CN115394394A - Resident health service reservation method and system based on big data processing technology - Google Patents

Resident health service reservation method and system based on big data processing technology Download PDF

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CN115394394A
CN115394394A CN202211321778.3A CN202211321778A CN115394394A CN 115394394 A CN115394394 A CN 115394394A CN 202211321778 A CN202211321778 A CN 202211321778A CN 115394394 A CN115394394 A CN 115394394A
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service
target
health service
demand
user
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CN115394394B (en
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孔庆来
刘宁
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Caoxian People's Hospital
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Caoxian People's Hospital
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/02—Reservations, e.g. for tickets, services or events
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms

Abstract

The invention relates to the technical field of service reservation, and particularly discloses a resident health service reservation method and system based on big data processing technology, wherein the method comprises the following steps: acquiring user information of a target user needing to reserve the target health service as a target user matching target service mechanism; reserving the target health service for a target user. According to the method and the system, the service quality of the service project is quantified by using the demand indexes of the service project, the demand value of the target health service is calculated according to the service project and the demand indexes, so that the user demand is quantified, the target health service project and the service mechanism which are suitable for the user demand are reserved for the user, the user can visually know about the service mechanism, the service mechanism can meet the user demand, and the technical problem that the health service is difficult to reserve is solved.

Description

Resident health service reservation method and system based on big data processing technology
Technical Field
The invention particularly relates to the technical field of service reservation, in particular to a resident health service reservation method and system based on big data processing technology.
Background
With the development of the times, the social progress and the continuous improvement of the living standard of people, the health becomes a topic which people pay more and more attention to, and the health consciousness of the masses is also generally enhanced. Health management refers to a set of perfect, delicate and personalized service procedures established on a scientific basis by applying information and medical technology, and aims to help healthy people and sub-healthy people to establish an ordered and healthy life style, reduce risk state and keep away from diseases by means of health maintenance, health promotion and the like; and once clinical symptoms appear, the health is restored as soon as possible through the arrangement of a hospitalization service. Health management is not only a concept, but also a method, and is a complete and careful service program, which aims to make patients and healthy people better recover health, maintain health and promote health.
Although the health management demands of residents and the social service organizations are rapidly increased along with the development of economy, in the prior art, a refined analysis method for the user demands and the service items and the service quality of medical organizations is lacked, so that the users with the health management demands lack the knowledge of the corresponding service organizations, and the target users cannot intuitively know the service organizations, the service quality and the service time through a mode of consulting and reserving the service organizations by telephone, short message or network before the service process, so that the users cannot select the proper service organizations, and the problem that the users are difficult to select the service organizations occurs.
Disclosure of Invention
The invention aims to provide a resident health service reservation method and system based on big data processing technology, and solves the problems that a user with health management requirement lacks the understanding of a corresponding service mechanism due to the lack of a refined analysis method between the user requirement and the service items and the service quality of a medical institution in the prior art, and the user cannot select a proper service mechanism due to the fact that a target user cannot intuitively know the service mechanism, the service quality and the service time by consulting and reserving the service mechanism in a telephone, short message or network mode before the service process, so that the user cannot select the proper service mechanism, and the service mechanism is difficult to select.
In order to achieve the purpose, the invention provides the following technical scheme:
a resident health service reservation method based on big data processing technology comprises the following steps:
s10, obtaining user information of a target user needing to reserve a target health service, wherein the user information comprises demand expectation data matched with the target health service;
s20, calculating the demand value of the residents based on the demand expectation data, and matching a target service mechanism for the target user based on the demand value and the target health service;
and S30, sending the matching result to the target user, acquiring feedback information of the target user on the matching result, and reserving the target health service for the target user based on the feedback information.
As a further scheme of the invention: in step S10, the user information further includes target user basic data and feedback data, and the demand expectation data includes at least one service item and a demand index of the target user for the service item;
furthermore, the types of the target health services comprise physical examination services, fitness services, nursing services, rehabilitation services, beauty services and the like, target service organizations corresponding to the various service items comprise physical examination centers, fitness centers, rehabilitation hospitals, beauty hospitals and the like, in addition, the target health services required to be reserved by residents comprise at least one service item, and the users can continue to select the service item after selecting one type of health services; in the process of selecting the service items, the target user quantifies the demand indexes of all the service items and displays the demand indexes in front of residents in a visual and clear manner;
for a health service organization providing target health services, the health service organization comprises at least one target health service, each target health service comprises at least one service item, when a target user subscribes to the target health service, different service items can be selected according to the requirement of the target user, and the requirement score of each service item, namely the requirement value, is provided; in addition, in order to enable the target user to reserve the proper health service, the comprehensive service capability of the target service organization is also subjected to service scoring, and the service scoring is calculated based on the number of service items selected by the target user and feedback data of residents, so that the target user can select the most proper health service organization and the target health service.
As a still further scheme of the invention: in step S20, the method of calculating the demand value of the resident includes the steps of:
s21, acquiring training data, wherein the training data are required expected data for model construction;
s21, identifying service items in the demand expectation data and demand indexes corresponding to the service items;
s22, constructing an initial evaluation model according to the number of the service items;
and S23, correspondingly inputting the resident demand index into an input layer for representing the evaluation index in the initial evaluation model, taking the demand value of the resident as the output of the initial evaluation model, and training the initial evaluation model to obtain the trained evaluation model.
As a still further scheme of the invention: the initial evaluation model comprises an input layer, a hidden layer and an output layer, wherein the input layer comprises
Figure 56244DEST_PATH_IMAGE001
A neuron for input, a hidden layer containing
Figure 667354DEST_PATH_IMAGE002
Each neuron is used for calculation, and the output layer contains
Figure 568445DEST_PATH_IMAGE003
The neurons are for outputting, wherein:
the output of the hidden layer is:
Figure 163374DEST_PATH_IMAGE004
wherein:
Figure 175324DEST_PATH_IMAGE005
in order to hide the layer activation function,
Figure 640940DEST_PATH_IMAGE006
as a hidden layer
Figure 712933DEST_PATH_IMAGE007
The output value of each of the neurons is,
Figure 795158DEST_PATH_IMAGE008
is as follows
Figure 345219DEST_PATH_IMAGE009
The input values of the individual input neurons are,
Figure 930921DEST_PATH_IMAGE010
as a hidden layer
Figure 173815DEST_PATH_IMAGE007
The first neuron and the input layer
Figure 353124DEST_PATH_IMAGE011
The connection weight of each of the neurons is calculated,
Figure 221723DEST_PATH_IMAGE012
as a hidden layer
Figure 678243DEST_PATH_IMAGE007
A bias of individual neurons;
the output of the output layer is:
Figure 419934DEST_PATH_IMAGE013
wherein:
Figure 352118DEST_PATH_IMAGE014
in order to output the layer activation function,
Figure 493249DEST_PATH_IMAGE015
is the first in the output layer
Figure 69855DEST_PATH_IMAGE016
The output value of each of the neurons is,
Figure 638240DEST_PATH_IMAGE017
as a hidden layer
Figure 933086DEST_PATH_IMAGE007
The first neuron and the output layer
Figure 877908DEST_PATH_IMAGE016
The connection weights of the individual neurons are,
Figure 309021DEST_PATH_IMAGE012
as an output layer
Figure 454831DEST_PATH_IMAGE007
Biasing of individual neurons.
As a still further scheme of the invention: first, the
Figure 955083DEST_PATH_IMAGE016
The error between the individual neurons and the desired output is:
Figure 454328DEST_PATH_IMAGE018
wherein the content of the first and second substances,
Figure 254794DEST_PATH_IMAGE019
is an output layer of
Figure 915714DEST_PATH_IMAGE016
The expected output value of the individual neuron.
As a still further scheme of the invention: the method of updating the connection weights and offsets by error is as follows:
Figure 168840DEST_PATH_IMAGE020
wherein the content of the first and second substances,
Figure 596411DEST_PATH_IMAGE021
in order to achieve the purpose of learning efficiency,
Figure 736536DEST_PATH_IMAGE021
is between 0 and 1;
training the initial evaluation model, wherein the conditions for obtaining the trained evaluation model are as follows:
Figure 348783DEST_PATH_IMAGE022
wherein, in the step (A),
Figure 574359DEST_PATH_IMAGE023
to the desired accuracy.
As a still further scheme of the invention: in step S20, the method for matching a target service organization with a target user includes the following steps:
selecting at least one service mechanism with target health services as a pre-selection mechanism, and calculating service scores of the target health services in the pre-selection mechanism;
step two, calculating the difference value between the demand value of the residents and the service score of the target health service:
Figure 664675DEST_PATH_IMAGE024
wherein, the first and the second end of the pipe are connected with each other,
Figure 924886DEST_PATH_IMAGE025
is a demand value of the residents,
Figure 442455DEST_PATH_IMAGE026
a service score for the target health service is provided,
Figure 155327DEST_PATH_IMAGE027
is an error expectation;
and step three, selecting the preselection mechanism with the minimum difference value as a target service mechanism.
As a still further scheme of the invention: the calculation method of the service score of the target health service comprises the following steps:
Figure 924700DEST_PATH_IMAGE028
wherein the content of the first and second substances,
Figure 288686DEST_PATH_IMAGE026
a service score for the target health service is provided,
Figure 727888DEST_PATH_IMAGE029
is as follows
Figure 442903DEST_PATH_IMAGE030
The evaluation score of the organization is preselected in the feedback data.
As a still further scheme of the invention: in step S30, the method for reserving a target service mechanism for a target user includes the following steps:
s31, sending the matching result to a target user;
s32, obtaining feedback information of the target user on the matching result;
and S33, verifying whether the feedback information contains the information that the target user receives the target service mechanism, and reserving the target health service for the target user if the feedback information passes the verification.
A resident health service reservation system based on big data processing technology, comprising:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring user information of a target user needing to reserve a target health service, and the user information comprises demand expectation data matched with the target health service;
a data processing module for calculating a demand value of the resident based on the demand expectation data; further configured to match a target service organization for a target user based on the demand value and a target health service;
and the reservation module is used for reserving the target health service for the target user.
In addition, above-mentioned resident health service reservation system based on big data processing technique installs in intelligent wearing equipment, and this intelligent wearing equipment includes equipment principal and display screen, the display screen is the touch screen display screen, and the resident can carry out data input through manual touch display screen, and this data input is as a mode that data acquisition module acquireed user information, of course, data acquisition module can also carry out pronunciation through installing the voice equipment of equipment principal inside and acquire user information and detect through the sensor equipment of installing on equipment principal and acquire user information.
Compared with the prior art, the invention has the beneficial effects that: according to the method and the system, the service quality of the service project is quantified by using the demand indexes of the service project, the demand value of the target health service is calculated according to the service project and the demand indexes, so that the user demand is quantified, the target health service project and the service mechanism which are suitable for the user demand are reserved for the user, the user can visually know about the service mechanism, the service mechanism can meet the user demand, and the technical problem that the health service is difficult to reserve is solved.
Drawings
Fig. 1 is a flowchart of a resident health service reservation method based on big data processing technology.
Fig. 2 is a flowchart of step S20 in the resident health service reservation method based on the big data processing technology.
Fig. 3 is a flowchart of step S30 in the resident health service reservation method based on the big data processing technology.
Fig. 4 is a block diagram showing the configuration of a resident health service reservation system based on a big data processing technology.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Although the health management needs of residents and the social service organizations are rapidly increased along with the development of economy, in the prior art, users with the health management needs lack knowledge of the corresponding service organizations, and target users generally consult and reserve the service organizations in a telephone, short message or network mode before carrying out the service process, but the mode can not lead the users to intuitively know the service organizations, the service quality and the service time, so that the users can not select the proper service organizations, and the problem that the users can not select the service organizations is caused.
Based on this, referring to fig. 1-3, in an embodiment of the present invention, a method for booking residential health services based on big data processing technology includes the following steps:
s10, obtaining user information of a target user needing to reserve a target health service, wherein the user information comprises demand expectation data matched with the target health service;
in step S10 of the embodiment of the present invention, the demand expectation data includes demand expectation data for model construction and demand expectation data for model calculation, where the demand expectation data includes at least one service item and a demand index of a target user for the service item, where types of residents include children, pregnant women, adults, and elderly people, and when different types of residents log in a resident health service reservation system based on a big data processing technology, health services corresponding to the current resident type are presented, different residents are given accurate delivery of appropriate service items, and resident experience is improved;
it should be further noted that the types of the target health services include physical examination service, fitness service, nursing service, rehabilitation service, beauty service, etc., target service organizations corresponding to the above various service items include physical examination centers, fitness centers, rehabilitation hospitals, beauty hospitals, etc., in addition, the target health services that residents need to make reservations include at least one service item, the user can continue to select the service item after selecting a type of health service, for example, after selecting beauty service, can select skin care item, body care item, etc., and after selecting fitness service, can select fat reduction item, strength item, flexibility item, etc.; in the process of selecting the service items, the target user quantifies the demand indexes of the service items and displays the demand indexes in front of residents in a visual and clear manner, so that the residents can conveniently select the demand indexes, for example, the service effect of the service items is quantified from 'professional', 'excellent', 'good', 'common' and 'entry' to be '5', '4', '3', '2', '1' and other demand indexes;
it should be further noted that, for a health service organization providing a target health service, the health service organization includes at least one target health service, each target health service includes at least one service item, when a target user subscribes to the target health service, different service items can be selected according to their own needs, and a demand score, i.e., a demand value, of each service item is proposed, for example, if the target user needs to select a "professional" fat-reduction item in the process of selecting a fitness service, the demand value of the target user for the fat-reduction item is "5"; in addition, in order to enable the target user to reserve the proper health service, the comprehensive service capability of the target service organization is also subjected to service scoring, and it is understood that the service scoring is calculated based on the number of service items selected by the target user and feedback data of residents, so that the target user can select the most proper health service organization and target health service.
In addition, the feedback data is evaluation data given by the target user to the health service institution after the health service is completed, and the evaluation data comprises evaluation data of each service item and a service score of the health service institution.
S20, calculating the demand value of residents based on the demand expectation data, matching a target service mechanism for a target user based on the demand value and the target health service, wherein the target service mechanism is a health service mechanism with the target health service required by the user;
in step S20 of the embodiment of the present invention, the method of calculating the demand value of the resident includes the steps of:
s21, acquiring training data, wherein the training data are required expected data for model construction;
s21, identifying service items in the demand expectation data and demand indexes corresponding to the service items;
s22, constructing an initial evaluation model according to the number of the service items;
s23, correspondingly inputting the resident demand index into an input layer for representing the evaluation index in the initial evaluation model, taking the demand value of the resident as the output of the initial evaluation model, training the initial evaluation model to obtain the trained evaluation model, wherein the demand index is a numerical value used for measuring the demand size of a service project in the target health service by a target user, the demand value is a numerical value used for measuring the demand size of the target health service calculated based on the demand index, and the demand value can intuitively reflect the demand size of the target service project by the target user and can be matched with the service score of a target service mechanism so as to enable the user to reserve to obtain a satisfied target service mechanism;
in the embodiment of the present invention, the evaluation model is:
Figure 625754DEST_PATH_IMAGE031
wherein:
Figure 985191DEST_PATH_IMAGE032
in order to activate the function(s),
Figure 844563DEST_PATH_IMAGE033
in order to output the value of the output,
Figure 532027DEST_PATH_IMAGE034
in order to input the value of the input,
Figure 767837DEST_PATH_IMAGE035
，
Figure 833709DEST_PATH_IMAGE036
is prepared by reacting with
Figure 863982DEST_PATH_IMAGE034
The corresponding weight vector is set to be,
Figure 38742DEST_PATH_IMAGE037
，
Figure 547084DEST_PATH_IMAGE038
is an offset;
furthermore, the evaluation model calculates the output value of the input sample by using forward feedback, if the actual output value is in accordance with the expected output value, the reverse feedback of the error is carried out, so that the network parameters are continuously adjusted and optimized, the accurate and reliable evaluation value is obtained through training of a large number of samples, when the evaluation model is carried forward, the input sample is transmitted from the output layer, and is transmitted to the output layer after being processed by each hidden layer of the middle layer, therefore, in the embodiment of the invention, the initial evaluation model comprises the input layer, the hidden layer and the output layer, wherein the input layer comprises the input layer, the hidden layer and the output layer
Figure 881113DEST_PATH_IMAGE001
A neuron for input, a hidden layer containing
Figure 833020DEST_PATH_IMAGE002
Each neuron is used for calculation, and the output layer contains
Figure 744344DEST_PATH_IMAGE003
The neurons are for outputting, wherein:
the output of the hidden layer is:
Figure 72689DEST_PATH_IMAGE004
wherein:
Figure 120279DEST_PATH_IMAGE005
in order to hide the layer activation function,
Figure 508666DEST_PATH_IMAGE006
as a hidden layer
Figure 641707DEST_PATH_IMAGE007
The output value of each of the neurons is,
Figure 898376DEST_PATH_IMAGE008
is as follows
Figure 816785DEST_PATH_IMAGE009
The input values of the individual input neurons are,
Figure 359762DEST_PATH_IMAGE010
as a hidden layer
Figure 465252DEST_PATH_IMAGE007
The first neuron and the input layer
Figure 650246DEST_PATH_IMAGE009
The connection weights of the individual neurons are,
Figure 157582DEST_PATH_IMAGE012
is a hidden layer
Figure 137039DEST_PATH_IMAGE007
A bias of individual neurons;
the output of the output layer is:
Figure 995405DEST_PATH_IMAGE013
wherein:
Figure 593876DEST_PATH_IMAGE014
in order to output the layer activation function,
Figure 204986DEST_PATH_IMAGE015
is the first in the output layer
Figure 106077DEST_PATH_IMAGE016
The output value of each of the neurons is,
Figure 701006DEST_PATH_IMAGE017
is a hidden layer
Figure 447377DEST_PATH_IMAGE007
The first neuron and the output layer
Figure 444152DEST_PATH_IMAGE016
The connection weights of the individual neurons are,
Figure 516144DEST_PATH_IMAGE012
is an output layer of
Figure 332790DEST_PATH_IMAGE007
A bias of individual neurons;
the evaluation model reverses the output value layer by utilizing reverse feedback, distributes errors to all neurons of each layer, takes the error signal as a correction basis for the weight value of each neuron, and continuously adjusts the weight value and the offset value of the neuron through the difference between the output layer and the expected value so as to continuously optimize the neural network, wherein the first time in the back propagation process is
Figure DEST_PATH_IMAGE039
The error between individual neurons and the desired output is:
Figure 351693DEST_PATH_IMAGE018
wherein, the first and the second end of the pipe are connected with each other,
Figure 937395DEST_PATH_IMAGE019
as an output layer
Figure 180289DEST_PATH_IMAGE016
The expected output value of the individual neuron;
in addition, the method of updating the connection weight and the offset by the error is as follows:
Figure 484231DEST_PATH_IMAGE020
wherein the content of the first and second substances,
Figure 103562DEST_PATH_IMAGE021
in order to achieve the purpose of learning efficiency,
Figure 543771DEST_PATH_IMAGE021
is between 0 and 1;
in the embodiment of the present invention, the initial evaluation model is trained, and the conditions for obtaining the trained evaluation model are as follows:
Figure 223145DEST_PATH_IMAGE022
wherein, in the process,
Figure 748804DEST_PATH_IMAGE023
set by the model builder for the desired accuracy;
in the embodiment of the invention, the hidden layer activation function and the output layer activation function are the same and are S-shaped functions
Figure 296460DEST_PATH_IMAGE040
。
In step S20 of the embodiment of the present invention, the closer the service score of the target service organization is to the demand value of the residents, the more the target service organization can meet the demands of the residents, and the target service organization closest to the demand value of the residents is the optimal target service organization;
further, in step S20, the method for calculating the service score of the target health service includes:
Figure 607487DEST_PATH_IMAGE028
wherein the content of the first and second substances,
Figure 441451DEST_PATH_IMAGE026
scoring the services of the target service organization,
Figure 736297DEST_PATH_IMAGE029
is as follows
Figure 681119DEST_PATH_IMAGE030
The evaluation score of the target service organization in the feedback data is that the target user scores the target health service of the target service organization after finishing the target health service, and the score is the target health service
Figure 112232DEST_PATH_IMAGE029
；
Still further, in step S20, the method for matching the target service organization with the target user includes the following steps:
selecting at least one service mechanism with target health services as a pre-selection mechanism, and calculating service scores of the target health services in the pre-selection mechanism;
step two, calculating the difference value between the demand value of the residents and the service score of the target health service:
Figure 992463DEST_PATH_IMAGE041
wherein the content of the first and second substances,
Figure 492715DEST_PATH_IMAGE025
in order to meet the demand of the residents,
Figure 257540DEST_PATH_IMAGE026
a service score for the target health service is provided,
Figure 526847DEST_PATH_IMAGE027
is an error expectation;
and step three, selecting the preselection mechanism with the minimum difference value as a target service mechanism.
S30, sending the matching result to a target user, acquiring feedback information of the target user on the matching result, and reserving the target service mechanism for the target user based on the feedback information;
in step S30 of the embodiment of the present invention, a method for reserving a target health service for a target user includes the following steps:
s31, sending the matching result to a target user;
s32, feedback information of the target user on the matching result is obtained;
and S33, verifying whether the feedback information contains the information that the target user receives the target service mechanism, and reserving the target health service for the target user if the feedback information passes the verification.
It should be noted that, after the target user and the target service mechanism are matched, the obtained matching result is the target service mechanism with the target health service, and after the target service mechanism is sent to the target user, the target user generates feedback information whether to receive the target health service of the target service mechanism based on the own requirement, and when the target user receives the feedback information, the target health service can be reserved for the user.
Referring to fig. 4, the present invention also discloses a resident health service reservation system based on big data processing technology, comprising:
the data acquisition module 100 is configured to acquire user information of a target user who needs to reserve a target health service, where the user information includes demand expectation data matched with the target health service;
a data processing module 200 for calculating demand values of residents based on the demand expectation data; further configured to match a target service organization for a target user based on the demand value and a target health service;
a matching module 300, configured to reserve the target service for the target user.
It should be noted that, in the embodiment of the present invention, the resident health service reservation system based on big data processing technology is installed in an intelligent wearable device, the intelligent wearable device includes a device main body and a display screen, the display screen is a touch screen display screen, a resident can perform data input by manually touching the display screen, the data input is used as a way for a data acquisition module to acquire user information, and certainly, the data acquisition module can also perform voice acquisition on user information through a voice device installed inside the device main body and detect and acquire the user information through a sensor device installed on the device main body:
in summary, compared with the prior art, the invention has the beneficial effects that: according to the method and the system, the service quality of the service project is quantified by using the demand indexes of the service project, the demand value of the target health service is calculated according to the service project and the demand indexes, the user demand is quantified, the target health service project and the service mechanism which are suitable for the user demand are reserved for the user, the user can visually know the service mechanism, the service mechanism can be ensured to meet the user demand, and the technical problem that the health service is difficult to reserve is solved.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.

Claims (10)

1. A resident health service reservation method based on big data processing technology is characterized by comprising the following steps:
s10, obtaining user information of a target user needing to reserve a target health service, wherein the user information comprises demand expectation data matched with the target health service;
s20, calculating the demand value of the residents based on the demand expectation data, and matching a target service mechanism for the target user based on the demand value and the target health service;
and S30, sending the matching result to the target user, acquiring feedback information of the target user on the matching result, and reserving the target health service for the target user based on the feedback information.
2. The resident health service reservation method based on big data processing technology according to claim 1, wherein in step S10, the user information further comprises objective user basic data and feedback data, and the demand expectation data comprises at least one service item and a demand index of the objective user for the service item.
3. The resident health service reservation method based on big data processing technology according to claim 1, wherein the method of calculating the demand value of the resident in the step S20 comprises the steps of:
s21, acquiring training data, wherein the training data are required expected data for model construction;
s21, identifying service items in the demand expectation data and demand indexes corresponding to the service items;
s22, constructing an initial evaluation model according to the number of the service items;
and S23, correspondingly inputting the resident demand indexes into an input layer of the characterization evaluation indexes in the initial evaluation model, taking the demand values of the residents as the output of the initial evaluation model, and training the initial evaluation model to obtain a trained evaluation model.
4. The resident health service booking method based on big data processing technology according to claim 3, wherein the initial evaluation model comprises an input layer, a hidden layer and an output layer, the input layer comprises an input layer, a hidden layer and an output layer
Figure 160670DEST_PATH_IMAGE001
A neuron for input, a hidden layer containing
Figure 17636DEST_PATH_IMAGE002
Each neuron is used for calculation, and the output layer contains
Figure 69906DEST_PATH_IMAGE003
The neurons are for outputting, wherein:
the output of the hidden layer is:
Figure 522884DEST_PATH_IMAGE004
wherein:
Figure 976999DEST_PATH_IMAGE005
in order to hide the layer activation function,
Figure 4867DEST_PATH_IMAGE006
as a hidden layer
Figure 278853DEST_PATH_IMAGE007
The output value of each of the neurons is,
Figure 801101DEST_PATH_IMAGE008
is as follows
Figure 358991DEST_PATH_IMAGE009
The input values of the individual input neurons are,
Figure 42913DEST_PATH_IMAGE010
as a hidden layer
Figure 538616DEST_PATH_IMAGE007
The first neuron and the input layer
Figure 113823DEST_PATH_IMAGE009
The connection weight of each of the neurons is calculated,
Figure 11372DEST_PATH_IMAGE011
as a hidden layer
Figure 866195DEST_PATH_IMAGE007
A bias of individual neurons;
the output of the output layer is:
Figure 358182DEST_PATH_IMAGE012
wherein:
Figure 222233DEST_PATH_IMAGE013
in order to output the layer activation function,
Figure 239868DEST_PATH_IMAGE014
is the first in the output layer
Figure 796751DEST_PATH_IMAGE015
The output value of each of the neurons is,
Figure 516314DEST_PATH_IMAGE016
as a hidden layer
Figure 652897DEST_PATH_IMAGE007
The first neuron and the output layer
Figure 790618DEST_PATH_IMAGE015
The connection weights of the individual neurons are,
Figure 767670DEST_PATH_IMAGE011
as an output layer
Figure 725262DEST_PATH_IMAGE007
Biasing of individual neurons.
5. The resident health service reservation method based on big data processing technology according to claim 4, characterized in that
Figure 931115DEST_PATH_IMAGE015
The error between the individual neurons and the desired output is:
Figure 172609DEST_PATH_IMAGE017
wherein the content of the first and second substances,
Figure 540137DEST_PATH_IMAGE018
as an output layer
Figure 250604DEST_PATH_IMAGE015
The expected output value of the individual neuron.
6. The resident health service reservation method based on big data processing technology according to claim 5, wherein the method of updating the connection weight and the bias by error is as follows:
Figure 243836DEST_PATH_IMAGE019
wherein the content of the first and second substances,
Figure 90570DEST_PATH_IMAGE020
in order to improve the learning efficiency of the learning,
Figure 894578DEST_PATH_IMAGE020
is between 0 and 1;
training the initial evaluation model, wherein the conditions for obtaining the trained evaluation model are as follows:
Figure 810450DEST_PATH_IMAGE021
wherein, in the step (A),
Figure 623685DEST_PATH_IMAGE022
to the desired accuracy.
7. The resident health service reservation method based on big data processing technology according to claim 2, wherein the method of matching the target service institution for the target user in step S20 comprises the steps of:
selecting at least one service mechanism with target health service as a preselection mechanism, and calculating a service score of the target health service in the preselection mechanism;
step two, calculating the difference value between the demand value of the residents and the service score of the target health service:
Figure 324925DEST_PATH_IMAGE023
wherein, the first and the second end of the pipe are connected with each other,
Figure 299834DEST_PATH_IMAGE024
in order to meet the demand of the residents,
Figure 968582DEST_PATH_IMAGE025
a service score for the target health service is determined,
Figure 54349DEST_PATH_IMAGE026
is an error expectation;
and step three, selecting the preselection mechanism with the minimum difference value as a target service mechanism.
8. The resident health service booking method based on big data processing technology according to claim 7, wherein the service score of the target health service is calculated by:
Figure 875675DEST_PATH_IMAGE027
wherein the content of the first and second substances,
Figure 5174DEST_PATH_IMAGE025
a service score for the target health service is provided,
Figure 911950DEST_PATH_IMAGE028
is as follows
Figure 801409DEST_PATH_IMAGE029
The evaluation score of the organization is preselected in the feedback data.
9. The resident health service reservation method based on big data processing technology according to claim 8, wherein the method of reserving the target service institution for the target user in step S30 comprises the steps of:
s31, sending the matching result to a target user;
s32, obtaining feedback information of the target user on the matching result;
and S33, verifying whether the feedback information contains the information that the target user receives the target service mechanism, and reserving the target health service for the target user if the feedback information passes the verification.
10. A resident health service reservation system based on big data processing technology, comprising:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring user information of a target user needing to reserve a target health service, and the user information comprises demand expectation data matched with the target health service;
a data processing module for calculating a demand value of the resident based on the demand expectation data; further configured to match a target service organization for a target user based on the demand value and a target health service;
and the reservation module is used for reserving the target health service for the target user.
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