CN115798677B - Intelligent child rehabilitation management system and method based on big data - Google Patents
Intelligent child rehabilitation management system and method based on big data Download PDFInfo
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Abstract
The invention provides an intelligent child rehabilitation management system and method based on big data, wherein the system comprises: the acquisition module is used for acquiring the children rehabilitation evaluation requirement, and the children rehabilitation evaluation requirement comprises: target children, child problems, and guardians; the construction module is used for constructing a child rehabilitation evaluation project library based on big data; the determining module is used for determining target evaluation items of target children based on the child rehabilitation evaluation item library according to the child problems, and simultaneously, scheduling preset therapists for corresponding evaluation to obtain an evaluation result; and the course arrangement module is used for carrying out course arrangement management based on the evaluation result. According to the intelligent child rehabilitation management system and method based on big data, a big data technology is introduced, a child rehabilitation evaluation item library is constructed, and the comprehensiveness of construction of the child rehabilitation evaluation item library is improved; based on the evaluation result of therapist, the course arrangement management is directly carried out, the course arrangement management is not needed to be carried out manually, the labor cost is reduced, and the method is more intelligent.
Description
Technical Field
The invention relates to the technical field of big data, in particular to an intelligent child rehabilitation management system and method based on big data.
Background
At present, when a child to be diagnosed is evaluated by a child rehabilitation management mechanism (for example: xx women and children health care department), an evaluation item (for example: dyskinesia evaluation) stored in an internal database is often called to evaluate the child, the internal database is slowly updated, an evaluation scale corresponding to the evaluation item cannot be updated in time, so that the child rehabilitation evaluation is not comprehensive enough, meanwhile, when a treatment course of a problem child is arranged, manual course arrangement is required, the labor cost is high, and the problem child rehabilitation management mechanism is not intelligent enough.
Thus, a solution is needed.
Disclosure of Invention
The invention aims at providing an intelligent child rehabilitation management system based on big data, which introduces big data technology to construct a child rehabilitation evaluation item library, so that the comprehensiveness of the construction of the child rehabilitation evaluation item library is improved; based on the evaluation result of therapist, the course arrangement management is directly carried out, the course arrangement management is not needed to be carried out manually, the labor cost is reduced, and the method is more intelligent.
The embodiment of the invention provides an intelligent child rehabilitation management system based on big data, which comprises the following components:
the acquisition module is used for acquiring the children rehabilitation evaluation requirement, and the children rehabilitation evaluation requirement comprises: target children, child problems, and guardians;
the construction module is used for constructing a child rehabilitation evaluation project library based on big data;
the determining module is used for determining target evaluation items of the target children based on the child rehabilitation evaluation item library according to the child problems, and simultaneously, scheduling a preset therapist to perform corresponding evaluation to obtain an evaluation result;
and the course arrangement module is used for carrying out course arrangement management based on the evaluation result.
Preferably, the obtaining module obtains a child rehabilitation evaluation requirement, including:
acquiring the child rehabilitation evaluation requirement received by a preset reservation node;
and/or the number of the groups of groups,
acquiring an outpatient record input by an attendant of an outpatient center of a child rehabilitation institution into an outpatient system;
analyzing the outpatient records to obtain the child rehabilitation evaluation requirements received by the attendant.
Preferably, the construction module constructs a child rehabilitation evaluation item library based on big data, and the construction module comprises:
based on big data, acquiring evaluation item information stored by a plurality of item resource access nodes and taking the evaluation item information as a pre-selection warehousing target;
performing qualification verification on the project resource access node;
if the qualification verification is passed, obtaining the access type when accessing the corresponding project resource access node;
if the access type is direct access, taking the corresponding pre-selected warehousing target as a first target to be warehoused;
if the access type is indirect access, acquiring a second target to be put in storage;
storing all the first objects to be put in storage and all the second objects to be put in storage into a preset empty database to finish construction;
the obtaining the second target to be put in storage includes:
obtaining the credibility of the indirectly accessed intermediate node;
and if the credibility is greater than or equal to a preset credibility threshold, taking the preselected warehousing target provided by the corresponding intermediate node as a second target to be warehoused.
Preferably, the construction module performs qualification verification on the project resource access node, including:
obtaining a node type of a project resource access node, wherein the node type comprises: off-line nodes and network nodes;
when the node type is an off-line node, acquiring an evaluation effect value corresponding to an evaluation item provided by the off-line node;
if the effect value is greater than or equal to a preset effect value threshold, the corresponding project resource access node passes verification;
when the node type is a network node, acquiring network evaluation information of a user corresponding to the network node;
determining a user evaluation value based on the network evaluation information;
and if the user evaluation value is greater than or equal to a preset evaluation value threshold, the corresponding project resource access node passes verification.
Preferably, the course arrangement module performs course arrangement management based on the evaluation result, including:
determining recommended course information of the target child based on the evaluation result;
pushing the evaluation result and the recommended course information to the guardian corresponding to the target child;
acquiring a target course selected from the recommended course information by the corresponding guardian, and associating the target course with the corresponding target child;
acquiring first class time information of the target course and idle time information of a corresponding target child;
based on the first lesson time information and the idle time information, determining lesson arrangement information;
and based on the course arrangement information, carrying out course arrangement management on the corresponding target children.
Preferably, the course arrangement module determines course arrangement information based on the first lesson time information and idle time information, and includes:
expanding the first class time information on a preset time axis, and determining a plurality of first class time periods;
expanding the idle time information on the time axis to determine a plurality of idle time periods;
sequentially traversing the first class time period from front to back, and taking the first class time period currently traversed as a second class time period;
judging whether the second class time period falls in any idle time period;
if so, taking the time corresponding to the second lesson time period of the current traversal as the target lesson time, and associating the target lesson time with the corresponding target child;
and taking the target courses and the target lesson time which are correspondingly associated with all target children as lesson arranging information.
Preferably, the course arrangement module judges whether the second lesson-taking time period falls in any idle time period, including:
acquiring a first starting position and a first ending position of the second lesson-taking time period on the time axis, and simultaneously acquiring a second starting position of the idle time period on the time axis;
determining a corresponding second starting position which is positioned before the first starting position and closest to the first starting position in the second starting position, and taking the second starting position as a third starting position;
acquiring a second end position of the idle time period corresponding to the third start position on the time axis;
judging whether the first end position is before the second end position;
if yes, the second class time period is all within the idle time period.
Preferably, the intelligent child rehabilitation management system based on big data further comprises:
and the adjusting module is used for acquiring the lesson taking behavior of the target child when the target child takes lessons and determining class performance scores of the target child based on the lesson taking behavior.
Preferably, the determining the class performance score of the target child based on the lesson taking behavior includes:
acquiring initial performance scores of target children;
acquiring a behavior permission state of a target child when the target child generates a lesson behavior;
if the behavior permission state is not permission, acquiring a corresponding down-regulation coefficient;
if the behavior permission state is permission, acquiring a preset expression feature extraction template;
extracting characteristics of the lesson-taking behavior to obtain a plurality of characteristic expression values;
acquiring the expression weight of the expression type corresponding to the expression characteristic value;
determining an up-regulation coefficient based on the expression characteristic value and the expression weight;
determining class performance scores for the target child based on the turndown factor, the turnup factor, and the initial performance scores
The embodiment of the invention provides an intelligent child rehabilitation management method based on big data, which comprises the following steps:
step 1: obtaining a child rehabilitation assessment demand, the child rehabilitation assessment demand comprising: target children, child problems, and guardians;
step 2: constructing a child rehabilitation evaluation project library based on big data;
step 3: based on the child rehabilitation evaluation item library, determining target evaluation items of the target child according to the child problems, and simultaneously, scheduling a preset therapist to perform corresponding evaluation to obtain an evaluation result;
step 4: based on the evaluation result, course arrangement management is performed.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of an intelligent child rehabilitation management system based on big data in an embodiment of the invention;
fig. 2 is a flowchart of an intelligent child rehabilitation management method based on big data in an embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
The embodiment of the invention provides an intelligent child rehabilitation management system based on big data, which is shown in figure 1 and comprises the following steps:
the acquisition module 1 is configured to acquire a child rehabilitation evaluation requirement, where the child rehabilitation evaluation requirement includes: target children, child problems, and guardians;
the construction module 2 is used for constructing a child rehabilitation evaluation project library based on big data;
the determining module 3 is configured to determine a target evaluation item of the target child according to the child problem based on the child rehabilitation evaluation item library, and schedule a preset therapist to perform corresponding evaluation at the same time, so as to obtain an evaluation result;
and the course arrangement module 4 is used for carrying out course arrangement management based on the evaluation result.
The working principle and the beneficial effects of the technical scheme are as follows:
the child rehabilitation assessment needs include: target children (children in need of rehabilitation management), child problems (e.g., social and linguistic development problems, etc.), and guardians (e.g., parents of target children). Acquiring evaluation items from a big data platform, and constructing a child rehabilitation evaluation item library, wherein the child rehabilitation evaluation item library comprises: a plurality of child rehabilitation assessment items, for example: autism spectrum screening, developmental capacity assessment, comprehensive assessment and the like, and a big data technology belongs to the prior art and is not repeated. Determining target evaluation items of a target child according to the child problem (for example, the child problem is a social problem, then the target evaluation items of the target child are autism spectrum screening and comprehensive evaluation), and simultaneously, scheduling a preset therapist to evaluate based on the target evaluation items to obtain an evaluation result (for example, the child social disorder, language development retardation and suggestion of cognitive language treatment); the preset therapist is a prearranged medical staff responsible for the assessment of the child problem, and is responsible for different types of child problems and treatment courses. Based on the evaluation results, course management is performed (e.g., cognitive language therapy is performed on target children diagnosed with language retardation, for example, four 18:00-19:30 weekly).
According to the method, a big data technology is introduced, the child rehabilitation evaluation item library is constructed, and the comprehensiveness of construction of the child rehabilitation evaluation item library is improved; based on the evaluation result of therapist, the course arrangement management is directly carried out, the course arrangement management is not needed to be carried out manually, the labor cost is reduced, and the method is more intelligent.
In one embodiment, the obtaining module 1 obtains the child rehabilitation evaluation requirement of the input intelligent child rehabilitation management app, including:
acquiring the child rehabilitation evaluation requirement received by a preset reservation node;
and/or the number of the groups of groups,
acquiring an outpatient record input by an attendant of an outpatient center of a child rehabilitation institution into an outpatient system;
analyzing the outpatient records to obtain the children rehabilitation evaluation demands received by the attendant.
The working principle and the beneficial effects of the technical scheme are as follows:
there are two methods of acquisition for the need for rehabilitation assessment of children. The method comprises the following steps: the method comprises the steps of obtaining a child rehabilitation evaluation requirement received by a preset reservation node, wherein the preset reservation node is as follows: xx child rehabilitation institution reserves apps. The second method is as follows: an attendant at an outpatient center of the child rehabilitation facility is acquired to input an outpatient record of the outpatient system, which is entered by an attendant at the outpatient center (e.g., an outpatient nurse), including, but not limited to, outpatient time, interviewee, and child rehabilitation assessment needs.
The application introduces two ways to obtain the children rehabilitation evaluation requirement, and improves the comprehensiveness of the children rehabilitation evaluation requirement.
In one embodiment, the building module 2 builds a child rehabilitation evaluation item library based on big data, including:
based on big data, acquiring evaluation item information stored by a plurality of item resource access nodes and taking the evaluation item information as a pre-selection warehousing target;
performing qualification verification on the project resource access node;
if the qualification verification is passed, obtaining the access type when accessing the corresponding project resource access node;
if the access type is direct access, taking the corresponding pre-selected warehousing target as a first target to be warehoused;
if the access type is indirect access, acquiring a second target to be put in storage;
storing all the first objects to be put in storage and all the second objects to be put in storage into a preset empty database to finish construction;
the obtaining the second target to be put in storage includes:
obtaining the credibility of the indirectly accessed intermediate node;
and if the credibility is greater than or equal to a preset credibility threshold, taking the preselected warehousing target provided by the corresponding intermediate node as a second target to be warehoused.
The working principle and the beneficial effects of the technical scheme are as follows:
based on big data technology, acquiring evaluation item information stored by a plurality of item resource access nodes, wherein the item resource access nodes are as follows: network nodes of the assessment item sharing platform (the network nodes are in communication connection with the assessment item sharing platform) and/or personnel nodes of other child rehabilitation management institutions; the evaluation item information is: evaluation items (such as sensory comprehensive evaluation) and evaluation scales (such as SEQ scales), and large data technology belongs to the prior art and is not described in detail. However, the evaluation item information acquired based on the big data is not uniform in quality although it is comprehensive, and therefore, qualification verification is performed on the item resource access node, and if the verification is passed, the access type of the access item resource access node is determined, and the access type includes: direct access and indirect access, the direct access being: accessing assessment item information collected directly by local collection personnel (e.g., information collection personnel of the rehabilitation management institution), indirectly accessing as follows: evaluation item information collected by a non-local collector (e.g., collector of evaluation item information) is accessed. If the access type is direct access, directly taking the preselected warehousing target obtained by the access as a first target to be warehoused, if the access type is indirect access, obtaining the credibility (the higher the credibility is, the more credible the evaluation item information obtained by the access intermediate node is), the higher the credibility can be, according to the accuracy of the history providing record, the higher the credibility is, and if the credibility is greater than or equal to a preset credibility threshold (the credibility threshold is preset manually), the corresponding intermediate node provides the evaluation item information, and taking the corresponding preselected warehousing target as a second target to be warehoused. And storing all the first objects to be put in storage and all the second objects to be put in storage into a preset empty database (for example, the empty database) to finish construction.
According to the method, the project resource access node is introduced, qualification verification is conducted on the project resource access node, evaluation project information provided by the project resource access node passing verification is determined, usability of the evaluation project information is improved, meanwhile, targets to be put in storage are screened according to different access types, and construction efficiency of constructing the child rehabilitation evaluation project library is further improved.
In one embodiment, the construction module 2 performs qualification verification on the project resource access node, including:
obtaining a node type of a project resource access node, wherein the node type comprises: off-line nodes and network nodes;
when the node type is an off-line node, acquiring an evaluation effect value corresponding to an evaluation item provided by the off-line node;
if the effect value is greater than or equal to a preset effect value threshold, the corresponding project resource access node passes verification;
when the node type is a network node, acquiring network evaluation information of a user corresponding to the network node;
determining a user evaluation value based on the network evaluation information;
and if the user evaluation value is greater than or equal to a preset evaluation value threshold, the corresponding project resource access node passes verification.
The working principle and the beneficial effects of the technical scheme are as follows:
obtaining a node type of the project resource access node, wherein the node type comprises: off-line nodes (off-line rehabilitation authorities) and network nodes (network nodes of the assessment project sharing platform). When the node type is an offline node, an evaluation effect value corresponding to an evaluation item provided by the offline node is obtained (for example, an experienter is dispatched to visit a rehabilitation institution corresponding to the offline node, the experienter experiences an evaluation process corresponding to the evaluation item, then the evaluation effect value is determined, the larger the evaluation effect value is, the easier the corresponding item resource visit node passes qualification verification), and if the evaluation effect value is greater than or equal to a preset effect value threshold (for example, 95), the corresponding item resource visit node passes verification. If the node type is a network node, network evaluation information (such as comprehensive evaluation dimension and reasonable process) of a user evaluation area of the corresponding network node is obtained, a user evaluation value (the more positive the network evaluation information is, the higher the user evaluation value is) is determined based on the network evaluation information, and if the user evaluation value is greater than or equal to a preset evaluation value threshold (such as 94), the corresponding project resource access node passes verification.
According to the method and the device, the node types of the project resource access nodes are introduced, different qualification verification modes of the project resource access nodes are determined according to different node types, and verification rationality is improved.
In one embodiment, the course ranking module 4 performs course ranking management based on the evaluation result, including:
determining recommended course information of the target child based on the evaluation result;
pushing the evaluation result and the recommended course information to the guardian corresponding to the target child;
acquiring a target course selected from the recommended course information by the corresponding guardian, and associating the target course with the corresponding target child;
acquiring first class time information of the target course and idle time information of a corresponding target child;
based on the first lesson time information and the idle time information, determining lesson arrangement information;
and based on the course arrangement information, carrying out course arrangement management on the corresponding target children.
The working principle and the beneficial effects of the technical scheme are as follows:
based on the evaluation results, recommended lesson information (e.g., social disabilities of children, language retardation, suggested cognitive language treatment) for the target child is determined. Pushing the evaluation result and the recommended course information to a guardian corresponding to the target child (for example, sending the recommended information to a client of the corresponding guardian on the rehabilitation medical app), acquiring a target course selected by the guardian in the recommended course information (acquired by analyzing a course option table submitted by the corresponding guardian on the rehabilitation medical app), and associating the target course with the corresponding target child. First lesson time information (e.g., 18:00-19:30 weekly) for the target lesson and idle time information (e.g., 8:00-21:00 for monday through 17:40-21:00 for friday, saturday, and sunday) for the target child are obtained. Based on the first lesson-time information and the idle-time information, lesson-placement information is determined. Based on the lesson-setting information, lesson-setting management (e.g., scheduling when and what courses the target child learns) is performed.
According to the method and the device, the target course of the target child is determined, and the course arrangement information is determined based on the first class time information of the target course and the idle time information of the target child, so that the suitability of course arrangement management is improved.
In one embodiment, the course arrangement module 4 determines course arrangement information based on the first lesson time information and idle time information, including:
expanding the first class time information on a preset time axis, and determining a plurality of first class time periods;
expanding the idle time information on the time axis to determine a plurality of idle time periods;
sequentially traversing the first class time period from front to back, and taking the first class time period currently traversed as a second class time period;
judging whether the second class time period falls in any idle time period;
if so, taking the time corresponding to the second lesson time period of the current traversal as the target lesson time, and associating the target lesson time with the corresponding target child;
and taking the target courses and the target lesson time which are correspondingly associated with all target children as lesson arranging information.
The working principle and the beneficial effects of the technical scheme are as follows:
at present, when a rehabilitation center manages the lesson time of a target child, doctors or nurses usually communicate the lesson time with guardians of the target child and record and sort the lesson time, so that the management is complicated, and the solution is needed.
Expanding the first lesson time information on a preset time axis, and determining a plurality of first lesson time periods (for example, x month and x day 18:00-19:30, xx month and xx day 18:00-19:30, etc.); the preset time axis is a recording system manually preset. The idle time information is spread out on a time axis to determine a plurality of idle time periods (e.g., x month x day 17:40-21:00, xx month xx day 8:00-21:00, etc.). And traversing the first class time period from front to back in sequence, judging whether the second class time period currently traversed in the first class time period falls in any idle time period, if so, determining that the corresponding second class time period is the target class time of the target child, and taking the target class time associated with the corresponding target child and the target course associated with the target child together as class arrangement information of the corresponding target child.
According to the method and the device for determining the target children, the time axis is introduced, the first class time period on the time axis and the idle time period of the corresponding target children are determined, and the first class time period which is in the idle time period is used as the target class time of the corresponding target children, so that the rationality of determining the target class time is improved.
In one embodiment, the course arrangement module 4 determines whether the second lesson taking time period falls within any idle time period, including:
acquiring a first starting position and a first ending position of the second lesson-taking time period on the time axis, and simultaneously acquiring a second starting position of the idle time period on the time axis;
determining a corresponding second starting position which is positioned before the first starting position and closest to the first starting position in the second starting position, and taking the second starting position as a third starting position;
acquiring a second end position of the idle time period corresponding to the third start position on the time axis;
judging whether the first end position is before the second end position;
if yes, the second class time paragraphs fall in the corresponding idle time period.
The working principle and the beneficial effects of the technical scheme are as follows:
a first starting position (for example, a position corresponding to x month and x day 18:00 on a time axis) and a first ending position (for example, a position corresponding to x month and x day 19:30 on a time axis) of a second lesson time period are obtained, and meanwhile, a second starting position (for example, a position corresponding to x month and x day 17:40 on a time axis, a position corresponding to xx month and xx day 8:00 on a time axis) of an idle time period is obtained. In order to ensure that the corresponding target child can complete a lesson, a second start position, which is before and closest to the first start position, of the second start positions is acquired as a third start position. And acquiring a second end position of the idle time period corresponding to the third start position on the time axis. If the second ending position is before the first ending position, the idle time period corresponding to the target child cannot be completely used as the target class time, otherwise, the second class time period currently being traversed falls into the idle time period corresponding to the target child and can be used as the target class time.
According to the method and the device, the target class time is determined based on the first starting position, the first ending position and the second starting position and the second ending position of the idle time period, and the manual determination is not needed, so that the labor cost is greatly reduced.
In one embodiment, the big data based child rehabilitation management system further comprises:
and the adjusting module is used for acquiring the lesson taking behavior of the target child when the target child takes lessons and determining class performance scores of the target child based on the lesson taking behavior.
The working principle and the beneficial effects of the technical scheme are as follows:
when the lesson taking behavior of the target child is obtained, the lesson taking video of the target child in lesson can be obtained through a camera (such as a notebook computer and a front camera arranged on a smart phone) arranged on the intelligent terminal, the lesson taking behavior of the target child in lesson taking video is extracted based on a behavior extraction technology and a face recognition technology, the lesson taking behavior of the target child is determined based on the lesson taking behavior (such as the lesson taking behavior of a therapist, the lesson taking behavior of xxx is downwards regulated, and the behavior extraction technology and the face recognition technology belong to the prior art and are not repeated.
The class performance score of the target child is determined based on the class behavior of the target child, and the method and the device have applicability.
In one embodiment, the determining the class performance score of the target child based on the lesson-taking behavior includes:
acquiring initial performance scores of target children;
acquiring a behavior permission state of a target child when the target child generates a lesson behavior;
if the behavior permission state is not permission, acquiring a corresponding down-regulation coefficient;
if the behavior permission state is permission, acquiring a preset expression feature extraction template;
extracting characteristics of the lesson-taking behavior to obtain a plurality of characteristic expression values;
acquiring the expression weight of the expression type corresponding to the expression characteristic value;
determining an up-regulation coefficient based on the expression characteristic value and the expression weight;
and determining the class performance score of the target child based on the down-regulation coefficient, the up-regulation coefficient and the initial performance score.
The working principle and the beneficial effects of the technical scheme are as follows:
the initial performance score is manually preset, for example: 100 minutes. The behavior permission states are: whether the lesson teacher allows the target child to behave, for example: teacher asks xx to answer the question, then xx's behavior permission state is permission, and for example: if the teacher gives lessons normally, the behavior permission states of all students in the classroom are not permitted. If the behavior permission status is not permission and the target child generates behavior, the corresponding down-regulation coefficient (down-regulation coefficient
τ is the duration of the corresponding lesson behavior when the behavior permission status is not permission, μ 1 For the preset relation coefficient, generally, when the behavior permission state is not permitted, the longer the duration of the behavior, the more the downadjustments are, the smaller the corresponding downadjustment coefficient is, if the behavior permission state is permitted, the preset performance feature extraction template is obtained to perform feature extraction on the behavior in lessons, and a plurality of performance feature values are obtained (for example: what actions last for how long, what language of the language behaviorSense, etc.), the preset expression feature extraction template is: a template for extracting such feature values is preset. The performance types include, but are not limited to: performance, language performance, etc., and obtaining a performance weight of a performance type corresponding to the performance characteristic value, in general, the higher the relevance between the performance type and the course type, the greater the performance weight of the corresponding performance type, and the course type may be, for example: language classes, cognitive classes, behavioral classes, and the like. Based on the expression characteristic value and the expression weight, an up-regulation coefficient (up-regulation coefficient +.>
P i For the ith expression characteristic value, gamma i The expression weight for the ith expression feature value, n is the total number of expression feature values, μ 2 For a preset relationship coefficient), determining a class performance score for the target child based on the turndown coefficient, the turnup coefficient, and the initial performance score, the class performance score ∈>
The calculation formula of (2) is as follows:
wherein 0< d <1, u >1.
The invention determines class performance scores of the target children based on the class behavior of the target children when the target children are in class, more intuitively displays the class state of the target children, is beneficial to adjustment of subsequent courses and problem feedback, and is more intelligent.
The embodiment of the invention provides an intelligent child rehabilitation management method for basic big data, which is shown in fig. 2 and comprises the following steps:
step 1: obtaining a child rehabilitation assessment demand, the child rehabilitation assessment demand comprising: target children, child problems, and guardians;
step 2: constructing a child rehabilitation evaluation project library based on big data;
step 3: based on the child rehabilitation evaluation item library, determining target evaluation items of the target child according to the child problems, and simultaneously, scheduling a preset therapist to perform corresponding evaluation to obtain an evaluation result;
step 4: based on the evaluation result, course arrangement management is performed.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (3)
1. Intelligent child rehabilitation management system based on big data, characterized by comprising:
the acquisition module is used for acquiring the children rehabilitation evaluation requirement, and the children rehabilitation evaluation requirement comprises: target children, child problems, and guardians;
the construction module is used for constructing a child rehabilitation evaluation project library based on big data;
the determining module is used for determining target evaluation items of the target children based on the child rehabilitation evaluation item library according to the child problems, and simultaneously, scheduling a preset therapist to perform corresponding evaluation to obtain an evaluation result;
the course arrangement module is used for carrying out course arrangement management based on the evaluation result;
the construction module constructs a child rehabilitation evaluation project library based on big data, and the construction module comprises:
based on big data, acquiring evaluation item information stored by a plurality of item resource access nodes and taking the evaluation item information as a pre-selection warehousing target;
performing qualification verification on the project resource access node;
if the qualification verification is passed, obtaining the access type when accessing the corresponding project resource access node;
if the access type is direct access, taking the corresponding pre-selected warehousing target as a first target to be warehoused;
if the access type is indirect access, acquiring a second target to be put in storage;
storing all the first objects to be put in storage and all the second objects to be put in storage into a preset empty database to finish construction;
the construction module performs qualification verification on the project resource access node, and the construction module comprises the following steps:
obtaining a node type of a project resource access node, wherein the node type comprises: off-line nodes and network nodes;
when the node type is an off-line node, acquiring an evaluation effect value corresponding to an evaluation item provided by the off-line node;
if the effect value is greater than or equal to a preset effect value threshold, the corresponding project resource access node passes verification;
when the node type is a network node, acquiring network evaluation information of a user corresponding to the network node;
determining a user evaluation value based on the network evaluation information;
if the user evaluation value is greater than or equal to a preset evaluation value threshold, the corresponding project resource access node passes verification;
the course arrangement module performs course arrangement management based on the evaluation result, and includes:
determining recommended course information of the target child based on the evaluation result;
pushing the evaluation result and the recommended course information to the guardian corresponding to the target child;
acquiring a target course selected from the recommended course information by the corresponding guardian, and associating the target course with the corresponding target child;
acquiring first class time information of the target course and idle time information of a corresponding target child;
based on the first lesson time information and the idle time information, determining lesson arrangement information;
based on the course arrangement information, carrying out course arrangement management on the corresponding target children;
wherein, arrange the class module and confirm and arrange the class information based on first time information and idle time information, include:
expanding the first class time information on a preset time axis, and determining a plurality of first class time periods;
expanding the idle time information on the time axis to determine a plurality of idle time periods;
sequentially traversing the first class time period from front to back, and taking the first class time period currently traversed as a second class time period;
judging whether the second class time period falls in any idle time period;
if so, taking the time corresponding to the second lesson time period of the current traversal as the target lesson time, and associating the target lesson time with the corresponding target child;
taking the target courses and the target lesson time which are correspondingly associated with all target children as lesson arranging information;
the course arrangement module judges whether the second course time period falls in any idle time period, and comprises the following steps:
acquiring a first starting position and a first ending position of the second lesson-taking time period on the time axis, and simultaneously acquiring a second starting position of the idle time period on the time axis;
determining a corresponding second starting position which is positioned before the first starting position and closest to the first starting position in the second starting position, and taking the second starting position as a third starting position;
acquiring a second end position of the idle time period corresponding to the third start position on the time axis;
judging whether the first end position is before the second end position;
if yes, the second class time period is all within the idle time period;
wherein, an intelligent children rehabilitation management system based on big data still includes:
the adjusting module is used for acquiring the lesson taking behavior of the target child when the target child takes lessons and determining class performance scores of the target child based on the lesson taking behavior;
wherein the determining the class performance score of the target child based on the lesson behavior comprises:
acquiring initial performance scores of target children;
acquiring a behavior permission state of a target child when the target child generates a lesson behavior;
if the behavior permission state is not permission, acquiring a corresponding down-regulation coefficient;
if the behavior permission state is permission, acquiring a preset expression feature extraction template;
extracting characteristics of the lesson-taking behavior to obtain a plurality of characteristic expression values;
acquiring the expression weight of the expression type corresponding to the expression characteristic value;
determining an up-regulation coefficient based on the expression characteristic value and the expression weight;
and determining the class performance score of the target child based on the down-regulation coefficient, the up-regulation coefficient and the initial performance score.
2. The intelligent child rehabilitation management system based on big data of claim 1, wherein the obtaining module obtains the child rehabilitation evaluation requirement, comprising:
acquiring the child rehabilitation evaluation requirement received by a preset reservation node;
and/or the number of the groups of groups,
acquiring an outpatient record input by an attendant of an outpatient center of a child rehabilitation institution into an outpatient system;
analyzing the outpatient records to obtain the child rehabilitation evaluation requirements received by the attendant.
3. An intelligent child rehabilitation management method based on big data is characterized by comprising the following steps:
step 1: obtaining a child rehabilitation assessment demand, the child rehabilitation assessment demand comprising: target children, child problems, and guardians;
step 2: constructing a child rehabilitation evaluation project library based on big data;
step 3: based on the child rehabilitation evaluation item library, determining target evaluation items of the target child according to the child problems, and simultaneously, scheduling a preset therapist to perform corresponding evaluation to obtain an evaluation result;
step 4: based on the evaluation result, performing course arrangement management;
wherein, step 2: based on big data, the construction module constructs a child rehabilitation evaluation item library, comprising:
based on big data, acquiring evaluation item information stored by a plurality of item resource access nodes and taking the evaluation item information as a pre-selection warehousing target;
performing qualification verification on the project resource access node;
if the qualification verification is passed, obtaining the access type when accessing the corresponding project resource access node;
if the access type is direct access, taking the corresponding pre-selected warehousing target as a first target to be warehoused;
if the access type is indirect access, acquiring a second target to be put in storage;
storing all the first objects to be put in storage and all the second objects to be put in storage into a preset empty database to finish construction;
wherein, the performing qualification verification on the project resource access node includes:
obtaining a node type of a project resource access node, wherein the node type comprises: off-line nodes and network nodes;
when the node type is an off-line node, acquiring an evaluation effect value corresponding to an evaluation item provided by the off-line node;
if the effect value is greater than or equal to a preset effect value threshold, the corresponding project resource access node passes verification;
when the node type is a network node, acquiring network evaluation information of a user corresponding to the network node;
determining a user evaluation value based on the network evaluation information;
if the user evaluation value is greater than or equal to a preset evaluation value threshold, the corresponding project resource access node passes verification;
wherein, the step 4: based on the evaluation result, performing course arrangement management, including:
determining recommended course information of the target child based on the evaluation result;
pushing the evaluation result and the recommended course information to the guardian corresponding to the target child;
acquiring a target course selected from the recommended course information by the corresponding guardian, and associating the target course with the corresponding target child;
acquiring first class time information of the target course and idle time information of a corresponding target child;
based on the first lesson time information and the idle time information, determining lesson arrangement information;
based on the course arrangement information, carrying out course arrangement management on the corresponding target children;
wherein the determining course arrangement information based on the first course time information and the idle time information includes:
expanding the first class time information on a preset time axis, and determining a plurality of first class time periods;
expanding the idle time information on the time axis to determine a plurality of idle time periods;
sequentially traversing the first class time period from front to back, and taking the first class time period currently traversed as a second class time period;
judging whether the second class time period falls in any idle time period;
if so, taking the time corresponding to the second lesson time period of the current traversal as the target lesson time, and associating the target lesson time with the corresponding target child;
taking the target courses and the target lesson time which are correspondingly associated with all target children as lesson arranging information;
the judging whether the second class time period falls in any idle time period comprises the following steps:
acquiring a first starting position and a first ending position of the second lesson-taking time period on the time axis, and simultaneously acquiring a second starting position of the idle time period on the time axis;
determining a corresponding second starting position which is positioned before the first starting position and closest to the first starting position in the second starting position, and taking the second starting position as a third starting position;
acquiring a second end position of the idle time period corresponding to the third start position on the time axis;
judging whether the first end position is before the second end position;
if yes, the second class time period is all within the idle time period;
the intelligent child rehabilitation management method based on big data further comprises the following steps:
acquiring a lesson taking behavior of the target child when the target child takes lessons, and determining class performance scores of the target child based on the lesson taking behavior;
wherein the determining the class performance score of the target child based on the lesson behavior comprises:
acquiring initial performance scores of target children;
acquiring a behavior permission state of a target child when the target child generates a lesson behavior;
if the behavior permission state is not permission, acquiring a corresponding down-regulation coefficient;
if the behavior permission state is permission, acquiring a preset expression feature extraction template;
extracting characteristics of the lesson-taking behavior to obtain a plurality of characteristic expression values;
acquiring the expression weight of the expression type corresponding to the expression characteristic value;
determining an up-regulation coefficient based on the expression characteristic value and the expression weight;
and determining the class performance score of the target child based on the down-regulation coefficient, the up-regulation coefficient and the initial performance score.
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