Nursing behavior supervision method and system based on smart pension

Abstract

The invention discloses a nursing behavior supervision method and system based on smart pension, and belongs to the technical field of smart pension, and the method comprises the steps: carrying out the unified numbering of behavior data for achieving a smart pension function, and generating a behavior data set; constructing a nursing worker database and an old man feedback evaluation database; based on the behavior data set, calculating a service level score of the nursing worker, and calculating a feedback evaluation score of the elderly to the nursing worker; calculating the comprehensive score of the nursing workers, calculating the comprehensive score difference between the nursing workers, and standardizing the comprehensive score difference into the similarity between the nursing workers; a similarity threshold value is preset, and nursing behavior supervision pairs of nursing workers are analyzed and constructed; according to the invention, the intelligent old-age nursing cloud database is combined with an old-age feedback evaluation mechanism, so that the nursing behavior can be monitored in all directions; through comprehensive score and similarity analysis, the service level of nursing workers can be accurately evaluated, and dynamic supervision and optimization are realized through intelligent threshold setting, so that the overall management efficiency and the service quality are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of smart elderly care technology, and specifically to a nursing behavior supervision method and system based on smart elderly care. Background Art

[0002] Smart elderly care technology has gradually become the focus of social attention; the development of smart elderly care technology aims to improve the quality of life and health management level of the elderly through advanced means such as information technology, the Internet of Things, and big data analysis; these technical applications cover multiple fields such as telemedicine, smart homes, health monitoring, and behavior tracking; among them, nursing behavior supervision, as an important part of smart elderly care, can evaluate and improve the work quality of nursing staff through real-time monitoring and data analysis; however, the existing nursing behavior supervision technology has problems such as single data processing, imperfect evaluation mechanism, and inability to achieve personalized services, which makes it difficult to meet the increasingly complex nursing needs; for example, traditional nursing supervision systems usually only rely on simple attendance records or basic interaction data between nurses and the elderly, and fail to fully capture and analyze complex behavioral data in the nursing process, resulting in insufficient accuracy in nursing quality evaluation.

[0003] The existing nursing behavior supervision technology still has obvious deficiencies in terms of intelligence and personalization; first, many systems lack in-depth analysis of nursing behavior data, and only stay at the data collection level, failing to effectively use these data for scientific evaluation and decision-making; second, the evaluation of nursing behavior usually fails to fully consider the comprehensive performance of nursing staff in terms of response speed and specific nursing operations, resulting in one-sided evaluation results and difficulty in fully reflecting the actual service level of nursing staff; in addition, the evaluation criteria for nursing behavior are relatively fixed and lack adaptability to different nursing scenarios and individual differences of the elderly, resulting in difficulty in achieving the expected supervision effect in practical applications; based on these problems, there is an urgent need for a smart elderly care nursing behavior supervision method that can comprehensively consider the multi-faceted performance of nursing staff and adapt to different scenarios and individual differences, so as to improve the quality and personalization of nursing services. Summary of the invention

[0004] The purpose of the present invention is to provide a nursing behavior supervision method and system based on smart elderly care to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A nursing behavior supervision method based on smart elderly care, the method comprises the following steps: constructing a smart elderly care cloud database, and uniformly numbering any behavior data that realizes the smart elderly care function to generate a behavior data set; constructing a caregiver database and an elderly feedback evaluation database; based on the behavior data set, obtaining the caregiver's response behavior data and nursing behavior data, which are recorded as a caregiver response behavior data set and a caregiver nursing behavior data set respectively; based on the caregiver response behavior data set and the caregiver nursing behavior data set, calculating the caregiver's service level score, and based on the elderly feedback evaluation database, calculating the elderly's feedback evaluation score for the caregiver; based on the caregiver's service level score and the elderly's feedback evaluation score for the caregiver, calculating the caregiver's comprehensive score, and calculating the comprehensive score difference between the caregivers; based on the comprehensive score difference, calculating the similarity between the caregivers; presetting a similarity threshold, analyzing and constructing the caregiver's nursing behavior supervision pair.

[0007] As a preferred solution of the nursing behavior supervision method based on smart elderly care described in the present invention, a smart elderly care cloud database is constructed, and the smart elderly care cloud database is used to collect behavior data generated when realizing the smart elderly care function. The smart elderly care function includes a bell function, a response function and a nursing function. The bell function realizes the elderly's bell-pressing behavior, the response function realizes the caregiver's response behavior after receiving the bell-pressing behavior, and the nursing function realizes the nursing behavior generated when the caregiver serves the elderly, and the nursing behavior includes serving the elderly's bathing behavior, dressing behavior, toileting behavior and eating behavior.

[0008] Any behavior data in the smart elderly care cloud database that realizes the smart elderly care function is uniformly numbered, and a behavior data set is generated, which is recorded as SBD={SBD a |a∈[1,A]}, where SBD a represents the ath behavior data for realizing the smart elderly care function, and A represents the total number of behavior data for realizing the smart elderly care function.

[0009] As a preferred solution of the nursing behavior supervision method based on smart elderly care described in the present invention, a nursing database HG = {HG h |h∈[1,H]}, where HG h represents the hth caregiver, H represents the total number of caregivers; construct an elderly feedback evaluation database, denoted as OFE = {OFE i (HG h )|i∈[1,I],h∈[1,H]}, where OFE i (HG h ) represents the elderly feedback evaluation data of the i-th elderly to the h-th caregiver, and I represents the total number of elderly feedback evaluation data; the elderly feedback evaluation refers to the elderly's evaluation of the care behavior of the caregiver.

[0010] Based on the behavior data set, the response behavior data and nursing behavior data of the care worker are obtained, which are respectively recorded as the care worker response behavior data set CB = {CB i (HG h )|i∈[1,I],h∈[1,H]} and the nursing behavior dataset CW={CW i (HG h )|i∈[1,I],h∈[1,H]}, where CB i (HG h ) represents the response behavior data of the h-th caregiver to the i-th elderly person, CW i (HG h ) represents the nursing behavior data of the h-th caregiver for the i-th elderly person.

[0011] As a preferred solution of the nursing behavior supervision method based on smart elderly care described in the present invention, based on the nursing worker response behavior dataset CB={CB i (HG h )|i∈[1,I],h∈[1,H]} and the nursing behavior dataset CW={CW i (HG h )|i∈[1,I],h∈[1,H]},calculate the care worker HG h The service level score is calculated as follows:

[0012]

[0013] Among them, α represents the preset response behavior coefficient, β represents the preset nursing behavior coefficient, SL h Indicates care worker HG h service level score.

[0014] In the present invention, the overall service level of the caregiver is evaluated by calculating and analyzing the two types of behavior data of the caregiver; in the formula, It reflects the performance of the nursing staff in terms of response. It reflects the nursing performance of the caregiver. The coefficients α and β represent the importance ratio of response behavior and nursing behavior in evaluating the nursing service level. Through this formula, the performance of the caregiver in response and nursing can be comprehensively considered.

[0015] Based on the elderly feedback evaluation database OFE={OFE i (HG h )|i∈[1,I],h∈[1,H]}, calculate the elderly's caregiver HG h The feedback evaluation score is calculated as follows:

[0016]

[0017] Among them, γ represents the preset elderly feedback evaluation coefficient, F h The elderly are concerned about the caregivers h Feedback evaluation score.

[0018] Based on the care worker HG h Service Level Score SL h And the elderly to the caregiver HG h Feedback rating score F h , Computing Nursing Worker HG h The comprehensive score is calculated as follows:

[0019] ZHP h =δ·SL h +ω·F h ;

[0020] Among them, δ and ω are the preset score coefficients, ZHP h Indicates care worker HG h The comprehensive score of .

[0021] Computing Nursing Assistant HG h and care worker HG h+1 The difference in comprehensive scores between ΔZHP h , the calculation formula is: ΔZHP h =|ZHP h -ZHP h+1 |, where ZHP h+1 Indicates care worker HG h+1 The comprehensive score of .

[0022] As a preferred solution of the nursing behavior supervision method based on smart elderly care described in the present invention, based on the nursing worker HG h and care worker HG h+1 The difference in comprehensive scores between ΔZHP h , the comprehensive score difference ΔZHP h Standardization for care workers HG h and care worker HG h+1 The similarity between them is calculated as: Among them, SD(HG h →HG h+1 ) means nurse HG h and care worker HG h+1 The similarity between .

[0023] In the present invention, the care worker HG h and care worker HG h+1 The difference in comprehensive scores between ΔZHP h Standardization for care workers HG hand care worker HG h+1 The similarity between SD(HG h →HG h+1 ), which takes the comprehensive score difference ΔZHP h Converted to a similarity value between 0 and 1, when the comprehensive score difference ΔZHP h The larger the similarity SD(HG h →HG h+1 ) is smaller; when the comprehensive score difference ΔZHP h The smaller the similarity SD(HG h →HG h+1 ) is larger.

[0024] Preset comprehensive score threshold τ, if the care worker HG h The overall score of ZHP h <τ, it means that the nursing behavior of the caregiver is poor; if the caregiver HG h The overall score of ZHP h >τ, it means that the nursing behavior of the caregiver is excellent.

[0025] The similarity threshold θ is preset based on the care worker HG h and care worker HG h+1 The similarity between SD(HG h →HG h+1 ), the comprehensive score ZHP h Caregiver HG whose comprehensive score is less than the threshold τ h , matched with care worker HG h The caregiver HG whose similarity is greater than the similarity threshold θ h+1 , build a supervision system for nursing behavior of nursing staff (HG h , H.G. h+1 ).

[0026] A nursing behavior supervision system based on smart elderly care, the system includes: a smart elderly care behavior data management module, a caregiver and elderly feedback database module, a caregiver service level evaluation module and a caregiver similarity analysis and supervision module.

[0027] The smart elderly care behavior data management module is used to build a smart elderly care cloud database, which is responsible for collecting and uniformly managing all behavior data generated in the smart elderly care system.

[0028] The caregiver and elderly feedback database module constructs a caregiver database and an elderly feedback evaluation database; collects and manages the personal information of caregivers and their behavioral data generated during the elderly care process, and stores the elderly's evaluation of the caregiver's services after receiving care.

[0029] The caregiver service level evaluation module is used to calculate and evaluate the caregiver's service level; by analyzing the caregiver's performance in response and care, and combining the elderly's feedback and evaluation of the caregiver, the caregiver's overall service level score is comprehensively calculated.

[0030] The caregiver similarity analysis and supervision module analyzes the differences in comprehensive scores between caregivers and standardizes them into the similarities between caregivers.

[0031] Furthermore, the smart elderly care behavior data management module also includes a behavior data collection unit and a behavior data numbering unit.

[0032] The behavior data collection unit constructs a smart elderly care cloud database, which is used to collect behavior data generated when realizing the smart elderly care functions. The smart elderly care functions include a bell function, a response function and a nursing function. The bell function realizes the elderly's bell-pressing behavior, the response function realizes the caregiver's response behavior after receiving the bell-pressing behavior, and the nursing function realizes the nursing behavior generated when the caregiver serves the elderly, and the nursing behavior includes serving the elderly's bathing behavior, dressing behavior, toileting behavior and eating behavior.

[0033] The behavior data numbering unit uniformly numbers any behavior data in the smart elderly care cloud database that implements the smart elderly care function, and generates a behavior data set, which is recorded as SBD={SBD a |a∈[1,A]}, where SBD a represents the ath behavior data for realizing the smart elderly care function, and A represents the total number of behavior data for realizing the smart elderly care function.

[0034] Furthermore, the caregiver and elderly feedback database module also includes an elderly feedback evaluation management unit and a caregiver information management unit.

[0035] The elderly feedback evaluation management unit constructs a caregiver database HG = {HG h |h∈[1,H]}, where HG h represents the hth caregiver, H represents the total number of caregivers; construct an elderly feedback evaluation database, denoted as oFE = {OFE i (HG h )|i∈[1,I],h∈[1,H]}, where OFE i (HG h ) represents the elderly feedback evaluation data of the i-th elderly to the h-th caregiver, and I represents the total number of elderly feedback evaluation data; the elderly feedback evaluation refers to the elderly's evaluation of the care behavior of the caregiver.

[0036] The care worker information management unit obtains the care worker's response behavior data and nursing behavior data based on the behavior data set, which are respectively recorded as the care worker response behavior data set CB = {CB i (HG h )|i∈[1,I],h∈[1,H]} and the nursing behavior dataset CW={CW i (HG h )|i∈[1,I],h∈[1,H]}, where CB i (HG h ) represents the response behavior data of the h-th caregiver to the i-th elderly person, CW i (HG h ) represents the nursing behavior data of the h-th caregiver for the i-th elderly person.

[0037] Furthermore, the nursing service level assessment module also includes a service level and feedback evaluation scoring unit and a comprehensive scoring unit.

[0038] The service level and feedback evaluation scoring unit is based on the care worker response behavior data set CB={CB i (HG h )|i∈[1,I],h∈[1,H]} and the nursing behavior dataset CW={CW i (HG h )|i∈[1,I],h∈[1,H]},calculate the care worker HG h OFE = {OFE i (HG h )|i∈[1,I],h∈[1,H]}, calculate the elderly's caregiver HG h Feedback evaluation score.

[0039] The comprehensive score unit is based on the care worker HG h Service Level Score SL h And the elderly to the caregiver HG h Feedback rating score F h , Computing Nursing Worker HG h Comprehensive score of the nursing staff; calculation of HG h and care worker HG h+1 The difference in comprehensive scores between ΔZHP h .

[0040] Furthermore, the nursing similarity analysis and supervision module also includes a similarity matching unit and a nursing behavior supervision unit.

[0041] The similarity matching unit, based on the care worker HG h and care worker HG h+1The difference in comprehensive scores between ΔZHP h , the comprehensive score difference ΔZHP h Standardization for care workers HG h and care worker HG h+1 The similarity between them is calculated as: Among them, SD(HG h →HG h+1 ) means nurse HG h and care worker HG h+1 The similarity between .

[0042] The nursing behavior supervision unit presets a comprehensive score threshold τ. If the nurse HG h The overall score of ZHP h <τ, it means that the nursing behavior of the caregiver is poor; if the caregiver HG h The overall score of ZHP h >τ, it means that the nursing behavior of the caregiver is excellent.

[0043] The similarity threshold θ is preset based on the care worker HG h and care worker HG h+1 The similarity between SD(HG h →HG h+1 ), the comprehensive score ZHP h Caregiver HG whose comprehensive score is less than the threshold τ h , matched with care worker HG h The caregiver HG whose similarity is greater than the similarity threshold θ h+1 , build a supervision system for nursing behavior of nursing staff (HG h , H.G. h+1 ).

[0044] Compared with the prior art, the beneficial effects achieved by the present invention are: through systematic data collection and multi-dimensional analysis, a comprehensive nursing service quality evaluation system is constructed. The smart elderly care cloud database is combined with the elderly feedback evaluation mechanism to achieve all-round monitoring of nursing behavior and ensure the integrity and objectivity of the data. The comprehensive score and similarity analysis not only enable the accurate evaluation of the caregiver's service level, but also realize dynamic supervision and optimization through intelligent threshold setting, thereby effectively improving the overall management efficiency and service quality, and ultimately significantly improving the quality of life and nursing experience of the elderly. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0046] in:

[0047] Figure 1 A flowchart of a nursing behavior supervision method based on smart elderly care according to an embodiment of the present invention;

[0048] Figure 2 It is a structural schematic diagram of a nursing behavior supervision system based on smart elderly care in the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] See also Figure 1-Figure 2 , the present invention provides a technical solution:

[0051] See also Figure 1 In the first embodiment of the present invention, a nursing behavior supervision method based on smart elderly care is provided, and the method comprises the following steps:

[0052] Step S1: Build a smart elderly care cloud database, and uniformly number any behavior data that realizes the smart elderly care function to generate a behavior data set.

[0053] A smart elderly care cloud database is constructed, and the smart elderly care cloud database is used to collect behavioral data generated when realizing smart elderly care functions. The smart elderly care functions include a bell function, a response function and a nursing function. The bell function realizes the elderly's bell-pressing behavior, the response function realizes the care worker's response behavior after receiving the bell-pressing behavior, and the nursing function realizes the nursing behavior generated when the care worker serves the elderly, and the nursing behavior includes serving the elderly's bathing behavior, dressing behavior, toileting behavior and eating behavior.

[0054] Any behavior data in the smart elderly care cloud database that realizes the smart elderly care function is uniformly numbered, and a behavior data set is generated, which is recorded as SBD={SBD a |a∈[1,A]}, where SBD a represents the ath behavior data for realizing the smart elderly care function, and A represents the total number of behavior data for realizing the smart elderly care function.

[0055] Step S2: Construct a caregiver database and an elderly feedback evaluation database; based on the behavior data set, obtain the caregiver's response behavior data and nursing behavior data, which are recorded as the caregiver response behavior data set and the caregiver nursing behavior data set, respectively.

[0056] Constructing the nursing staff database HG = {HG h |h∈[1,H]}, where HG h represents the hth caregiver, H represents the total number of caregivers; construct an elderly feedback evaluation database, denoted as OFE = {OFE i (HG h )|i∈[1,I],h∈[1,H]}, where OFE i (HG h ) represents the elderly feedback evaluation data of the i-th elderly to the h-th caregiver, and I represents the total number of elderly feedback evaluation data; the elderly feedback evaluation refers to the elderly's evaluation of the care behavior of the caregiver.

[0057] Based on the behavior data set, the response behavior data and nursing behavior data of the care worker are obtained, which are respectively recorded as the care worker response behavior data set CB = {CB i (HG h )|i∈[1,I],h∈[1,H]} and the nursing behavior dataset CW={CW i (HG h )|i∈[1,I],h∈[1,H]}, where CB i (HG h ) represents the response behavior data of the h-th caregiver to the i-th elderly person, CW i (HG h ) represents the nursing behavior data of the h-th caregiver for the i-th elderly person.

[0058] Step S3: Based on the caregiver response behavior dataset and the caregiver nursing behavior dataset, calculate the caregiver's service level score, and based on the elderly feedback evaluation database, calculate the elderly's feedback evaluation score for the caregiver; based on the caregiver's service level score and the elderly's feedback evaluation score for the caregiver, calculate the caregiver's comprehensive score, and calculate the difference in comprehensive scores between caregivers.

[0059] Based on the care worker response behavior dataset CB={CB i (HG h )|i∈[1,I],h∈[1,H]} and the nursing behavior dataset CW={CW i (HG h )|i∈[1,I],h∈[1,H]},calculate the care worker HG h The service level score is calculated as follows:

[0060]

[0061] Among them, α represents the preset response behavior coefficient, β represents the preset nursing behavior coefficient, SL h Indicates care worker HG h service level score.

[0062] In the present invention, the overall service level of the caregiver is evaluated by calculating and analyzing the two types of behavior data of the caregiver; in the formula, It reflects the performance of the nursing staff in terms of response. It reflects the nursing performance of the caregiver. The coefficients α and β represent the importance ratio of response behavior and nursing behavior in evaluating the nursing service level. Through this formula, the performance of the caregiver in response and nursing can be comprehensively considered.

[0063] For example, assuming that the coefficients α and β are 0.2 and 0.4 respectively, the total number of elderly feedback evaluation data I is 2, there are two caregivers, that is, H = 2, and the response behavior data CB of the first caregiver to the first elderly person is i (HG h ) is 5, the response behavior data CB of the first caregiver to the second elderly i (HG h ) is 6, the first caregiver's nursing behavior data CW for the first elderly person i (HG h ) is 4, the nursing behavior data CW of the first caregiver to the second elderly i (HG h ) is 6, and the formula is used to obtain the first care worker HG h Service Level Score SL h It is 0.2*0.5*(5+6)+0.4*0.5*(4+6)=1.1+2=3.1.

[0064] Based on the elderly feedback evaluation database OFE={OFE i (HG h )|i∈[1,I],h∈[1,H]}, calculate the elderly's caregiver HG h The feedback evaluation score is calculated as follows:

[0065]

[0066] Among them, γ represents the preset elderly feedback evaluation coefficient, F h The elderly are concerned about the caregivers h Feedback evaluation score;

[0067] For example, assuming the coefficient γ is 1, the first elderly person's feedback evaluation data on the first caregiver is OFE i (HGh ) is 6, the second elderly person's feedback evaluation data OFE on the first caregiver i (HG h ) is 4, and the feedback evaluation score F is obtained by substituting it into the formula h It is 1*0.5*(6+4)=5.

[0068] Based on the care worker HG h Service Level Score SL h And the elderly to the caregiver HG h Feedback rating score F h , Computing Nursing Worker HG h The comprehensive score is calculated as follows:

[0069] ZHP h =δ·SL h +ω·F h ;

[0070] Among them, δ and ω are the preset score coefficients, ZHP h Indicates care worker HG h The comprehensive score of .

[0071] For example, assuming that δ and ω are 0.4 and 0.6, we can substitute them into the formula to get the care worker HG h The overall score of ZHP h It is 0.4*3.1+0.6*5=1.24+3=4.24.

[0072] Computing Nursing Assistant HG h and care worker HG h+1 The difference in comprehensive scores between ΔZHP h , the calculation formula is: ΔZHP h =|ZHP h -ZHP h+1 |, where ZHP h+1 Indicates care worker HG h+1 The comprehensive score of .

[0073] For example, suppose that the care worker HG h+1 The comprehensive score is 4, then substitute it into the formula to get the comprehensive score difference ΔZHP h is 0.24.

[0074] Step S4: Based on the difference in comprehensive scores, calculate the similarity between caregivers; preset a similarity threshold, analyze and construct the nursing behavior supervision pairs of caregivers.

[0075] Based on the care worker HG h and care worker HG h+1 The difference in comprehensive scores between ΔZHP h , the comprehensive score difference ΔZHPh Standardization for care workers HG h and care worker HG h+1 The similarity between them is calculated as: Among them, SD(HG h →HG h+1 ) means nurse HG h and care worker HG h+1 The similarity between .

[0076] In the present invention, the care worker HG h and care worker HG h+1 The difference in comprehensive scores between ΔZHP h Standardization for care workers HG h and care worker HG h+1 The similarity between SD(HG h →HG h+1 ), which takes the comprehensive score difference ΔZHP h Converted to a similarity value between 0 and 1, when the comprehensive score difference ΔZHP h The larger the similarity SD(HG h →HG h+1 ) is smaller; when the comprehensive score difference ΔZHP h The smaller the similarity SD(HG h →HG h+1 ) is larger.

[0077] For example, based on the above calculation results, the similarity SD(HG h →HG h+1 ) is 0.8.

[0078] Preset comprehensive score threshold τ, if the care worker HG h The overall score of ZHP h <τ, it means that the nursing behavior of the caregiver is poor; if the caregiver HG h The overall score of ZHP h >τ, it means that the nursing behavior of the caregiver is excellent.

[0079] The similarity threshold θ is preset based on the care worker HG h and care worker HG h+1 The similarity between SD(HG h →HG h+1 ), the comprehensive score ZHP h Caregiver HG whose comprehensive score is less than the threshold τ h , matched with care worker HG h The caregiver HG whose similarity is greater than the similarity threshold θ h+1 , build a supervision system for nursing behavior of nursing staff (HG h , H.G.h+1 )

[0080] For example, assuming that the comprehensive score threshold τ is 5, then the care worker HG h The overall score of ZHP h is 4.24, which is less than the comprehensive score threshold τ, then the nursing behavior of the caregiver is poor; assuming that the similarity threshold θ is 0.5, then the similarity SD (HG h →HG h+1 ) is 0.8, which is greater than the similarity threshold θ, then the nursing behavior supervision pair (HG h , H.G. h+1 ).

[0081] See also Figure 2 In the second embodiment of the present invention, a nursing behavior supervision system based on smart elderly care is provided, which includes: a smart elderly care behavior data management module, a caregiver and elderly feedback database module, a caregiver service level evaluation module and a caregiver similarity analysis and supervision module.

[0082] The smart elderly care behavior data management module is used to build a smart elderly care cloud database, which is responsible for collecting and uniformly managing all behavior data generated in the smart elderly care system.

[0083] The caregiver and elderly feedback database module constructs a caregiver database and an elderly feedback evaluation database; collects and manages the personal information of caregivers and their behavioral data generated during the elderly care process, and stores the elderly's evaluation of the caregiver's services after receiving care.

[0084] The caregiver service level evaluation module is used to calculate and evaluate the caregiver's service level; by analyzing the caregiver's performance in response and care, and combining the elderly's feedback and evaluation of the caregiver, the caregiver's overall service level score is comprehensively calculated.

[0085] The caregiver similarity analysis and supervision module analyzes the differences in comprehensive scores between caregivers and standardizes them into the similarities between caregivers.

[0086] Furthermore, the smart elderly care behavior data management module also includes a behavior data collection unit and a behavior data numbering unit.

[0087] The behavior data collection unit constructs a smart elderly care cloud database, which is used to collect behavior data generated when realizing the smart elderly care functions. The smart elderly care functions include a bell function, a response function and a nursing function. The bell function realizes the elderly's bell-pressing behavior, the response function realizes the caregiver's response behavior after receiving the bell-pressing behavior, and the nursing function realizes the nursing behavior generated when the caregiver serves the elderly, and the nursing behavior includes serving the elderly's bathing behavior, dressing behavior, toileting behavior and eating behavior.

[0088] The behavior data numbering unit uniformly numbers any behavior data in the smart elderly care cloud database that implements the smart elderly care function, and generates a behavior data set, which is recorded as SBD={SBD a |a∈[1,A]}, where SBD a represents the ath behavior data for realizing the smart elderly care function, and A represents the total number of behavior data for realizing the smart elderly care function.

[0089] Furthermore, the caregiver and elderly feedback database module also includes an elderly feedback evaluation management unit and a caregiver information management unit.

[0090] The elderly feedback evaluation management unit constructs a caregiver database HG = {HG h |h∈[1,H]}, where HG h represents the hth caregiver, H represents the total number of caregivers; construct an elderly feedback evaluation database, denoted as OFE = {OFE i (HG h )|i∈[1,I],h∈[1,H]}, where OFE i (HG h ) represents the elderly feedback evaluation data of the i-th elderly to the h-th caregiver, and I represents the total number of elderly feedback evaluation data; the elderly feedback evaluation refers to the elderly's evaluation of the care behavior of the caregiver.

[0091] The care worker information management unit obtains the care worker's response behavior data and nursing behavior data based on the behavior data set, which are respectively recorded as the care worker response behavior data set CB = {CB i (HG h )|i∈[1,I],h∈[1,H]} and the nursing behavior dataset CW={CW i (HG h )|i∈[1,I],h∈[1,H]}, where CB i (HG h ) represents the response behavior data of the h-th caregiver to the i-th elderly person, CW i (HG h ) represents the nursing behavior data of the h-th caregiver for the i-th elderly person.

[0092] Furthermore, the nursing service level assessment module also includes a service level and feedback evaluation scoring unit and a comprehensive scoring unit.

[0093] The service level and feedback evaluation scoring unit is based on the care worker response behavior data set CB={CB i (HG h)|i∈[1,I],h∈[1,H]} and the nursing behavior dataset CW={CW i (HG h )|i∈[1,I],h∈[1,H]},calculate the care worker HG h OFE = {OFE i (HG h )|i∈[1,I],h∈[1,H]}, calculate the elderly's caregiver HG h Feedback evaluation score.

[0094] The comprehensive score unit is based on the care worker HG h Service Level Score SL h And the elderly to the caregiver HG h Feedback rating score F h , Computing Nursing Worker HG h Comprehensive score of the nursing staff; calculation of HG h and care worker HG h+1 The difference in comprehensive scores between .

[0095] Furthermore, the nursing similarity analysis and supervision module also includes a similarity matching unit and a nursing behavior supervision unit.

[0096] The similarity matching unit, based on the care worker HG h and care worker HG h+1 The difference in comprehensive scores between ΔZHP h , the comprehensive score difference ΔZHP h Standardization for care workers HG h and care worker HG h+1 The similarity between them is calculated as: Among them, SD(HG h →HG h+1 ) means nurse HG h and care worker HG h+1 The similarity between .

[0097] The nursing behavior supervision unit presets a comprehensive score threshold τ. If the nurse HG h The overall score of ZHP h <τ, it means that the nursing behavior of the caregiver is poor; if the caregiver HG h The overall score of ZHP h >τ, it means that the nursing behavior of the caregiver is excellent.

[0098] The similarity threshold θ is preset based on the care worker HG h and care worker HG h+1 The similarity between SD(HG h →HGh+1 ), the comprehensive score ZHP h Caregiver HG whose comprehensive score is less than the threshold τ h , matched with care worker HG h The caregiver HG whose similarity is greater than the similarity threshold θ h+1 , build a supervision system for nursing behavior of nursing staff (HG h , H.G. h+1 ).

[0099] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0100] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A nursing behavior supervision method based on smart elderly care, characterized in that: The method comprises the following steps: Step S1: construct a smart elderly care cloud database, and uniformly number the behavior data that realizes the smart elderly care function to generate a behavior data set; Step S2: construct a caregiver database and an elderly feedback evaluation database; based on the behavior data set, obtain the caregiver's response behavior data and nursing behavior data, which are recorded as the caregiver response behavior data set and the caregiver nursing behavior data set respectively; Step S3: Based on the caregiver response behavior data set and the caregiver nursing behavior data set, the caregiver's service level score is calculated, and based on the elderly feedback evaluation database, the elderly's feedback evaluation score for the caregiver is calculated; based on the caregiver's service level score and the elderly's feedback evaluation score for the caregiver, the caregiver's comprehensive score is calculated, and the comprehensive score difference between the caregivers is calculated; Step S4: Based on the difference in comprehensive scores, calculate the similarity between caregivers; preset a similarity threshold, analyze and construct the nursing behavior supervision pairs of caregivers.

2. According to claim 1, a nursing behavior supervision method based on smart elderly care is characterized in that: The step S1 comprises the following steps: Constructing a smart elderly care cloud database, the smart elderly care cloud database is used to collect behavior data generated when realizing smart elderly care functions, the smart elderly care functions include a bell-pressing function, a response function and a nursing function, the bell-pressing function realizes the elderly's bell-pressing behavior, the response function realizes the response behavior of the care worker after receiving the bell-pressing behavior, and the nursing function realizes the nursing behavior generated when the care worker serves the elderly, and the nursing behavior includes serving the elderly's bathing behavior, dressing behavior, toileting behavior and eating behavior; Any behavior data in the smart elderly care cloud database that realizes the smart elderly care function is uniformly numbered, and a behavior data set is generated, which is recorded as SBD={SBD a |a∈[1,A]} where SBD a represents the ath behavior data for realizing the smart elderly care function, and A represents the total number of behavior data for realizing the smart elderly care function.

3. According to claim 2, a nursing behavior supervision method based on smart elderly care is characterized in that: The step S2 comprises the following steps: Constructing the nursing staff database HG = {HG h |h∈[1,H]} where HG h represents the hth caregiver, H represents the total number of caregivers; construct an elderly feedback evaluation database, denoted as OFE = {OFE i (HG h )|i∈[1,I],h∈[1,H]}where, OFE i (HG h ) represents the elderly feedback evaluation data of the i-th elderly to the h-th caregiver, and I represents the total number of elderly feedback evaluation data; the elderly feedback evaluation refers to the elderly's evaluation of the care behavior of the caregiver; Based on the behavior data set, the response behavior data and nursing behavior data of the care worker are obtained, which are respectively recorded as the care worker response behavior data set CB = {CB i (HG h )|i∈[1,I],h∈[1,H]} and the nursing behavior dataset CW={CW i (HG h )|i∈[1,I],h∈[1,H]}where CB i (HG h ) represents the response behavior data of the h-th caregiver to the i-th elderly person, CW i (HG h ) represents the nursing behavior data of the h-th caregiver for the i-th elderly person.

4. According to claim 3, a nursing behavior supervision method based on smart elderly care is characterized in that: The step S3 comprises the following steps: Based on the care worker response behavior dataset CB={CB i (HG h )|i∈[1,I],h∈[1,H]} and the nursing behavior dataset CW={CW i (HG h )|i∈[1,I],h∈[1,H]} calculates the care worker HG h The service level score is calculated as follows: Among them, α represents the preset response behavior coefficient, β represents the preset nursing behavior coefficient, SL h Indicates care worker HG h service level score; Based on the elderly feedback evaluation database OFE={OFE i (HG h )|i∈[1,I],h∈[1,H]}, calculate the elderly's caregiver HG h The feedback evaluation score is calculated as follows: Among them, γ represents the preset elderly feedback evaluation coefficient, F h The elderly are concerned about the caregivers h Feedback evaluation score; Based on the care worker HG h Service Level Score SL h And the elderly to the caregiver HG h Feedback rating score F h , Computing Nursing Worker HG h The comprehensive score is calculated as follows: ZHP h =δ·SL h +ω·F h ; Among them, δ and ω are the preset score coefficients, ZHP h Indicates care worker HG h The comprehensive score of Computing Nursing Assistant HG h and care worker HG h+1 The difference in comprehensive scores between ΔZHP h , the calculation formula is: ΔZHP h =|ZHP h -ZHP h+1 |, where ZHP h+1 Indicates care worker HG h+1 The comprehensive score of .

5. According to claim 4, a nursing behavior supervision method based on smart elderly care is characterized in that: The step S4 comprises the following steps: Based on the care worker HG h and care worker HG h+1 The difference in comprehensive scores between ΔZHP h , the comprehensive score difference ΔZHP h Standardization for care workers HG h and care worker HG h+1 The similarity between them is calculated as: Among them, SD(HG h →HG h+1 ) means nurse HG h and care worker HG h+1 The similarity between Preset comprehensive score threshold τ, if the care worker HG h The overall score of ZHP h <τ, it means that the nursing behavior of the caregiver is poor; if the caregiver HG h The overall score of ZHP h >τ, it means the nursing behavior of the caregiver is excellent; The similarity threshold θ is preset based on the care worker HG h and care worker HG h+1 The similarity between SD(HG h →HG h+1 ), the comprehensive score ZHP h Caregiver HG whose comprehensive score is less than the threshold τ h , matched with care worker HG h The caregiver HG whose similarity is greater than the similarity threshold θ h+1 , build a supervision system for nursing behavior of nursing staff (HG h , H.G. h+1 ).

6. A nursing behavior supervision system based on smart elderly care, characterized in that: The system includes: smart elderly care behavior data management module, caregiver and elderly feedback database module, caregiver service level assessment module and caregiver similarity analysis and supervision module; The smart elderly care behavior data management module is used to build a smart elderly care cloud database, which is responsible for collecting and managing all behavior data generated in the smart elderly care system; The caregiver and elderly feedback database module builds a caregiver database and an elderly feedback evaluation database; collects and manages the personal information of caregivers and their behavioral data generated during the elderly care process, and stores the elderly's evaluation of caregiver services after receiving care; The care worker service level evaluation module is used to calculate and evaluate the care worker's service level; by analyzing the care worker's performance in response and care, and combining the elderly's feedback and evaluation of the care worker, the overall service level score of the care worker is comprehensively calculated; The caregiver similarity analysis and supervision module analyzes the differences in comprehensive scores between caregivers and standardizes them into the similarities between caregivers.

7. According to claim 6, a nursing behavior supervision system based on smart elderly care is characterized by: The smart elderly care behavior data management module also includes a behavior data collection unit and a behavior data numbering unit; The behavior data collection unit constructs a smart elderly care cloud database, and the smart elderly care cloud database is used to collect behavior data generated when realizing the smart elderly care function, and the smart elderly care function includes a bell function, a response function and a nursing function. The bell function realizes the elderly's bell-pressing behavior, the response function realizes the response behavior of the care worker after receiving the bell-pressing behavior, and the nursing function realizes the nursing behavior generated when the care worker serves the elderly, and the nursing behavior includes the elderly's bathing behavior, dressing behavior, toilet behavior and eating behavior; The behavior data numbering unit uniformly numbers any behavior data in the smart elderly care cloud database that implements the smart elderly care function, and generates a behavior data set, which is recorded as SBD={SBD a |a∈[1,A]}, where SBD a represents the ath behavior data for realizing the smart elderly care function, and A represents the total number of behavior data for realizing the smart elderly care function.

8. The nursing behavior supervision system based on smart elderly care according to claim 7 is characterized by: The caregiver and elderly feedback database module also includes an elderly feedback evaluation management unit and a caregiver information management unit; The elderly feedback evaluation management unit constructs a caregiver database HG = {HG h |h∈[1,H]}, where HG h represents the hth caregiver, H represents the total number of caregivers; construct an elderly feedback evaluation database, denoted as OFE = {OFE i (HG h )|i∈[1,I],h∈[1,H]}, where OFE i (HG h ) represents the elderly feedback evaluation data of the i-th elderly to the h-th caregiver, and I represents the total number of elderly feedback evaluation data; the elderly feedback evaluation refers to the elderly's evaluation of the care behavior of the caregiver; The care worker information management unit obtains the care worker's response behavior data and nursing behavior data based on the behavior data set, which are respectively recorded as the care worker response behavior data set CB = {CB i (HG h )|i∈[1,I],h∈[1,H]} and the nursing behavior dataset CW={CW i (HG h )|i∈[1,I],h∈[1,H]}, where CB i (HG h ) represents the response behavior data of the h-th caregiver to the i-th elderly person, CW i (HG h ) represents the nursing behavior data of the h-th caregiver for the i-th elderly person.

9. The nursing behavior supervision system based on smart elderly care according to claim 8 is characterized by: The nursing service level assessment module also includes a service level and feedback evaluation scoring unit and a comprehensive scoring unit; The service level and feedback evaluation scoring unit is based on the care worker response behavior data set CB={CB i (HG h )|i∈[1,I],h∈[1,H]} and the nursing behavior dataset CW={CW i (HG h )|i∈[1,I],h∈[1,H]},calculate the care worker HG h OFE = {OFE i (HG h )|i∈[1,I],h∈[1,H]}, calculate the elderly's caregiver HG h Feedback evaluation score; The comprehensive score unit is based on the care worker HG h Service Level Score SL h And the elderly to the caregiver HG h Feedback rating score F h , Computing Nursing Worker HG h Comprehensive score of the nursing staff; calculation of HG h and care worker HG h+1 The difference in comprehensive scores between ΔZHP h .

10. A nursing behavior supervision system based on smart elderly care according to claim 9, characterized in that: The nursing similarity analysis and supervision module also includes a similarity matching unit and a nursing behavior supervision unit; The similarity matching unit, based on the care worker HG h and care worker HG h+1 The difference in comprehensive scores between ΔZHP h , the comprehensive score difference ΔZHP h Standardization for care workers HG h and care worker HG h+1 The similarity between them is calculated as: Among them, SD(HG h →HG h+1 ) means nurse HG h and care worker HG h+1 The similarity between The nursing behavior supervision unit presets a comprehensive score threshold τ. If the nurse HG h The overall score of ZHP h <τ, it means that the nursing behavior of the caregiver is poor; if the caregiver HG h The overall score of ZHP h >τ, it means the nursing behavior of the caregiver is excellent; The similarity threshold θ is preset based on the care worker HG h和护工 HG h+1 The similarity between SD(HG h →HG h+1 ), the comprehensive score ZHP h Caregiver HG whose comprehensive score is less than the threshold τ h , matched with care worker HG h The caregiver HG whose similarity is greater than the similarity threshold θ h+1 , build a supervision system for nursing behavior of nursing staff (HG h , H.G. h+1 ).

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