the equipment state updating data to obtain first equipment using time window data;

Performing rehabilitation and nursing use prediction according to equipment type data in rehabilitation and nursing equipment data to obtain rehabilitation stage use prediction data;

and generating an event trigger window according to the estimated data used in the rehabilitation stage to obtain second equipment use time window data.

6. The method according to claim 4, wherein step S22 is specifically:

performing frequency of use feature extraction on the rehabilitation and nursing equipment data according to the equipment use time window data to obtain first frequency of use feature data;

carrying out average calculation on the first frequency of use characteristic data according to the equipment type data in the rehabilitation and nursing equipment data to obtain second frequency of use characteristic data;

and carrying out moving average line processing on the second using frequency characteristic data according to the equipment using time window data to obtain the using frequency characteristic data.

7. The method according to claim 4, wherein step S23 is specifically:

step S231: extracting maintenance requirements of the equipment state update data to obtain maintenance requirement data, wherein the maintenance requirement data is one of required maintenance data or unnecessary maintenance data;

step S232: when the maintenance requirement data is determined to be the data to be maintained, carrying out load state characteristic extraction on the rehabilitation and nursing equipment data according to the equipment type data in the rehabilitation and nursing equipment data to obtain first load state characteristic data;

Step S233: when the maintenance requirement data is determined to be maintenance-free data, carrying out load state feature extraction on the rehabilitation and nursing equipment data to obtain second load state feature data;

the step S232 specifically includes:

when the equipment type data in the rehabilitation nursing equipment data is determined to be sports equipment type data, performing motion trail feature extraction on the rehabilitation nursing equipment data to obtain equipment motion feature data;

when the equipment type data in the rehabilitation nursing equipment data is determined to be the pressure equipment type data, performing motion trail feature extraction on the rehabilitation nursing equipment data to obtain equipment pressure feature data;

and when the equipment type data in the rehabilitation nursing equipment data is determined to be the temperature equipment type data, performing motion trail feature extraction on the rehabilitation nursing equipment data to obtain equipment temperature feature data.

8. The method according to claim 1, wherein step S3 is specifically:

step S31: acquiring demand data of rehabilitation and nursing equipment;

step S32: estimating the service life of equipment according to the frequency characteristic data and the load state characteristic data to obtain estimated service life data of the equipment;

step S33: dynamically labeling the rehabilitation and nursing equipment data according to the equipment life estimated data to obtain rehabilitation and nursing equipment labeling data;

Step S34: carry out the breach calculation according to recovered care equipment demand data and recovered care equipment mark data, obtain recovered care equipment breach data, including recovered care equipment no breach data, recovered care equipment positive breach data and recovered care equipment negative breach data, recovered care equipment positive breach data is less than recovered care equipment demand data for recovered care equipment mark data, recovered care equipment negative breach data is greater than recovered care equipment demand data for recovered care equipment mark data.

9. The method according to claim 8, wherein step S33 is specifically:

performing equipment use load hierarchical mapping on the rehabilitation and nursing equipment data according to the rehabilitation and nursing equipment demand data to obtain equipment use load hierarchical mapping data;

weighting calculation is carried out on the equipment life estimated data according to the equipment use load hierarchical mapping data to obtain equipment life estimated weighted data;

labeling the rehabilitation and nursing equipment data according to the equipment life prediction weighting data to obtain rehabilitation and nursing equipment labeling data.

10. A rehabilitation and care equipment data management platform for performing the rehabilitation and care equipment data management method of claim 1, the rehabilitation and care equipment data management platform comprising:

The equipment state updating module is used for acquiring rehabilitation and nursing equipment data from different data sources, and updating the equipment state of the rehabilitation and nursing equipment data to obtain equipment state updating data;

the rehabilitation and nursing equipment characteristic extraction module is used for carrying out frequency of use characteristic extraction and load state characteristic extraction on the rehabilitation and nursing equipment data according to the equipment state updating data to obtain frequency of use characteristic data and load state characteristic data;

the equipment gap calculation module is used for calculating equipment gaps according to the frequency of use characteristic data and the load state characteristic data of the rehabilitation and nursing equipment data to obtain rehabilitation and nursing equipment gap data;

and the equipment calling plan generation module is used for generating equipment calling plans for the rehabilitation care equipment data according to the rehabilitation care equipment gap data to obtain the rehabilitation care equipment calling plan data so as to carry out rehabilitation care equipment calling operation.