CN115394394B - 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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CN115394394B
CN115394394B CN202211321778.3A CN202211321778A CN115394394B CN 115394394 B CN115394394 B CN 115394394B CN 202211321778 A CN202211321778 A CN 202211321778A CN 115394394 B CN115394394 B CN 115394394B
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service
target
demand
health service
user
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CN115394394A (en
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孔庆来
刘宁
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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 is more and more concerned by people, 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 medical services. Health management is not only a concept, but also a method, and is a complete and thorough 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, a refined analysis method for the user demands and the service items and the service quality of medical organizations is lacked in the prior art, so that the users with the health management demands lack the understanding of the corresponding service organizations, and the target users cannot intuitively know the service organizations, the service quality and the service time through a telephone, short message or network mode to consult and reserve the service organizations 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 is caused.
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;
s22, identifying service items in the demand expectation data and demand indexes corresponding to the service items;
s23, constructing an initial evaluation model according to the number of the service items;
and S24, 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 74122DEST_PATH_IMAGE001
A neuron for input, a hidden layer containing
Figure 69760DEST_PATH_IMAGE002
Each neuron is used for calculation, and the output layer contains
Figure 473059DEST_PATH_IMAGE003
The neurons are for outputting, wherein:
the output of the hidden layer is:
Figure 466423DEST_PATH_IMAGE004
wherein:
Figure 892856DEST_PATH_IMAGE005
in order to hide the layer activation function,
Figure 364289DEST_PATH_IMAGE006
as a hidden layer
Figure 887674DEST_PATH_IMAGE007
The output value of each of the neurons is,
Figure 114256DEST_PATH_IMAGE008
is as follows
Figure 90303DEST_PATH_IMAGE009
The input values of the individual input neurons are,
Figure 536065DEST_PATH_IMAGE010
as a hidden layer
Figure 179536DEST_PATH_IMAGE007
The first neuron and the input layer
Figure 249123DEST_PATH_IMAGE009
The connection weights of the individual neurons are,
Figure 774782DEST_PATH_IMAGE011
as a hidden layer
Figure 588018DEST_PATH_IMAGE007
A bias of individual neurons;
the output of the output layer is:
Figure 85995DEST_PATH_IMAGE012
wherein:
Figure 529746DEST_PATH_IMAGE013
in order to output the layer activation function,
Figure 214805DEST_PATH_IMAGE014
is the first in the output layer
Figure 97311DEST_PATH_IMAGE015
The output value of each of the neurons is,
Figure 246532DEST_PATH_IMAGE016
is a hidden layer
Figure 923501DEST_PATH_IMAGE007
The first neuron and the output layer
Figure 33540DEST_PATH_IMAGE015
The connection weights of the individual neurons are,
Figure 454157DEST_PATH_IMAGE017
is an outputLayer one
Figure 661147DEST_PATH_IMAGE015
Biasing of individual neurons.
As a still further scheme of the invention: first, the
Figure 571334DEST_PATH_IMAGE015
The error between individual neurons and the desired output is:
Figure 496565DEST_PATH_IMAGE018
wherein, the first and the second end of the pipe are connected with each other,
Figure 455294DEST_PATH_IMAGE019
as an output layer
Figure 188894DEST_PATH_IMAGE015
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 207666DEST_PATH_IMAGE020
wherein the content of the first and second substances,
Figure 620193DEST_PATH_IMAGE021
in order to achieve the purpose of learning efficiency,
Figure 444929DEST_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 360933DEST_PATH_IMAGE022
wherein, in the step (A),
Figure 721245DEST_PATH_IMAGE023
to a 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 service as a preselection mechanism, and calculating a service score of the target health service in the preselection mechanism;
step two, calculating the difference between the demand value of the residents and the service score of the target health service:
Figure 621068DEST_PATH_IMAGE024
wherein the content of the first and second substances,
Figure 921599DEST_PATH_IMAGE025
in order to meet the demand of the residents,
Figure 20005DEST_PATH_IMAGE026
a service score for the target health service is determined,
Figure 115000DEST_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 502119DEST_PATH_IMAGE028
wherein, the first and the second end of the pipe are connected with each other,
Figure 544024DEST_PATH_IMAGE026
a service score for the target health service is determined,
Figure 169041DEST_PATH_IMAGE029
is as follows
Figure 762833DEST_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 main part 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 the pronunciation through installing in the inside voice equipment of equipment main part and acquire user information and detect through the sensor equipment of installing in the equipment main part 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 construction 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 derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within 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 a 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 services, fitness services, nursing services, rehabilitation services, beauty services, and the like, target service organizations corresponding to the above various service items include physical examination centers, fitness halls, rehabilitation hospitals, beauty hospitals, and the like, in addition, the target health services that residents need to reserve include at least one service item, and the users can continue to select the service item after selecting one type of health services, for example, after selecting beauty services, skin care items, body care items, and the like can be selected, and after selecting fitness services, fat reduction items, strength items, flexibility items, and the like can be selected; 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 provided, 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 organization after the health service is completed, and the evaluation data comprises evaluation data of each service item and service scores of the health service organization.
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;
s22, identifying service items in the demand expectation data and demand indexes corresponding to the service items;
s23, constructing an initial evaluation model according to the number of the service items;
s24, correspondingly inputting the resident demand indexes into an input layer of a characteristic evaluation index in the initial evaluation model, taking the demand values of residents as the output of the initial evaluation model, training the initial evaluation model to obtain a trained evaluation model, wherein the demand indexes are numerical values used for measuring the demand size of service items in the target health service by target users, the demand values are numerical values used for measuring the demand size of the target health service and calculated based on the demand indexes, and the demand values can intuitively reflect the demand size of the target service items by the target users and can be matched with the service scores of a target service mechanism so that the users can reserve to obtain a satisfactory target service mechanism;
in the embodiment of the present invention, the evaluation model is:
Figure 106090DEST_PATH_IMAGE031
wherein:
Figure 279582DEST_PATH_IMAGE032
in order to activate the function(s),
Figure 962367DEST_PATH_IMAGE033
in order to output the value of the output,
Figure 664744DEST_PATH_IMAGE034
in order to input the value of the digital signal,
Figure 557614DEST_PATH_IMAGE035
，
Figure 269218DEST_PATH_IMAGE036
is and is
Figure 868826DEST_PATH_IMAGE037
The corresponding weight vector is then used to determine the weight,
Figure 679788DEST_PATH_IMAGE038
，
Figure 263216DEST_PATH_IMAGE039
is an offset;
further, 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, and the evaluation model evaluates the network parameters by training a large number of samples until obtaining an accurate and reliable evaluation valueIn the forward propagation of the price model, the input sample is transmitted from the output layer, and is processed by the hidden layers in the middle layer and then transmitted to the output layer, so in the embodiment of the invention, the initial evaluation model comprises the input layer, the hidden layer and the output layer, and the input layer comprises the input layer, the hidden layer and the output layer
Figure 247352DEST_PATH_IMAGE001
A neuron for input, a hidden layer containing
Figure 29363DEST_PATH_IMAGE002
Each neuron is used for calculation, and the output layer contains
Figure 73543DEST_PATH_IMAGE003
The neurons are for outputting, wherein:
the output of the hidden layer is:
Figure 144267DEST_PATH_IMAGE004
wherein:
Figure 368313DEST_PATH_IMAGE005
in order to hide the layer activation function,
Figure 942513DEST_PATH_IMAGE006
as a hidden layer
Figure 219911DEST_PATH_IMAGE007
The output value of each of the neurons is,
Figure 512352DEST_PATH_IMAGE008
is a first
Figure 103870DEST_PATH_IMAGE009
The input value of each of the input neurons,
Figure 470261DEST_PATH_IMAGE010
is a hidden layer
Figure 856243DEST_PATH_IMAGE007
The first neuron and the input layer
Figure 901559DEST_PATH_IMAGE009
The connection weight of each of the neurons is calculated,
Figure 93506DEST_PATH_IMAGE011
as a hidden layer
Figure 376720DEST_PATH_IMAGE007
A bias of individual neurons;
the output of the output layer is:
Figure 136865DEST_PATH_IMAGE012
wherein:
Figure 403899DEST_PATH_IMAGE013
in order to output the layer activation function,
Figure 337220DEST_PATH_IMAGE014
is the first in the output layer
Figure 537257DEST_PATH_IMAGE015
The output value of each of the neurons is,
Figure 796200DEST_PATH_IMAGE016
as a hidden layer
Figure 284950DEST_PATH_IMAGE007
The first neuron and the output layer
Figure 694066DEST_PATH_IMAGE015
The connection weights of the individual neurons are,
Figure 951872DEST_PATH_IMAGE017
is an output layer of
Figure 178454DEST_PATH_IMAGE015
A bias of individual neurons;
the evaluation model utilizes the reverse feedback to reverse the output value layer by layer, distributes errors to all the neurons of each layer, uses the error signals as correction basis for the weight values of the neurons, and continuously adjusts the weight values and the bias values of the neurons through the difference between the output layer and the expected value so as to continuously optimize the neural network, and for the first neuron in the back propagation process
Figure 154500DEST_PATH_IMAGE015
The error between individual neurons and the desired output is:
Figure 429624DEST_PATH_IMAGE018
wherein the content of the first and second substances,
Figure 243734DEST_PATH_IMAGE019
as an output layer
Figure 313321DEST_PATH_IMAGE015
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 42242DEST_PATH_IMAGE020
wherein the content of the first and second substances,
Figure 917794DEST_PATH_IMAGE021
in order to achieve the purpose of learning efficiency,
Figure 150193DEST_PATH_IMAGE021
is between 0 and 1;
in an embodiment of the present invention, the initial evaluation model is trained, and the conditions for obtaining the trained evaluation model are as follows:
Figure 328364DEST_PATH_IMAGE022
wherein，
Figure 544582DEST_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 161508DEST_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 310730DEST_PATH_IMAGE028
wherein, the first and the second end of the pipe are connected with each other,
Figure 987699DEST_PATH_IMAGE026
scoring the services of the target service organization,
Figure 97737DEST_PATH_IMAGE029
is a first
Figure 518354DEST_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 725345DEST_PATH_IMAGE041
；
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 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 369953DEST_PATH_IMAGE024
wherein the content of the first and second substances,
Figure 295183DEST_PATH_IMAGE025
is a demand value of the residents,
Figure 253912DEST_PATH_IMAGE026
a service score for the target health service is provided,
Figure 253092DEST_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, 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.
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 the 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 manually touch the display screen to perform data input, the data input is used as a way for a data acquisition module to acquire user information, of course, 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 (9)

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, matching the target service institution for the target users based on the demand value and the target health service, and calculating the demand value of the residents, wherein the method comprises the following steps:
s21, acquiring training data, wherein the training data are required expected data for model construction;
s22, identifying service items in the demand expectation data and demand indexes corresponding to the service items;
s23, constructing an initial evaluation model according to the number of the service items;
s24, correspondingly inputting the demand indexes into an input layer of the characteristic evaluation indexes in the initial evaluation model, taking the demand values of residents as the output of the initial evaluation model, and training the initial evaluation model to obtain a trained evaluation model;
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 as claimed in claim 1, wherein in step S10, the user information further comprises target user basic data and feedback data, and the demand expectation data comprises at least one service item and a demand index of the target user for the service item.
3. The resident health service booking method based on big data processing technology according to claim 1, 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 248150DEST_PATH_IMAGE001
A neuron for input, a hidden layer containing
Figure 370827DEST_PATH_IMAGE002
Each neuron is used for calculation, and the output layer contains
Figure 330692DEST_PATH_IMAGE003
The neurons are for outputting, wherein:
the output of the hidden layer is:
Figure 123199DEST_PATH_IMAGE004
wherein:
Figure 434095DEST_PATH_IMAGE005
in order to hide the layer activation function,
Figure 94883DEST_PATH_IMAGE006
is a hidden layer
Figure 643676DEST_PATH_IMAGE007
The output value of each of the neurons is,
Figure 997297DEST_PATH_IMAGE008
is as follows
Figure 903811DEST_PATH_IMAGE009
The input values of the individual input neurons are,
Figure 837132DEST_PATH_IMAGE010
is a hidden layer
Figure 771590DEST_PATH_IMAGE007
The first neuron and the input layer
Figure 296112DEST_PATH_IMAGE009
The connection weight of each of the neurons is calculated,
Figure 50442DEST_PATH_IMAGE011
as a hidden layer
Figure 53033DEST_PATH_IMAGE007
A bias of individual neurons;
the output of the output layer is:
Figure 717363DEST_PATH_IMAGE012
wherein:
Figure 147208DEST_PATH_IMAGE013
in order to output the layer activation function,
Figure 654412DEST_PATH_IMAGE014
is the first in the output layer
Figure 460694DEST_PATH_IMAGE015
The output value of each of the neurons is,
Figure 838586DEST_PATH_IMAGE016
as a hidden layer
Figure 580277DEST_PATH_IMAGE007
The first neuron and the output layer
Figure 309199DEST_PATH_IMAGE015
The connection weights of the individual neurons are,
Figure 919172DEST_PATH_IMAGE017
is an output layer of
Figure 417149DEST_PATH_IMAGE015
Biasing of individual neurons.
4. The resident health service reservation method based on big data processing technology according to claim 3, wherein the first step
Figure 188796DEST_PATH_IMAGE015
The error between individual neurons and the desired output is:
Figure 545959DEST_PATH_IMAGE018
wherein, the first and the second end of the pipe are connected with each other,
Figure 428464DEST_PATH_IMAGE019
is an output layer of
Figure 46528DEST_PATH_IMAGE015
Stage of individual neuronThe output value of the observation device is expected,
Figure 989076DEST_PATH_IMAGE014
is the first in the output layer
Figure 692590DEST_PATH_IMAGE015
The output value of each neuron.
5. The resident health service booking method based on big data processing technology according to claim 4, wherein the method of updating the connection weight and the bias by error is as follows:
Figure 378786DEST_PATH_IMAGE020
wherein the content of the first and second substances,
Figure 225257DEST_PATH_IMAGE021
in order to achieve the purpose of learning efficiency,
Figure 604286DEST_PATH_IMAGE021
is between 0 and 1 and is,
Figure 795096DEST_PATH_IMAGE016
is a hidden layer
Figure 284983DEST_PATH_IMAGE007
The first neuron and the output layer
Figure 612059DEST_PATH_IMAGE022
Figure 37355DEST_PATH_IMAGE015
Connection weights of individual neurons;
training the initial evaluation model, wherein the conditions for obtaining the trained evaluation model are as follows:
Figure 449882DEST_PATH_IMAGE023
wherein, in the step (A),
Figure 743460DEST_PATH_IMAGE024
to the desired accuracy.
6. The resident health service booking 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 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 925043DEST_PATH_IMAGE025
wherein the content of the first and second substances,
Figure 645874DEST_PATH_IMAGE026
in order to meet the demand of the residents,
Figure 686642DEST_PATH_IMAGE027
a service score for the target health service is provided,
Figure 518332DEST_PATH_IMAGE028
is an error expectation;
and step three, selecting the preselection mechanism with the minimum difference value as a target service mechanism.
7. The resident health service booking method based on big data processing technology according to claim 6, wherein the service score of the target health service is calculated by:
Figure 554421DEST_PATH_IMAGE029
wherein the content of the first and second substances,
Figure 446154DEST_PATH_IMAGE027
a service score for the target health service is provided,
Figure 833273DEST_PATH_IMAGE030
is as follows
Figure 344020DEST_PATH_IMAGE031
The evaluation score of the organization is preselected in the feedback data.
8. The resident health service reservation method based on big data processing technology according to claim 7, 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.
9. A system for the resident health service booking method based on big data processing technology according to any one of claims 1 to 8, 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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