CN113506201A - Intelligent old-age-care ecosystem mode method - Google Patents
Intelligent old-age-care ecosystem mode method Download PDFInfo
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
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Abstract
An intelligent endowment ecosystem mode method is implemented in a software form and runs on a cloud platform, a user designs a volunteer duration model, a frequency model, a risk model, an intelligent matching model and a comprehensive scoring model to evaluate the personal social public value and the professional endowment service organization level through browsing and accessing of a webpage, a mobile terminal application program and a WeChat applet, the public contribution of volunteers and the social public value of volunteer organizations can be accurately reflected through indexes and mathematical algorithms, the intelligent matching of the machine learning algorithm is adopted, and the method is applied to the fields of enterprise social public welfare value rating, volunteer social public welfare evaluation and authentication, smart city scientific and technological park management planning and community senior citizen public welfare management, solves the social problems caused by population aging and the problem of asymmetric volunteer service and social demand information, and simultaneously solves the problem of social shortage of a public welfare volunteer value evaluation and authentication method.
Description
Technical Field
The invention relates to an intelligent endowment ecosystem mode method, which solves the social problem brought by population aging, solves the problem that the demand of an endowment hospital is not symmetrical to the information of young volunteers, solves the problem that dining and body-building activities in a scientific and technological park of a smart city are difficult, solves the problems of social public welfare demand for recruitment of enterprises and difficult employment of college students, can accurately reflect the social public welfare value of volunteers, can accurately reflect the social public welfare value of volunteer organizations, and belongs to the technical field of computer software; the method is applied to the fields of enterprise social public welfare value rating, volunteer social public welfare evaluation and authentication, smart city science and technology park management planning and community senior citizen home public welfare management.
Background
The existing nursing home service management mode mainly takes living necessities supply and sanitary cleaning service as main parts, lacks psychological soothing to the old, but the old needs the most psychological soothing, and at present mainly solves the problems in a family accompanying mode, while the way of introducing a third party to provide volunteering service or professional service is mainly a payment mode, the technical method is simple information release, lacks an effective evaluation mode, and is difficult to match with suitable people for selection; some large-scale enterprises recruit and government officers screen the application persons by increasing community volunteer activity indexes, while the application persons as college students and young white-collar workers are willing to provide volunteer activities in the holidays, but the society lacks a technical way of public welfare activity information disclosure and evaluation authentication; meanwhile, an evaluation system of social public value of enterprise staff is lacked, and guidance to an employment unit is lacked.
Disclosure of Invention
In order to solve the social problems caused by population aging, the problem that volunteer service and social demand information are asymmetric, the social problem that the public is lack of public interest volunteer value evaluation and authentication methods, and the social problem that the public interest volunteer value evaluation aiming at enterprise employees is lack of methods, the invention designs an intelligent endowment ecosystem mode method, designs an intelligent endowment ecosystem platform as a bridge of a endowment hospital, volunteers, volunteer organizations and social institutions, the intelligent endowment ecosystem platform comprises a personal social public interest value evaluation model and a professional endowment service organization rating model, and intelligent matching is realized by adopting a machine learning algorithm, wherein:
personal social public value evaluation model
The individual social public benefit value evaluation model refers to evaluation indexes and algorithms aiming at individual natural people, namely individual social volunteer service values, service objects are a nursing home and an old community, and more specifically, the service objects are old people in the nursing home or the old community.
1. Index (I)
(1) Number of times (T)d)
In a certain time period (time period of time classification), a process of providing volunteering activities for a rest home or an old community is one time, service ways include field service and remote virtual service, and the number of a plurality of processes is the number of times. For example, assuming that one day (24 hours) is a set number classification time period, the number of days actually providing the volunteering process is the number. The nursing home or the old community consists of a subject and an object, wherein the subject is the old or the disabled, and the object is a house building, a living and service facility and a software and hardware facility.
(2) Duration of a single time (A)h)
In one volunteering activity, the duration of providing the volunteering service is a single duration. For example, the time classification period is one day, the service duration of a certain day is 3 hours, and the single duration of the day is 3 hours.
(3) Total time length (S)h)
The sum of all the single-time durations in all the times within a certain time period (total duration classification time period) is the total duration, for example, if one year is a set total duration classification time period, the total duration of one year is the sum of all the single-time durations in one year.
(4) Average single-time duration (E)h)
The total duration is divided by the number of times, i.e. the average single duration. Similarly, the total time length is obtained by multiplying the times by the average single time length, and the total time length is ShAverage single-pass duration of EhThe number of times is TdThen S ish＝Eh×Td。
(5) General categories of items
The types of items, i.e., the types of services offered in the volunteer activities, include chat interactions, multimedia lectures, entertainment shows, handcraft making interactions, labor (serving physical handicappers; infrastructure maintenance; other related physical labor).
(6) Service mode
The system comprises an offline service mode and an online service mode, wherein the offline service mode is a field service mode, and the online service mode is a remote virtual service mode provided by an internet technology.
(7) Single service item category (P)t)
The types of services provided in a single volunteer campaign, i.e., single service item categories, are further subdivided on the basis of total categories of items, with the number of single service item categories being N (N ═ 1, 2, 3, …, N).
(8) Single item duration (L)e)
The duration of a service provided for a certain item category in a single volunteer activity, i.e., the service duration of a single service item in a single volunteer activity, is referred to as a single item duration, and the sum of the durations of all single items in a single volunteer activity is a single duration.
(9) Average single service item duration
Refers to the value of the total duration of a service item in all single services divided by the number of times, i.e. the average duration of a service item in each volunteer activity.
(10) Total duration of service item
Refers to the total duration of a single service item in all volunteer activities within a certain period of time, i.e. the sum of the durations of all single service items of a single service item. Also referred to herein simply as the total duration of the project (L)T) That is, the sum of all single item durations of an item in all times within a certain time period (total duration classification time period) is the item total duration of the item, for example: a total item duration of item LTaThe accumulation (sum) of the single item durations of the a items in all times.
(11) Single service item evaluation (E)v)
The evaluation score of the service object based on the single service item comprises evaluation classification and score of a manager (or a related person of responsibility) of the nursing home or the old community and evaluation classification and score of a service subject (such as the old) of the nursing home or the old community. The evaluation classification is evaluation indexes including affinity, enthusiasm, professionalism, diligence and comprehensive literacy, and the evaluation score of each evaluation index is the highest five stars (corresponding to 20 points) and the lowest one star (corresponding to 4 points). Evaluation of a Single service object (G)T) Is the total evaluation score of five indexes, which is the sum of each index multiplied by a coefficient, namely GT＝kaGa+kbGb+kcGc+ kdGd+keGe，ka+kb+kc+kd+k e1. Except for the star level (selected number of stars)) In addition to the options, a text box is provided for the service object to input the evaluation content of the volunteer service. Service item rating of all (one or more) service objects of a single service item, i.e. single service item rating (E)v) Value is all service object ratings (G)T) The summed average. In a volunteer activity, each service item, i.e. each single service item category (P)t) And when a plurality of service objects exist, the primary evaluation result is the average value of the evaluation results of the plurality of service objects.
(12) Single evaluation (E)A)
The single rating, i.e. the average of the single service item ratings, is the sum of the ratings of all (single) service items in one volunteer campaign divided by the number of service items.
(13) Mean item evaluation (E)TE)
All single service item evaluation (E) of a certain item in all times in a certain time period (total duration classification time period)v) Divided by the number of times the service item is evaluated.
(14) Volunteer identification
The volunteer identification comprises an identification card or passport, a student card or a work card, and the individual identification not only serves as the verification and registration certificate of volunteer activities, but also supports participation in the scoring calculation of the model in a configuration coefficient mode. The volunteer identification comprises name, gender, birth year and month, place of family, native place, colleges and universities in reading or graduation, academic calendar and profession.
(15) Risks
Including health risks, customer complaint risks, legal risks, health risks including risks closely related to infectious diseases, risks related to volunteer's ability to bear volunteer workload, legal risks being illicit risks.
(16) Volunteer acceptance
The method is characterized in that the acceptance degree of volunteers for service objects and service projects is evaluated from five dimensions (secondary indexes) of the character, comprehensive literacy, communication, service difficulty and project content of old people in a nursing home, each secondary index is divided into five stars, each star is 4 points, the maximum value of each index is 20 points, and the total value of the five indexes is 100 points.
(17) Volunteer personality testing
The second-level indexes of the volunteer personality test comprise the personality type, interest and hobbies and behavior habits, and each second-level index comprises a plurality of third-level indexes.
(18) Senior citizen character testing in nursing home
The second-level indexes of the sexual character test of the old people in the nursing home comprise the character types, the interests and hobbies and the behavior habits, and each second-level index comprises a plurality of third-level indexes.
(19) Nursing home old man identity information
The identity information of the old in the nursing home comprises name, gender, birth year, month or age, family and mouth location, native place, colleges and universities, academic calendar and professions.
2. Model (Algorithm)
(1) Volunteer duration model
With total duration (S) in a certain period of timeh) As a core index, all single service item evaluations in the time period are combined (E)v) The sum of (a) and (b). When the single service item has N (N is 1, 2, 3, …, N) service objects, the single service item is evaluated (E)v) The values of (d) are mean values, i.e.:
when there are N (N ═ 1, 2, 3, …, N) services in a single volunteer campaign, then a single evaluation (E)A) Evaluating for all single service items (E)v) I.e.:
volunteer duration evaluation (D)L) The model is as follows:
DLis the evaluation value of the volunteer duration; k is the coefficient of a single evaluation, EviIs the value of the ith single service item rating, EAiIs the value of the ith single evaluation, AhiIs the ith single time duration. i is an angle mark, and i is more than or equal to 1 and less than or equal to N.
(2) Number of times model
DTdIs the number of evaluation values, k, EviAnd i is the same as the definition and value in the volunteer time length model in the step (1).
(3) Offline volunteer duration model
The total time length (S) in a certain time periodha) Duration of a single time (A)ha) Evaluation of single service item (E)va)
The method is classified into two types of offline and online, and the calculation mode is the same as the volunteer duration model in (1).
Evaluation of duration of offline volunteer (D)La) The model is as follows:
DLais the evaluation value of the volunteer duration; k is a coefficient of single service item evaluation, EvaiIs the value of the ith single service item rating, AhaiIs the ith single time duration. i is an angle mark, and i is more than or equal to 1 and less than or equal to N.
(4) Online volunteer duration model
On-line volunteer duration evaluation (D)Lb) The model is as follows:
DLbevaluation value of volunteer duration(ii) a k is a coefficient of single service item evaluation, EvbiIs the value of the ith single service item rating, AhbiIs the ith single time duration. i is an angle mark, and i is more than or equal to 1 and less than or equal to N.
(5) Online and offline mixed duration model
Evaluation of on-line and off-line mixing duration (D)Lab) The model is as follows:
δm、δnthe configuration coefficients of the offline duration and the online volunteer duration, respectively, deltam、δnMay be provided.
(6) Composite score model (D)G)
Based on all item categories (P) engaged in by the volunteert) Total duration of project (L)T) And respectively configuring corresponding coefficients, and finally calculating a comprehensive score by combining evaluation summation, namely:
DGis the value of the composite score; kptiIs the configuration coefficient of the ith item category, gamma is the coefficient of the average evaluation of the items, ETEiIs the value of the average evaluation of the ith item, LTiIs the total duration of the ith item, i is an angle mark, and i is more than or equal to 1 and less than or equal to N.
(7) Identity configuration model
The identity configuration model is based on the 6 models, and the identity identification of the volunteer is added with an index (15) as a configuration coefficient, for example: the comprehensive scoring model (6) increases the volunteer identity recognition configuration coefficient, the students are additionally divided into 10 points, and the working staff are not divided into additional points.
(8) Risk model
Risk model, i.e. risk value ═ legal risk + health risk + customer complaint risk. Assuming no risk is 0 and risk is 1, the risk can be further divided into multiple levels and differentiated by numerical size based on the subdivision index.
(9) Intelligent matching model for volunteers and aged people in nursing home
MD＝kPc×Pc+kPn×Pn+kGi×Gi+kHi×Hi+kBh×Bh+kSg×Sg+kAt×At+kCa×Ca +kCp×Cp+kAs×As
MDThe matching degree of the volunteers and the aged in the nursing home is shown; pcIs the degree of match of character type, kPcIs a character type configuration coefficient; pnIs the degree of native matching, kPnIs the native configuration coefficient; giIs the degree of matching of the graduates and colleges, kGiIs the configuration coefficient of the graduation colleges; hiIs the interest and preference matching degree, kHiIs an interest configuration coefficient; b ishIs the degree of matching of behavioral habits, kBhIs a behavioral habit configuration coefficient; sgIs the sex match, kSgIs a gender disposition coefficient; a. thetIs age-matching degree, kAtIs an age collocation factor; caDegree of matching of the study calendar, kCaIs the configuration coefficient of the academic calendar; cpIs the professional degree of matching, kCpIs a professional configuration coefficient; a. thesIs degree of familiarity, kAsIs the familiarity configuration factor.
The index is classified into character types (P) according to different configuration coefficient sizes and specific gravities (specific gravities in all index configuration coefficients) of the indexesc) Guide model, native place (P)n) Guide model, graduate colleges (G)i) Oriented models, hobbies (H)i) Oriented model, behavior habits (B)h) Guided model, gender (S)g) Guided model, age (A)t) Guiding model, studying calendar (C)a) Guided model, specialty (C)p) Guided model, familiarity (A)s) The guidance model is called an index guidance model when the arrangement coefficient (weight) of each model is greater than a set value and is greater than n (n > 1) times of any other arrangement coefficient.
(10) Intelligent matching model for volunteers and nursing homes
The volunteer can manually select the nursing home; in addition, design asylum for aged and volunteer intelligence matching model, promptly: and respectively training and learning the selection conditions of the rest homes of the volunteers in a certain area and the adjacent area by adopting a K neighbor model in a machine learning algorithm based on the historical data of the living position, the working position or the learning unit position input by the volunteers, and further predicting the rest homes which are most prone to be selected by the volunteers based on the real-time data of the living position, the working position or the learning unit position input by the volunteers.
Inputting: training data set
T＝{(x1，y1)，(x2，y2)，(x3，y3)，...，(xN，yN)}
Wherein x isiIs an example feature vector, yi＝{c1，c2，c3，...，ckIs a class of examples, i 1, 2, 3.., N;
and (3) outputting: example x belongs to class y.
The residence or working learning position of the volunteer is x, k volunteers nearest to x are found in the training set T according to the given distance (geographical position coordinate distance) measurement, and the neighborhood of x covering the k volunteers is marked as Nk(x) (ii) a In Nk(x) The category y of x is determined according to the majority voting classification decision rule, y is the area of the rest home or neighborhood (covering other neighboring rest homes) where the volunteer selects to provide the volunteer service, namely:
wherein I is an indicator function, i.e. when yi＝cjWhen I is 1, otherwise, I is 0.
According to the K-nearest neighbor algorithm, the most probable selection of the nursing home and the next probable selection of the nursing home of the volunteer in a certain position are predicted by training the corresponding relation (probability) of the location area of the living or working learning place of the learning volunteer and the nursing home or the adjacent range area of the selected volunteer service, for example: 6 volunteers, { x1、x2、x3、x4、x5、x6With a certain origin ox1Distance L ofpx≤LXAnd 6 volunteers all selected y1The rest of the aged house or 6 volunteers respectively select the rest of the aged house1、y2、y3And y is1、y2、y3To a certain origin oy1Distance L ofpy≤LYI.e. y1、y2、y3Respectively, the region qy1In the adjacent nursing home, volunteer x appears7When and x7And the origin ox1Is in a distance of Lpx≤LXThen x is predicted7The most inclined nursing home may be y1Or most inclined to qy1Regional rest homes.
The models (1), (2), (3), (4), (5), (6) and (7) all belong to the category of scoring models, (9) and (10) are matching models, and the scoring models, the matching models and (8) risk models are listed as three major models of the patent.
Based on the model, a social public welfare value evaluation model for hiring employees of a unit and a social public welfare value evaluation model for students in colleges and universities can be designed and calculated.
Second, professional endowment service organization rating model
The professional endowment service organization comprises professional service enterprises and social groups, wherein the professional service enterprises refer to registered companies and individual industrial and commercial enterprises which provide services for endowment homes or old communities to obtain profits, and the service contents comprise psychological consultation, medical care, appearance management, washing, dining and kitchen, environment cleaning and living; the social group encompasses conventional enterprises (non-professional service enterprises not making profits from the above-described services), institutions or units (government agencies, scientific institutions, colleges), group organizations to which institutions or units belong, and non-profit organizations.
1. Index (I)
(1) Qualification (Q)c)
The system covers the qualification certification and the employee health certification of the endowment service of professional service enterprises; for the social community, the qualifications include an organization code certificate or related identity information for the organizational unit.
(2) Single service item evaluation (E)v)
The method is the same as the evaluation of single service items in the personal social public value evaluation model.
(3) Mean item evaluation (E)TE)
The evaluation method is the same as the average evaluation of the items in the personal social public value evaluation model.
(4) Single evaluation (E)A)
The same as the single evaluation in the personal social equity value evaluation model.
(5) Average evaluation of History (E)E)
The average value of the evaluation sum of all the past service items of the professional endowment service organization is historical average evaluation.
(6) Number of times (T)d)
The number of times is the same as that in the personal social public value evaluation model.
(7) Duration of a single time (A)h)
The time length is the same as the single time length in the personal social public value evaluation model.
(8) Total time length (S)h)
The total time length is the same as that in the personal social public value evaluation model.
(9) Average single-time duration (E)h)
The average single-time duration is the same as that in the personal social public value evaluation model.
(10) General categories of items
The general types of the items for providing services by the professional endowment service organization comprise psychological consultation, medical care, appearance cleaning, washing clothes, dining and kitchen, environment cleaning and daily life.
(11) Service mode
The service mode is the same as that in the personal social public value evaluation model, and comprises an online mode and an offline mode.
(12) Single item duration (L)e)
The method is the same as the definition of the duration of a single project in the personal social public value evaluation model.
(13) Average single service item duration
The method is the same as the definition idea of the average single service item duration in the personal social public value evaluation model.
(14) Total length of project
The definition concept of the total duration of the service items in the personal social public value evaluation model is the same.
(15) Risks
The risk is mainly aimed at professional service enterprises and is based on legal risk and customer complaint risk of the professional service enterprises. Legal risks are judged based on judicial disputes and labor arbitration events, and customer complaint risks are judged based on complaints of service objects, namely, retirement homes, mass reports of media exposure and dispute events.
2. Scoring model (Algorithm)
(1) Comprehensive scoring model
DEIs the value of the composite score; kptiIs the configuration coefficient of the ith item category, gamma is the coefficient of the average evaluation of the items, ETEiIs the value of the average evaluation of the ith item, LTiIs the total duration of the ith item, i is an angle mark, and i is more than or equal to 1 and less than or equal to N.
(2) An online duration model; an offline duration model; online and offline mixed duration model
The online time length, the offline time length and the mixed time length model idea in the personal social public value evaluation model are the same.
(3) Duration and evaluation hybrid model
Wherein E isAAverage of all single service item evaluations, i.e.
DLEIs the duration and the rating mix score; eviIs the value of the ith single service item rating, EAiIs the value of the ith single evaluation, AhiIs the ith single time duration, km、knRespectively, are configuration coefficients. i is an angle mark, and i is more than or equal to 1 and less than or equal to N.
3. Matching model (Algorithm)
(1) Intelligent matching model for nursing home and volunteer organization
The intelligent matching model of the nursing home and the volunteer organization is an intelligent matching model of the volunteer organization and the nursing home, and the volunteer organization can manually select the nursing home; in addition, an intelligent matching model of the nursing home and the volunteer organization is designed, and the idea of the intelligent matching model is the same as that of the intelligent matching model of the volunteer and the nursing home in the personal social public value evaluation model.
(2) Intelligent matching model for volunteer tissue and aged people in nursing home
The idea is the same as the intelligent matching model of the volunteers and the old people in the nursing home in the personal social public value evaluation model.
4. Risk model (Algorithm)
The idea is the same as a risk model in the personal social public value evaluation model.
The personal social public value evaluation model and the professional endowment service organization rating model comprise indexes and a specific model, namely an algorithm, wherein the indexes are penetrated in the algorithm.
Drawings
FIG. 1 is a schematic diagram of an intelligent endowment ecosystem model method, wherein the numerical symbols explain:
1 intelligent old-age ecosystem platform
2 volunteers
3 volunteer tissue
4 asylum for aged
5 social organization
6 grading model
7 matching model
8 Risk model
Detailed Description
The intelligent old-age-care ecosystem mode method is implemented in a software mode, the software runs on a cloud platform, and a user browses and accesses through a webpage, a mobile application program (APP) and a WeChat applet.
The intelligent endowment ecosystem mode method software, namely an intelligent endowment ecosystem platform, provides identity identification information input, learning or working place position information input, endowment homes and old people browsing selection, receptivity evaluation input, client anonymous comprehensive evaluation browsing, character testing, endowment homes and old people basic information browsing services for volunteers; providing a nursing home basic information input, an old man identity information input, an old man character test input, a service evaluation input, volunteer or volunteer organization basic information browsing, volunteer or volunteer organization screening consent input and a volunteer or volunteer organization comprehensive evaluation information browsing service for nursing home managers and old men in the nursing home; providing qualification information input, basic information browsing of the rest homes and the old people, anonymous comprehensive evaluation browsing of clients and browsing and selecting services of the rest homes for the volunteer organization.
After the volunteers are registered, based on the geographical position information of the residence or the learning and working place input by the volunteers or based on the position sharing and positioning information of the volunteers APP, the system automatically calculates based on the intelligent matching model of the volunteers and the nursing home and pushes the result to the volunteers.
The system automatically calculates based on the intelligent matching model of the volunteer and the aged in the nursing home and pushes the matching degree analysis result to the volunteer and the aged user in the nursing home.
The system automatically evaluates the volunteers based on a comprehensive scoring model, a frequency model and a volunteer duration model, and is available for the nursing home (including managers and the old) and the volunteers to check; the system automatically calculates based on the risk model and presents the results to the retirement home for reference.
A manager of the intelligent old-age ecosystem platform scores and grades volunteers based on the personal social public value evaluation model (grading model score ladder), and issues a social public value grade certificate for the volunteers; similarly, based on a professional endowment service organization rating model, scoring the social institutions and grading (sum or product of score step of the scoring model and calculation result of the risk model); the manager of the intelligent endowment ecosystem platform establishes a cooperative relationship with social institutions (governments, universities, scientific research institutions, public institutions, enterprises and non-profit organizations), including approval of social public welfare value level certificates and support of related agreements; volunteers with social public value level certificates preferentially obtain the preferential registration of application units, preferentially enjoy welfare restaurant service established by cooperation of governments and enterprises, preferentially obtain talent apartments and economic applicable room service provided by governments, enjoy local government off-house credit policy support, and conditionally obtain the grade-increasing and scoring service of an education system. Meanwhile, the government sets a rating standard for the number of volunteers of the social institution, the total sum of the volunteer durations (the sum of the total durations each volunteer provides volunteer services) or the sum of the times (the sum of the times each volunteer provides volunteer services), below which a restriction measure will be taken and above which a reward criterion will be policy-supported. On the same principle, volunteer organizations will also be supported by the policies of social institutions.
As shown in fig. 1, the intelligent endowment ecosystem platform 1 serves as a bridge between a nursing home 4 and volunteers 2 and volunteers 3, and serves as a bridge between a social institution 5 and volunteers 2 and volunteers 3, and the intelligent endowment ecosystem platform 1 also indirectly serves as a bridge between the social institution 5 and the nursing home 4; the scoring model 6, the matching model 7 and the risk model 8 are included in the intelligent endowment ecosystem platform 1. The intelligent endowment ecosystem platform 1 establishes a connection relationship for a volunteer 2, a volunteer tissue 3 and an endowment home 4; the intelligent old-age ecosystem platform 1 authenticates and issues certificates for the volunteers 2 and the volunteer organizations 3, the volunteers 2 and the volunteer organizations 3 support policies of the social organization 5 by means of the certificates, and the social organization 5 provides power for the volunteers 2 and the volunteer organizations 3 and indirectly provides support for the old-age hospital 5.
Claims (4)
1. The intelligent endowment ecosystem mode method is characterized in that an intelligent endowment ecosystem platform is designed to serve as a bridge of an endowment hospital, volunteers, volunteer organizations and social institutions, the intelligent endowment ecosystem platform comprises a personal social public value evaluation model and a professional endowment service organization rating model, and intelligent matching is achieved by adopting a machine learning algorithm.
2. The method according to claim 1, wherein the personal social public benefit value evaluation model includes the index of times, single-time duration, total duration, average single-time duration, total category of projects, service mode, single service project category, single-project duration, average single service project duration, total duration of service projects, single service project evaluation, single evaluation, average evaluation of projects, volunteer identification, risk, volunteer acceptance, volunteer personality test, senior citizen personality test in the senior citizen care department, senior citizen identity information in the senior citizen care department; indexes of the professional endowment service organization rating model comprise qualification, single service item evaluation, item average evaluation, single evaluation, historical average evaluation, times, single time duration, total duration, average single time duration, total item category, service mode, single item duration, average single service item duration, total item duration and risk.
3. The method of claim 1, wherein the volunteer duration model in the personal social public value evaluation model is
The order model is
The offline volunteer duration model is
The on-line volunteer duration model is
The on-line and off-line mixed duration model is
The comprehensive scoring model is
The intelligent matching model of the volunteer and the aged in the nursing home is MD＝kPc×Pc+kPn×Pn+kGi×Gi+kHi×Hi+kBh×Bh+kSg×Sg+kAt×At+kCa×Ca+kCp×Cp+kAs×As(ii) a The risk model is risk value ═ legal risk + health risk + customer complaint risk; the intelligent matching model of the volunteer and the nursing home is as follows: respectively training and learning the selection conditions of the rest homes of the volunteers in a certain area and the adjacent area by adopting a K-neighbor model in a machine learning algorithm based on the historical data of the living position, the working position or the learning unit position input by the volunteers, predicting the rest homes most prone to be selected by the volunteers based on the real-time data of the living position, the working position or the learning unit position input by the volunteers, and predicting the rest homes most likely to be selected by the volunteers in a certain position and the rest homes likely to be selected by the volunteers according to the K-neighbor algorithm by training the corresponding relation between the living place or the working learning position area of the learning volunteers and the rest homes or the adjacent range areas for selecting volunteers to serve.
4. The system of claim 1, wherein the comprehensive scoring model in the professional endowment services organization rating model is
An online duration model; an offline duration model; the online and offline mixed duration model has the same idea as the online duration, the offline duration and the mixed duration model in the personal social public value evaluation model; the duration and evaluation mixed model is
The intelligent matching model of the nursing home and the volunteer organization has the same idea as the intelligent matching model of the nursing home and the volunteer in the personal social public value evaluation model; the idea of the intelligent matching model of the volunteer organization and the old people in the nursing home is the same as that of the intelligent matching model of the volunteer and the old people in the nursing home in the personal social public value evaluation model; and the idea of the risk model is the same as that of the risk model in the personal social public value evaluation model.
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