CN-121983224-A - Personal and environment matching system and method for managing social separation of old people

CN121983224ACN 121983224 ACN121983224 ACN 121983224ACN-121983224-A

Abstract

The invention belongs to the technical field of pension services, and relates to a personal and environment matching system and method for treating the social separation of the elderly, wherein the system comprises a data acquisition and integration module, a matching consistency evaluation module, a matching mode recommendation module and a process feedback regulation and control module, wherein the multi-dimensional longitudinal data is integrated through a data merging analysis technology, the matching degree and the treatment dilemma are accurately identified through a UTADIS multi-criterion classification method, the personalized matching mode is recommended through a fuzzy multi-attribute group decision and a TOPSIS method, and the dynamic feedback regulation and control is realized through a mixed time-varying effect model and an SOC measurement scheme.

Inventors

LI WEITONG
XU GUIHUA
BAI YAMEI
YAN SHUXIA
TU WENJING
SONG YULEI
WANG QIUQIN
CHEN JUNYU
ZHAO YAYI

Assignees

南京中医药大学

Dates

Publication Date: 20260505
Application Date: 20260209

Claims (10)

1. A personal and environmental matching system for managing social abscission in elderly people, comprising: the data acquisition and integration module acquires personal resource and environment resource element data of the old, integrates long-term longitudinal panel data and intensive longitudinal data through a dynamic panel model, a hybrid negative two-term model and a multi-level model, and forms a personal and environment resource matching data set; The matching consistency evaluation module adopts an evaluation model constructed by UTADIS multi-criterion classification method, takes personal resource and environment resource element data as input, and obtains the matching degree of individuals and environment and the social separation treatment dilemma type by calculating index node values, estimating marginal utility values, solving total utility values and generating utility estimation errors; The matching mode recommendation module is used for selecting an alternative matching mode by taking consistency matching, demand and supply matching and demand and capacity matching as targets based on the personal and environment resource matching data set through an expert conference, constructing a personal and environment matching strategy library, determining an evaluation index weight by adopting a fuzzy multi-attribute group decision method and combining a hierarchical analysis method of a fuzzy language preference relation matrix, and sequencing the alternative matching modes in the personal and environment matching strategy library through a TOPSIS method so as to obtain an optimal personalized intervention matching mode; And the process feedback regulation and control module evaluates the use preference of the elderly to the matching mode through an SOC measurement method, captures the time-varying characteristics of the interaction of individuals and the environment by adopting a mixed time-varying effect model, and predicts the failure risk of the matching mode by combining the output sensitivity coefficient with the self-adaptive regulation and control algorithm so as to realize the dynamic balance of the personal-environment matching.
2. The personal and environmental matching system for managing senior citizen social separation of claim 1 wherein the personal resource element data comprises social network density, daily range of activity, frequency of child visits, career confidence, and the environmental resource element data comprises social reachability, activity content diversity, participation persistence, unobstructed, availability and satisfaction.
3. The personal and environmental matching system for managing senior citizen social separation of claim 2 wherein the long term longitudinal panel data is integrated using a dynamic panel model, a hybrid negative two term model, a group growth model or a growth hybrid model, wherein the long term longitudinal panel data comprises: The personal resource element data comprises personal elements including gender, age, marital status, educational level, primary work type, primary economic source, primary residence, co-illness condition, social participation willingness; The environment resource element data comprises household elements, mechanism elements, social elements and government elements, wherein the household elements comprise household intimate relations, household support and household residence areas, the mechanism elements comprise mechanism geographic accessibility, mechanism management system, career qualification, education degree of the same resident, original work type of the same resident, mechanism attention degree and matched facilities, the social elements comprise social support and social capital, and the government elements comprise supervision force, policy and regulation, propaganda guidance, resource utilization, manpower guarantee and public service; the intensive longitudinal data are integrated by adopting a multi-level model, the data sampling period is T0-T3 four-period tracking investigation, and ecological instantaneous evaluation data are synchronously integrated, wherein the intensive longitudinal data comprise: the personal resource element data comprises personal elements, namely the number of steps per day, the medication condition, the daily life capacity, psychological characteristics, cognitive functions and the radius of the daily activity range; The environment resource element data comprises household elements and mechanism elements, wherein the household elements comprise video frequency, video time and video time, and the mechanism elements comprise carer intimate relationship and resident daily life capability.
4. The personal and environment matching system for managing aged social abscission of claim 3, wherein the construction method of the evaluation model comprises the following steps: s41, segmenting the data interval of each evaluation index, and calculating the evaluation index value of each node; s42, estimating a marginal utility value of each node through linear interpolation, and meeting marginal utility monotonicity constraint; S43, summarizing marginal utility of each index, calculating a total utility value, and dividing the matching degree and the social separation treatment dilemma type by comparing with a utility threshold; S44, introducing an overestimate error and an underestimate error, minimizing the error sum under a constraint condition, and performing utility normalization processing.
5. A personal and environmental matching system for managing aged social withdrawal according to claim 4 wherein said degree of matching comprises five degrees of matching, full match, exact match, partial match, uncertain match and mismatch.
6. A person and environment matching system for managing senior citizens social distraction of claim 5 wherein the social distraction management dilemma types include capability and demand imbalance dilemma type, demand and supply misplacement dilemma type and double system detuning dilemma type.
7. The personal and environment matching system for managing senior citizens' social separation of claim 6 wherein the specific implementation of the matching pattern recommending module function comprises the steps of: S71, calculating fuzzy weights of the matching mode evaluation indexes based on a fuzzy language preference relation analytic hierarchy process; S72, determining performance values of mixed indexes of alternative matching modes, describing qualitative indexes by adopting linguistic variables and corresponding to triangular fuzzy functions, and scoring quantitative indexes by adopting experts to obtain the performance values; S73, carrying out standardization and weighting treatment on the group decision matrix, and calculating the relative closeness between the alternative matching mode and the ideal solution by using a TOPSIS method; S74, constructing a group decision weighting matrix, calculating the relative closeness of each alternative matching mode and the ideal solution of the group decision, sequencing according to the numerical value, and outputting the best mode.
8. The personal and environment matching system for managing the aged social separation of claim 7 wherein the mixed time-varying effect model is characterized in that individual characteristics and environmental factors are respectively used as a fixed effect and a random effect, regression coefficients are time-varying parameters, the magnitude of a penalty function is selected through a Bayesian information criterion, and standard errors of estimated coefficients are calculated through a mixed regression information method.
9. The personal and environment matching system for managing the social separation of the elderly according to claim 8, wherein the process feedback regulation and control module automatically matches an optimal regulation and control strategy through a sensitivity coefficient and an adaptive regulation algorithm output by a mixed time-varying effect model, and predicts the failure risk of a matching mode.
10. A personal and environment matching method for treating the social separation of the old is characterized by comprising the following steps: s101, acquiring personal resource and environment resource element data of the old, and integrating long-term longitudinal panel data and intensive longitudinal data through a dynamic panel model, a hybrid negative two-term model and a multi-level model to form a personal and environment resource matching data set; S102, an evaluation model constructed by adopting UTADIS multi-criterion classification method takes personal resource and environment resource element data as input, and obtains the matching degree of the personal and the environment and the social separation treatment dilemma type by calculating index node values, estimating marginal utility values, solving total utility values and generating utility estimation errors; S103, based on the personal and environment resource matching data set, taking consistency matching, demand-supply matching and demand-capacity matching as targets, selecting an alternative matching mode through an expert conference, constructing a personal and environment matching strategy library, determining an evaluation index weight by adopting a fuzzy multi-attribute group decision method and combining a hierarchical analysis method of a fuzzy language preference relation matrix, sequencing the alternative matching modes in the personal and environment matching strategy library through a TOPSIS method, and outputting an optimal personalized intervention matching mode; s104, evaluating the use preference of the elderly to the matching mode based on the SOC measurement scheme, capturing the time-varying characteristics of the interaction of the individual and the environment by adopting a mixed time-varying effect model, and predicting the failure risk of the matching mode by combining the sensitivity coefficient output by the mixed time-varying effect model with an adaptive regulation algorithm so as to realize the dynamic balance of the matching of the individual and the environment elements.

Description

Personal and environment matching system and method for managing social separation of old people Technical Field The invention belongs to the technical field of pension services, and particularly relates to a personal and environment matching system and method for treating social separation of old people. Background With the aging of population, the social separation problem of the aged-care institutions is increasingly prominent, and the social separation problem is mainly represented by emotion separation, capacity separation, space separation, interpersonal separation, event separation and the like, and the management dilemma such as capacity-requirement unbalance, demand-supply dislocation, double-system detuning and the like is derived, so that the physical and mental health and the quality of life of the aged-care institutions are seriously affected. In the prior art, the related pension service model focuses on demand assessment or resource allocation in a single dimension, and has the obvious defects that firstly, the matching assessment is static, for example, a fixed threshold value and a static model are adopted for matching assessment in a patent CN115438506A, the characteristic of the dynamic evolution of the social separation of the elderly along with time cannot be adapted, and the long-term longitudinal panel data and the dense longitudinal data are more difficult to integrate to form a dynamic matching data set, so that the assessment result lags behind the actual demand change; secondly, the recommendation weight is immobilized, the recommendation mode is recommended to adopt fixed weight settings, such as a patent CN120148804A, dynamic situations of service resource shortage degree, human resource allocation situation, sudden changes of health conditions and the like of old people in institutions are not considered, a multi-dimensional matching strategy library is not built, flexible weight determining methods such as fuzzy multi-attribute group decision and the like are not adopted, the recommendation result cannot accurately agree with core targets of consistency matching, demand-supply matching and demand-capacity matching, the recommendation accuracy is insufficient, thirdly, privacy and sharing are contradictory, an effective privacy protection mechanism is lacked in the process of sharing data across institutions, such as a patent CN118035870B, multi-mechanism data integration is difficult to be realized on the premise of guaranteeing data safety, multi-mechanism cooperation is difficult to be realized for constructing a personal and environment resource matching data set of the old people in the process of data security, fourth, feedback control is lag is only dependent on single-dimensional data, a non-failure risk pre-judging function such as a patent CN118761885B is not adopted, the old people use preference is not adopted, and the characteristics of the method is always changed in a five-time mode to be changed in a time delay mode of interaction and the environment is not always changed in the process of capturing the model 48, the prior art can only passively cope with the current management dilemma, does not relate to social separation evolution track prediction, cannot capture the evolution law of social separation through a dynamic model, is difficult to realize prospective intervention, and cannot radically relieve the core management dilemma such as capability-requirement unbalance, demand-supply dislocation and the like. Therefore, it is highly desirable to construct a personal-environment dynamic matching model with dynamic evaluation, accurate matching, rapid feedback regulation and look-ahead prediction, which solves many of the shortcomings of the prior art and provides reliable technical support for the social separation management of the aged in the senior citizen institution. Disclosure of Invention The invention aims to overcome the defects in the prior art, and provides a personal and environment matching system and method for treating the social separation of the aged, so as to solve the problem of the social separation treatment of the aged in the aged care institutions. In order to achieve the purpose of the invention, the invention is implemented by adopting the following technical scheme. A personal and environmental matching system for managing social abscission of elderly people, comprising: The data acquisition and integration module acquires personal resource and environment resource element data of old people of the pension institution, integrates long-term longitudinal panel data and intensive longitudinal data through a dynamic panel model, a hybrid negative two-term model and a multi-level model, and forms a personal and environment resource matching data set; The matching consistency evaluation module adopts an evaluation model constructed by UTADIS multi-criterion classification method, takes personal resource and environment resource element dat