CN115905559B - Knowledge graph construction method and device for field of care of mental retardation - Google Patents
Knowledge graph construction method and device for field of care of mental retardation Download PDFInfo
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Abstract
The invention provides a knowledge graph construction method and a device in the field of care of mental mishap, wherein the method comprises the following steps: acquiring historical care data of a person with impaired intelligence, and extracting initial data of a knowledge graph node from the historical care data based on a preset knowledge graph node; preprocessing the initial data to obtain target data of the knowledge graph nodes; determining a first association relationship between the knowledge graph nodes based on a first preset rule; determining a second association relationship between each knowledge graph node based on a second preset rule and the first association relationship; and obtaining a knowledge graph of the care of the loss of intelligence according to the target data and the second association relation. According to the invention, the knowledge graph of the care of the miscorsion person is constructed and obtained by acquiring the historical care data of the miscorsion person and combining with the preset rule, so that the efficiency of the care person for acquiring the care scheme according to the knowledge graph of the care of the miscorsion is improved.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to a knowledge graph construction method and device in the field of care of intelligence loss.
Background
Dementia, also known as dementia or cognitive disorder, is a syndrome that is characterized by impaired acquired cognitive function and is accompanied by a significant decline in daily life ability, work ability, learning ability and social interaction ability, and is often associated with behavioral, mental and personality abnormalities. As the aging process of the population increases, the number of elderly people with impaired intelligence increases year by year. The elderly with impaired intelligence face greater challenges in their care due to multiple aspects of capacity degradation or loss than the care of the average elderly. The problems related to the care of the malpractice are complex and various, the care of the malpractice old people has poor knowledge and skill for a long time, the traditional one-to-one guidance under the line can not meet the requirements, and along with the rapid development of the artificial intelligence, the knowledge graph technology is generated, so that a solving path is provided for realizing personalized remote support service. At present, the knowledge graph plays an important role in semantic search, intelligent question and answer, intelligent recommendation, intelligent prediction and the like, and is a fundamental guarantee of artificial intelligence application.
The existing medical knowledge graph mainly focuses on the prevention and treatment of diseases, but the related medical knowledge graph for disease care is less, and caregivers are difficult to acquire a care scheme through the existing medical knowledge graph, so that the determination efficiency of the care scheme is quite low.
Disclosure of Invention
The invention provides a knowledge graph construction method and a knowledge graph construction device in the field of care of mental retardation, which are used for solving the defects that the knowledge graph of traditional Chinese medicine is mainly focused on the prevention and treatment of diseases, the related knowledge graph of the care of diseases is less, and a caretaker is difficult to acquire a care scheme through the traditional knowledge graph of the medicine, so that the determination efficiency of the care scheme is quite low.
The invention provides a knowledge graph construction method in the field of care of mental retardation, which comprises the following steps:
acquiring historical care data of a person with impaired intelligence, and extracting initial data of a knowledge graph node from the historical care data based on a preset knowledge graph node;
preprocessing the initial data to obtain target data of the knowledge graph nodes;
determining a first association relationship between the knowledge graph nodes based on a first preset rule;
determining a second association relationship between each knowledge graph node based on a second preset rule and the first association relationship;
and obtaining a knowledge graph of the care of the loss of intelligence according to the target data and the second association relation.
According to the knowledge graph construction method in the care field of the loss of intelligence, the method for extracting the initial data of the knowledge graph nodes from the historical care data based on the preset knowledge graph nodes comprises the following steps:
and extracting initial data of the knowledge graph nodes from the historical care data based on the characteristics of the persons with poor intelligence, the care problems and the care schemes, wherein the characteristics of the persons with poor intelligence, the care problems and the care schemes are preset knowledge graph nodes.
According to the knowledge graph construction method in the care field of the loss of intelligence, the preprocessing is carried out on the initial data to obtain the target data of the knowledge graph node, and the knowledge graph construction method comprises the following steps:
and sequencing and screening the initial data according to the influence factors of the knowledge-graph nodes to obtain target data of the knowledge-graph nodes.
According to the knowledge graph construction method in the care field of the loss of intelligence, the first association relationship between the knowledge graph nodes is determined based on the first preset rule, and the knowledge graph construction method comprises the following steps:
if the knowledge graph nodes have the historical association relationship, a first initial association relationship between the knowledge graph nodes is obtained;
obtaining category nodes according to the categories of the knowledge graph nodes;
determining category association relations among the knowledge graph nodes according to the category nodes;
updating the first initial association relation according to the category association relation to obtain a first association relation between the knowledge graph nodes.
According to the knowledge graph construction method in the care field of the loss of intelligence provided by the invention, the second association relation between each knowledge graph node is determined based on the second preset rule and the first association relation, and the knowledge graph construction method comprises the following steps:
and adding, deleting and modifying the first association relation based on expert scores, and determining a second association relation between each knowledge graph node.
According to the method for constructing the knowledge graph in the care field of the loss of intelligence, the knowledge graph of the care of the loss of intelligence is obtained according to the target data and the second association relation, and the method comprises the following steps:
obtaining a first target file according to the target data;
obtaining a second target file according to the second association relation;
and writing the first target file and the second target file into a map database to obtain a knowledge map of the care of the loss of intelligence.
A knowledge graph construction device in the field of care of loss of intelligence, comprising:
the data acquisition module is used for acquiring historical care data of the person with the loss of intelligence, and extracting initial data of the knowledge graph nodes from the historical care data based on preset knowledge graph nodes;
the node data determining module is used for preprocessing the initial data to obtain target data of the knowledge graph nodes;
the association relation determining module is used for determining a first association relation between the knowledge graph nodes based on a first preset rule; determining a second association relationship between each knowledge graph node based on a second preset rule and the first association relationship;
and the knowledge graph construction module is used for obtaining the knowledge graph of the care of the loss of intelligence according to the target data and the second association relation.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the knowledge graph construction method in the care domain of the loss of intelligence when executing the program.
The present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a knowledge graph construction method of the care domain of misintelligence as described in any of the above.
The invention also provides a computer program product comprising a computer program which when executed by a processor implements the knowledge graph construction method of the care domain of loss of intelligence as described in any one of the above.
According to the knowledge graph construction method and device for the field of the care of the lost intelligence, the knowledge graph of the care of the lost intelligence is constructed by acquiring the historical care data of the personnel of the lost intelligence and combining the preset rules, and the efficiency of the caregivers for acquiring the care plan according to the knowledge graph of the care of the lost intelligence is improved.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a knowledge graph construction method in the field of care of mental retardation provided by the invention;
FIG. 2 is a schematic flow chart of S130 in FIG. 1 provided by the present invention;
FIG. 3 is a schematic flow chart of S150 in FIG. 1 provided by the present invention;
fig. 4 is a schematic structural diagram of the knowledge graph construction device in the field of care of mental retardation provided by the invention;
fig. 5 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the embodiments of the present application, it should be noted that, directions or positional relationships indicated by terms such as "center", "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., are based on those shown in the drawings, are merely for convenience in describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the embodiments of the present application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the embodiments of the present application, it should be noted that, unless explicitly specified and limited otherwise, the terms "connected," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium. The specific meaning of the terms in the embodiments of the present application will be understood by those of ordinary skill in the art in a specific context.
In the examples herein, a first feature "on" or "under" a second feature may be either the first and second features in direct contact, or the first and second features in indirect contact via an intermediary, unless expressly stated and defined otherwise. Moreover, a first feature being "above," "over" and "on" a second feature may be a first feature being directly above or obliquely above the second feature, or simply indicating that the first feature is level higher than the second feature. The first feature being "under", "below" and "beneath" the second feature may be the first feature being directly under or obliquely below the second feature, or simply indicating that the first feature is less level than the second feature.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the embodiments of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
FIG. 1 is a flow chart of a knowledge graph construction method in the field of care of mental retardation provided by the invention; referring to fig. 1, the invention provides a knowledge graph construction method in the field of care of mental mishap, comprising the following steps:
s110, acquiring historical care data of a person losing intelligence, and extracting initial data of a knowledge graph node from the historical care data based on a preset knowledge graph node;
s120, preprocessing the initial data to obtain target data of the knowledge graph nodes;
s130, determining a first association relationship between the knowledge-graph nodes based on a first preset rule;
s140, determining a second association relationship between each knowledge graph node based on a second preset rule and the first association relationship;
and S150, obtaining a knowledge graph of the care of the loss of intelligence according to the target data and the second association relation.
Optionally, in step S110, the historical care data of the misshapen person is a real care case.
Optionally, in step S120, the preset knowledge graph node is selected from a plurality of category keywords after classifying the historical care data, and at least includes a care plan, so that the caretaker can obtain the care plan and the care suggestion from the failure care knowledge graph later.
Optionally, in step S130, the initial data is preprocessed, which is favorable for improving efficiency and accuracy of subsequent knowledge graph construction.
Optionally, in step S140, the first preset rule is a rule for determining an association relationship between the knowledge-graph nodes, and the association between the knowledge-graph nodes may be established according to expert experience, and the knowledge-graph nodes and the association may be carded to obtain the first association relationship.
Optionally, in step S150, the second preset rule is a rule for modifying the first association relationship. And a plurality of disciplinary experts in the field of care for losing intelligence can be searched to review the first association relationship, and the first association relationship is modified to obtain a final second association relationship.
Alternatively, in step S160, a knowledge graph of care for loss of intelligence may be constructed based on the computer language Python.
It can be understood that the invention constructs the care knowledge graph of the miscorsion by acquiring the historical care data of the miscorsion person and combining with the preset rules, and is beneficial to improving the efficiency of the caregivers for acquiring the care scheme according to the care knowledge graph of the miscorsion.
On the basis of the foregoing embodiment, as an optional embodiment, the extracting, based on a preset knowledge-graph node, initial data of the knowledge-graph node from the historical care data includes:
and extracting initial data of the knowledge graph nodes from the historical care data based on the characteristics of the persons with poor intelligence, the care problems and the care schemes, wherein the characteristics of the persons with poor intelligence, the care problems and the care schemes are preset knowledge graph nodes.
Optionally, the initial data of the knowledge graph node includes the miswisdom person characteristic data, the care question data and the care plan data.
Wherein, the miswisdom person characteristic data comprises personalized characteristics such as gender, walking ability, hobbies, disease severity and the like of the miswisdom person. The caretaking problem data include caretaking problems of the mental retardation of the person in daily life (eating, washing, dressing, excreting, sleeping, communicating, etc.), mental activities (beating, curbing, destroying articles, east and west, hallucinations, delusions, apathy, etc.), and safety risks (falling, losing, hurting, mistaking, choking, pressure sores, etc.). The care plan data includes care plans corresponding to care questions according to personalized feature data of the mispeople.
Optionally, extracting the initial data of the knowledge graph node from the historical care data includes performing semi-automatic feature extraction on the historical care data to obtain the initial data of the knowledge graph node.
It can be appreciated that the invention provides a specific scheme of the preset knowledge graph nodes, and the individuation and the accuracy of the knowledge graph of the miscare person are improved by determining the individuation characteristics of the miscare person.
On the basis of the foregoing embodiment, as an optional embodiment, the preprocessing the initial data to obtain target data of the knowledge-graph node includes:
and sequencing and screening the initial data according to the influence factors of the knowledge-graph nodes to obtain target data of the knowledge-graph nodes.
Optionally, the node with the greatest influence on the care plan is selected from the knowledge graph nodes, for example, the characteristics of the person with the greatest influence on the care plan are selected, and then the personalized characteristics with the greatest influence on the person with the greatest influence on the care plan are selected from the characteristics of the person with the greatest influence on the care plan, and the personalized characteristics are used as knowledge graph node data together with the care questions and the care plan.
It can be understood that the invention can effectively reduce redundant data of the knowledge graph node data and improve the efficiency of the subsequent knowledge graph construction by preprocessing the initial data.
FIG. 2 is a schematic flow chart of S130 in FIG. 1 provided by the present invention; referring to fig. 2, on the basis of the foregoing embodiment, as an optional embodiment, the determining, based on a first preset rule, a first association relationship between the knowledge-graph nodes includes:
s210, if the knowledge-graph nodes have historical association relations, obtaining a first initial association relation between the knowledge-graph nodes;
s220, obtaining category nodes according to the categories of the knowledge graph nodes;
s230, determining category association relations among the knowledge graph nodes according to the category nodes;
and S240, updating the first initial association relation according to the category association relation to obtain a first association relation between the knowledge graph nodes.
Optionally, in step S210, if the knowledge graph node has a history association relationship, the history association relationship is used as a first initial association relationship.
Optionally, in step S220, a category node is established for the same class of impaired people features, the same class of care questions and the same class of care suggestions.
Optionally, in step S240, it is determined whether there is no association relationship in the first initial association relationship in the category association relationship, if there is a relationship in the category association relationship, the category association relationship is added to the first initial association relationship, and if there is a relationship in the category association relationship that is opposite to the first initial association relationship, the first initial association relationship is corrected based on the category association relationship, so as to obtain the first association relationship between the knowledge graph nodes.
It can be understood that the invention can conveniently establish the association relation between the knowledge graph nodes by establishing the category nodes, thereby improving the accuracy of the knowledge graph of the care for the misintelligence.
On the basis of the foregoing embodiment, as an optional embodiment, the determining, based on a second preset rule and the first association relationship, a second association relationship between each of the knowledge-graph nodes includes:
and adding, deleting and modifying the first association relation based on expert scores, and determining a second association relation between each knowledge graph node.
Optionally, the second preset rule includes adding and deleting and modifying the first association relationship based on expert scores. The expert score is a preset score.
Optionally, a plurality of multidisciplinary specialists in the field of care for loss of intelligence can be searched for to review and modify the relationship between nodes.
It can be understood that the accuracy of the association relationship can be improved by adding, deleting and modifying the first association relationship through expert scoring.
FIG. 3 is a schematic flow chart of S150 in FIG. 1 provided by the present invention; referring to fig. 3, on the basis of the foregoing embodiment, as an alternative embodiment, the obtaining a knowledge graph of care for loss of intelligence according to the target data and the second association relationship includes:
s310, obtaining a first target file according to the target data;
s320, obtaining a second target file according to the second association relation;
and S330, writing the first target file and the second target file into a map database to obtain a knowledge map of the care for the loss of intelligence.
Optionally, the knowledge graph nodes may be numbered, target data corresponding to the knowledge graph nodes is input into a CSV (common-Separated Values, comma Separated Values or symbol Separated Values) file, and a second association relationship between the knowledge graph nodes is input into the CSV file through the number.
Optionally, the Neo4j package in Python is used to read the node data and the inter-node connection data in the CSV file, and write the data into the Neo4j graph database, and Neo4j is used as a carrier of the knowledge graph.
Optionally, the computer language Python is used for realizing the screening and the inquiry of the relation among the knowledge graph nodes, the returned data is the care plan data of dictionary type, the data is submitted to the java background service, and the data is transmitted to the front end to be displayed to the caretaker. Meanwhile, by drawing the knowledge graph through echart, a manager of the knowledge graph can intuitively see the connection between the framework of the knowledge graph and the nodes.
It can be understood that the invention constructs the knowledge graph of the care field of the person with no intelligence by means of computer technology, and the constructed knowledge graph not only contains structured knowledge, but also fully considers the personalized characteristics of the person with no intelligence. The knowledge graph constructed by the invention lays a solid foundation for solving the embarrassment that the caretaker of the person with lost intelligence can not find when seeking the care plan, and is beneficial to improving the efficiency of the caretaker to obtain the care plan, saving the care time and reducing the care cost.
The knowledge graph construction device for the care domain of the misconvergence provided by the invention is described below, and the knowledge graph construction device for the care domain of the misconvergence and the knowledge graph construction method for the care domain of the misconvergence described below can be correspondingly referred to each other.
Fig. 4 is a schematic structural diagram of the knowledge graph construction device in the field of care of mental retardation provided by the invention; referring to fig. 4, the invention further provides a knowledge graph construction device in the field of care of intelligence loss, which comprises:
a data acquisition module 410, configured to acquire historical care data of a person with a loss of intelligence, and extract initial data of a knowledge graph node from the historical care data based on a preset knowledge graph node;
the node data determining module 420 is configured to pre-process the initial data to obtain target data of the knowledge-graph node;
the association determining module 430 is configured to determine a first association between the knowledge-graph nodes based on a first preset rule; determining a second association relationship between each knowledge graph node based on a second preset rule and the first association relationship;
the knowledge graph construction module 440 is configured to obtain a knowledge graph of care for loss of intelligence according to the target data and the second association relationship.
In one embodiment, the data acquisition module 410 is further configured to:
and extracting initial data of the knowledge graph nodes from the historical care data based on the characteristics of the persons with poor intelligence, the care problems and the care schemes, wherein the characteristics of the persons with poor intelligence, the care problems and the care schemes are preset knowledge graph nodes.
In one embodiment, the node data determination module 420 is further configured to:
and sequencing and screening the initial data according to the influence factors of the knowledge-graph nodes to obtain target data of the knowledge-graph nodes.
In one embodiment, the association determining module 430 is further configured to:
if the knowledge graph nodes have the historical association relationship, a first initial association relationship between the knowledge graph nodes is obtained;
obtaining category nodes according to the categories of the knowledge graph nodes;
determining category association relations among the knowledge graph nodes according to the category nodes;
updating the first initial association relation according to the category association relation to obtain a first association relation between the knowledge graph nodes.
In one embodiment, the association determining module 430 is further configured to:
and adding, deleting and modifying the first association relation based on expert scores, and determining a second association relation between each knowledge graph node.
In one embodiment, the knowledge graph construction module 440 is further configured to:
obtaining a first target file according to the target data;
obtaining a second target file according to the second association relation;
and writing the first target file and the second target file into a map database to obtain a knowledge map of the care of the loss of intelligence.
Fig. 5 illustrates a physical schematic diagram of an electronic device, as shown in fig. 5, which may include: processor 510, communication interface (Communications Interface) 520, memory 530, and communication bus 540, wherein processor 510, communication interface 520, memory 530 complete communication with each other through communication bus 540. Processor 510 may invoke logic instructions in memory 530 to perform a care domain knowledge graph construction method that includes:
acquiring historical care data of a person with impaired intelligence, and extracting initial data of a knowledge graph node from the historical care data based on a preset knowledge graph node;
preprocessing the initial data to obtain target data of the knowledge graph nodes;
determining a first association relationship between the knowledge graph nodes based on a first preset rule;
determining a second association relationship between each knowledge graph node based on a second preset rule and the first association relationship;
and obtaining a knowledge graph of the care of the loss of intelligence according to the target data and the second association relation.
Further, the logic instructions in the memory 530 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, where the computer program product includes a computer program, where the computer program can be stored on a non-transitory computer readable storage medium, and when the computer program is executed by a processor, the computer can execute the method for constructing a knowledge graph of a care domain of failure provided by the above methods, and the method includes:
acquiring historical care data of a person with impaired intelligence, and extracting initial data of a knowledge graph node from the historical care data based on a preset knowledge graph node;
preprocessing the initial data to obtain target data of the knowledge graph nodes;
determining a first association relationship between the knowledge graph nodes based on a first preset rule;
determining a second association relationship between each knowledge graph node based on a second preset rule and the first association relationship;
and obtaining a knowledge graph of the care of the loss of intelligence according to the target data and the second association relation.
In still another aspect, the present invention further provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the method for constructing a knowledge graph in a care field of loss of intelligence provided by the above methods, the method comprising:
acquiring historical care data of a person with impaired intelligence, and extracting initial data of a knowledge graph node from the historical care data based on a preset knowledge graph node;
preprocessing the initial data to obtain target data of the knowledge graph nodes;
determining a first association relationship between the knowledge graph nodes based on a first preset rule;
determining a second association relationship between each knowledge graph node based on a second preset rule and the first association relationship;
and obtaining a knowledge graph of the care of the loss of intelligence according to the target data and the second association relation.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. The knowledge graph construction method in the field of care of loss of intelligence is characterized by comprising the following steps:
acquiring historical care data of a person with impaired intelligence, and extracting initial data of a knowledge graph node from the historical care data based on a preset knowledge graph node;
preprocessing the initial data to obtain target data of the knowledge graph nodes;
determining a first association relationship between the knowledge graph nodes based on a first preset rule;
determining a second association relationship between each knowledge graph node based on a second preset rule and the first association relationship;
obtaining a knowledge graph of the care of the loss of intelligence according to the target data and the second association relation;
the determining, based on a first preset rule, a first association relationship between the knowledge-graph nodes includes:
if the knowledge graph nodes have the historical association relationship, a first initial association relationship between the knowledge graph nodes is obtained;
obtaining category nodes according to the categories of the knowledge graph nodes;
determining category association relations among the knowledge graph nodes according to the category nodes;
updating the first initial association according to the category association to obtain a first association between the knowledge graph nodes; judging whether the category incidence relation exists an incidence relation which is not in the first initial incidence relation or not, if so, adding the category incidence relation into the first initial incidence relation, and if so, correcting the first initial incidence relation by taking the category incidence relation as the criterion to obtain a first incidence relation between the knowledge graph nodes;
numbering the knowledge graph nodes, inputting target data corresponding to the knowledge graph nodes into a CSV file, inputting a second association relation between the knowledge graph nodes into the CSV file through the numbering, reading node data and inter-node connection data in the CSV file by using a Neo4j packet in Python and writing the data into a Neo4j graph database, using Neo4j as a carrier of the knowledge graph, using computer language Python to screen and inquire the relation between the knowledge graph nodes, returning the data as dictionary type care plan data, submitting the data to java background service and transmitting the data to the front end for display to a carer, and drawing the knowledge graph through echart so that a knowledge graph manager can check the relation between the framework of the knowledge graph and the nodes.
2. The knowledge-graph construction method of the field of care for loss of intelligence according to claim 1, wherein the extracting initial data of the knowledge-graph node from the historical care data based on the preset knowledge-graph node comprises:
and extracting initial data of the knowledge graph nodes from the historical care data based on the characteristics of the persons with poor intelligence, the care problems and the care schemes, wherein the characteristics of the persons with poor intelligence, the care problems and the care schemes are preset knowledge graph nodes.
3. The knowledge graph construction method of the care domain of loss of intelligence according to claim 1, wherein the preprocessing the initial data to obtain target data of the knowledge graph node comprises:
and sequencing and screening the initial data according to the influence factors of the knowledge-graph nodes to obtain target data of the knowledge-graph nodes.
4. The method for constructing a knowledge graph in the field of care of mental loss according to claim 1, wherein the determining a second association relationship between each of the knowledge graph nodes based on a second preset rule and the first association relationship comprises:
and adding, deleting and modifying the first association relation based on expert scores, and determining a second association relation between each knowledge graph node.
5. The method for constructing a knowledge graph in the field of care of loss of intelligence according to claim 1, wherein the obtaining the knowledge graph in the field of care of loss of intelligence according to the target data and the second association relation comprises:
obtaining a first target file according to the target data;
obtaining a second target file according to the second association relation;
and writing the first target file and the second target file into a map database to obtain a knowledge map of the care of the loss of intelligence.
6. The utility model provides a knowledge graph construction device in careless field of care, its characterized in that includes:
the data acquisition module is used for acquiring historical care data of the person with the loss of intelligence, and extracting initial data of the knowledge graph nodes from the historical care data based on preset knowledge graph nodes;
the node data determining module is used for preprocessing the initial data to obtain target data of the knowledge graph nodes;
the association relation determining module is used for determining a first association relation between the knowledge graph nodes based on a first preset rule; determining a second association relationship between each knowledge graph node based on a second preset rule and the first association relationship;
the knowledge graph construction module is used for obtaining a knowledge graph of the care of the loss of intelligence according to the target data and the second association relation;
the determining, based on a first preset rule, a first association relationship between the knowledge-graph nodes includes:
if the knowledge graph nodes have the historical association relationship, a first initial association relationship between the knowledge graph nodes is obtained;
obtaining category nodes according to the categories of the knowledge graph nodes;
determining category association relations among the knowledge graph nodes according to the category nodes;
updating the first initial association according to the category association to obtain a first association between the knowledge graph nodes; judging whether the category incidence relation exists an incidence relation which is not in the first initial incidence relation or not, if so, adding the category incidence relation into the first initial incidence relation, and if so, correcting the first initial incidence relation by taking the category incidence relation as the criterion to obtain a first incidence relation between the knowledge graph nodes;
numbering the knowledge graph nodes, inputting target data corresponding to the knowledge graph nodes into a CSV file, inputting a second association relation between the knowledge graph nodes into the CSV file through the numbering, reading node data and inter-node connection data in the CSV file by using a Neo4j packet in Python and writing the data into a Neo4j graph database, using Neo4j as a carrier of the knowledge graph, using computer language Python to screen and inquire the relation between the knowledge graph nodes, returning the data as dictionary type care plan data, submitting the data to java background service and transmitting the data to the front end for display to a carer, and drawing the knowledge graph through echart so that a knowledge graph manager can check the relation between the framework of the knowledge graph and the nodes.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of knowledge graph construction in the field of care of loss of intelligence as claimed in any one of claims 1 to 5 when the program is executed by the processor.
8. A non-transitory computer-readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the knowledge graph construction method of the care field of loss of intelligence as claimed in any one of claims 1 to 5.
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