CN115718797A - Sleep disorder information processing method, device and system - Google Patents
Sleep disorder information processing method, device and system Download PDFInfo
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Abstract
The invention discloses an information processing method, a device and a system for sleep disorder, which are used for acquiring service data, performing structured processing, setting fuzzy logic, establishing a corresponding evaluation system according to a fuzzification concept input by a service expert, establishing at least one mapping relation table of the fuzzification concept and a fuzzy logic judgment result, establishing a neural network model through a deep learning algorithm, performing driving processing on the fuzzy logic, establishing a knowledge map and classifying by combining experience knowledge of the service expert, reasoning and describing the fuzzification concept, iterating the results and the description results into the model to obtain a final judgment basis, processing service requirements to obtain and output results, and can effectively improve the decision efficiency of the service expert and give consideration to the decision accuracy without a large number of existing samples.
Description
The application is a divisional application of a Chinese patent application with an application date of 2021, 8 and 23 months and an application number of CN202110964802.4, and is named as a fuzzy logic-based business processing method, a fuzzy logic-based business processing device and a fuzzy logic-based business processing system.
Technical Field
The invention relates to the field of artificial intelligence, in particular to a sleep disorder information processing method, device and system.
Background
With the development of internet technology, big data application is more and more popular, and various unstructured data are already in huge quantities, and the specificity and field specialty of many data used in the field need to be judged and evaluated by expert personal experience, but most of the work in the data can be solved by an intelligent system, so that a new technology is urgently needed to improve the efficiency of auxiliary processing.
Disclosure of Invention
Aiming at the defects, the technical problem to be solved by the invention is how to learn the experience of experts by an intelligent scientific and technological means, solve the problem of a large amount of primary judgment and decision making of the experts, reduce the burden of the experts and improve the overall business processing efficiency.
In view of the above-mentioned drawbacks, an object of the present invention is to provide a sleep disorder information processing method, system, and electronic device, computer storage medium, and program product.
According to an aspect of the embodiments of the present specification, there is provided an information processing method for sleep disorders, which is used at a server side, and is configured to obtain causes of diseases, examination data of patients, and other event data, and perform structured processing, where the examination data includes text data and picture data, the text data and the picture data are identified and extracted through an NLP algorithm, an image recognition algorithm, or deep learning, and a neural network model is constructed, fuzzy logic is set, the data are classified, different classifications correspond to different dimensions and indexes, the dimensions and the indexes correspond to nodes in fuzzification logic, the different nodes have priorities, the dimensions and the indexes of an evaluation system are classified and layered according to different business requirements, mapping relationships still exist between the dimensions and the indexes, the different hierarchies correspond to different membership equations, expert experience is mainly dispersed for the classification mode of the index, different business judgment methods are adopted for different business characteristics, an evaluation system corresponding to the fuzzification concepts input by business experts is established, at least one mapping relationship table of the fuzzification concepts and fuzzy logic judgment results is established, the neural network model is constructed through the deep learning algorithm, the fuzzy logic is driven by the fuzzy concepts, the fuzzy concepts are combined with the fuzzification concepts, the fuzzy concepts are described in the business classification results, and the business classification results are obtained by the expert reasoning, and the business classification results are described according to the business classification results, and the business classification results are described, and the business classification results are obtained by the business classification results.
Preferably, the evaluation system establishes relevant dimensions and indexes, and different dimensions and indexes form a network information distribution.
Preferably, the indexes comprise a good grade, a medium grade and a poor grade.
Preferably, the nodes are distributed according to layers, and the nodes with high priority are preferentially defuzzified in the fuzzy logic reasoning process.
The invention provides an information processing method of sleep disorder, which is applied to an internet medical platform, collects etiology of diseases input by a user, inspection data of patients and other event data, carries out structured processing to obtain standardized data, the inspection data comprises text data and picture data, the text data and the picture data are identified and extracted through an NLP algorithm or an image recognition algorithm or deep learning, a neural network model is constructed, fuzzy logic is set, the data are classified, different classifications correspond to different dimensions and indexes, the dimensions and the indexes correspond to nodes in fuzzification logic, the different nodes have priorities, the dimensions and the indexes of the evaluation system are classified and layered according to different service requirements, mapping relations also exist between the dimensions and the indexes, the different hierarchies correspond to different membership equations, reasoning and dispersion processing are carried out on expert experience for the index modes of the classifications, different service characteristics adopt different service judgment methods, node information is input to a back-end server, the corresponding evaluation system and fuzzification concept mapping relation table are established through the fuzzification concept input by back-end server service experts, knowledge is constructed through the deep learning algorithm, knowledge processing is carried out on fuzzy logic driving the fuzzy knowledge processing, and the service model is described according to the service classification results, and the service classification results are finally output to the user classification service models.
The invention provides an information processing system for sleep disorder, which comprises a server, a client and an internet medical platform,
the user submits the information through the client terminal,
the internet medical platform collects etiology of diseases input by a user, inspection data of patients and other event data are subjected to structured processing to be standardized data, the inspection data comprise text data and picture data, the text data and the picture data are identified and extracted through an NLP algorithm or an image recognition algorithm or deep learning, a neural network model is constructed, fuzzy logic is set, the data are classified, different classifications correspond to different dimensions and indexes, the dimensions and the indexes correspond to nodes in the fuzzy logic, priorities are arranged among the different nodes, the dimensions and the indexes of an evaluation system are classified and layered according to different business requirements, mapping relations also exist among the dimensions and the indexes, different levels correspond to different membership equations, the grading index mode mainly comprises dispersing expert experience, different business judgment methods are adopted according to different business characteristics, and node information is input to a rear-end server;
the back-end server establishes a corresponding evaluation system and a mapping relation table of fuzzy concepts and fuzzy logic judgment results for the fuzzy concepts input by the service experts, establishes a neural network model through a deep learning algorithm, performs driving processing on the fuzzy logic, establishes a knowledge map and classifies according to experience knowledge of the service experts, infers and describes the fuzzy concepts, iterates inference and description results into the model to obtain a final judgment basis, processes service requirements to obtain an output result and feeds the output result back to the Internet medical platform.
Preferably, the evaluation system establishes relevant dimensions and indexes, and different dimensions and indexes form a network information distribution.
The present invention provides an electronic device, including:
a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to:
the method comprises the steps of obtaining etiology of diseases, examination data of patients and other event data and conducting structural processing, wherein the examination data comprises text data and picture data, the text data and the picture data are identified and extracted through an NLP algorithm or an image identification algorithm or deep learning, a neural network model is built, fuzzy logic is set, the data are classified, different classifications correspond to different dimensions and indexes, the dimensions and the indexes correspond to nodes in fuzzy logic, priorities are arranged among different nodes, the dimensions and the indexes of an evaluation system are classified and layered according to different service requirements, mapping relations also exist among the dimensions and the indexes, different hierarchies correspond to different membership equations, the grading index mode mainly comprises the step of conducting decentralized processing on expert experience, different service judgment methods are adopted for different service characteristics, a corresponding reasoning evaluation system is built according to fuzzy concepts input by experts, at least one fuzzy concept and fuzzy logic judgment result mapping relation table is built, the neural network model is built through the deep learning algorithm, the fuzzy logic is driven to conduct iterative processing on expert processing results obtained by combining service experience knowledge, and classification, knowledge concepts are conducted on the fuzzy model, and final judgment results are output according to the requirements on service judgment results.
The present invention provides a computer readable storage medium having stored thereon a computer program/instructions, characterized in that the computer program/instructions, when executed by a processor, implement the steps of the above-mentioned method.
The method comprises the steps of obtaining service data, carrying out structured processing, setting fuzzy logic, establishing a corresponding evaluation system according to fuzzification concepts input by service experts, establishing at least one fuzzification concept and fuzzy logic judgment result mapping relation table, establishing a neural network model through a deep learning algorithm, carrying out driving processing on the fuzzy logic, establishing a knowledge map and classifying by combining experience knowledge of the service experts, reasoning and describing the fuzzification concepts, iterating the reasoning and description results into the model to obtain a final judgment basis, processing service requirements to obtain and output results, effectively improving the decision efficiency of the service experts on the premise of not needing a large number of existing samples, and considering the accuracy of decision.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of an embodiment of a sleep disorder information processing method according to the present invention;
FIG. 2 is a flowchart illustrating an embodiment of a sleep disorder information processing method according to the present invention;
FIG. 3 is a flow diagram illustrating an embodiment of fuzzy logic of an information processing method for sleep disorders of the present invention;
FIG. 4 is a flow chart of an artificial intelligence embodiment of the sleep disorder information processing method of the present invention;
FIG. 5 is a flow chart of an embodiment of the sleep disorder information processing system of the present invention;
FIG. 6 is a flow chart of an embodiment of the external output of the sleep disorder information processing system of the present invention.
Detailed Description
Features of various aspects and exemplary embodiments of the present invention will be described in detail below, and in order to make objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and the embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It will be apparent to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising 8230; \8230;" comprises 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
In some service scenes, it is necessary to perform manual experience to assist judgment, such as manual interpretation of remote sensing images, and according to the requirements of each specialty (department), the interpretation marks and practical experience and knowledge are used to identify targets from the remote sensing images, qualitatively and quantitatively extract the relevant information of the distribution, structure, function, etc. of the targets, and represent them on the geographic base map. For example, the interpretation of the current land utilization situation is to identify land utilization types on the image and then measure and calculate various land areas on the image. Visual interpretation of remote sensed images is the process by which the interpreter identifies the desired surface feature information by direct observation or with the aid of some simple tool (e.g., magnifying glasses, etc.).
For example, in medical diagnosis in the medical field, especially in sleep disorder, the symptoms of each patient are different, some are caused by diseases, some are caused by life habits, and some are related to ingested substances, and a large amount of basic work is required before the judgment of experts, including pathological examination, physical examination, condition investigation, and the like. These expert-based decisions may rely on insufficient historical samples, require excessive manual intervention and analysis, and require both time and labor costs.
As shown in fig. 1, an embodiment of the present specification provides an information processing method for sleep disorders, which is used at a server side, acquires service data and performs structuring processing, sets fuzzy logic, establishes a corresponding evaluation system according to a fuzzification concept input by a service expert, establishes at least one mapping relation table between the fuzzification concept and a fuzzy logic judgment result, establishes a neural network model through a deep learning algorithm, performs driving processing on the fuzzy logic, establishes a knowledge graph and classifies according to experience knowledge of the service expert, infers and describes the fuzzification concept, iterates inference and description results into the model to obtain a final judgment basis, and processes service requirements to obtain and output results.
In some specific examples, the evaluation system establishes relevant dimensions and indicators, and different dimensions and indicators form a mesh information distribution.
The dimension and the index of the evaluation system are graded and layered according to different business requirements, and a mapping relation also exists between the dimension and the index.
In some possible embodiments, the index includes three levels of "good", "middle" and "poor", and may also be finer in granularity, including five levels of "very good", "middle" and "poor", in a specific implementation process, different levels correspond to different membership equations, and the index manner for rating is mainly to disperse expert experience. And adopting different service judgment methods for different service characteristics.
In some possible embodiments, the business data includes the cause of the disease, examination data of the patient, and other event data.
In some specific embodiments, the different dimensions and metrics form nodes of the fuzzy logic process, with priorities between different nodes. The settings by the nodes can be used for processing in the algorithmic model. The nodes are distributed according to layers, and the high priority is preferentially defuzzification in the fuzzy logic reasoning process.
An embodiment of the present specification provides an information processing method for sleep disorders, which is applied to an internet medical platform, and includes collecting information input by a user, performing structured processing to obtain standardized data, setting fuzzy logic, classifying the data, wherein different classifications correspond to different dimensions and indexes, establishing nodes in the fuzzy logic correspondingly according to the dimensions and the indexes, inputting node information to a back-end server, establishing a corresponding evaluation system and a mapping relation table of the fuzzy concept and a fuzzy logic judgment result according to a fuzzy concept input by a back-end server service expert, establishing a neural network model according to a deep learning algorithm, performing driving processing on the fuzzy logic, establishing a knowledge graph and classifying according to experience knowledge of the service expert, reasoning and describing the fuzzy concept, iterating the reasoning and description results into the model to obtain a final judgment basis, and processing service requirements to obtain and output results to the user.
In some embodiments, the business data includes the sharpness of the remote sensing image, the acquisition mode, the image path, the weather conditions, and other event data.
In some embodiments, other event data includes, but is not limited to, imaging modality (such as multispectral, radar imagery), imaging time, historical meteorological conditions.
In some embodiments, the business data includes the cause of the disease, examination data of the patient, and other event data.
In some embodiments, the inspection data includes text data and picture data. The text data is identified and extracted through algorithms such as NLP (non line of sight) and the like, and the picture data is identified and extracted through an image identification algorithm.
In some embodiments, other event data includes, but is not limited to, whether to ingest caffeine, whether to drink alcohol, whether to inject a sensitive medication.
As shown in fig. 2, an embodiment of the present specification provides an information processing method for sleep disorders, including:
s101, acquiring service data through methods such as NLP (non line of sight), image recognition algorithm, deep learning and the like;
s102, setting fuzzy logic and generating a reasoning graph;
s103, establishing a knowledge graph and classifying by combining the experience knowledge of the service experts;
s104, reasoning and describing the fuzzy concept;
s105, combining the training samples to construct a neural network model;
and S106, iterating the reasoning and describing results into the model to obtain a final judgment basis, and processing the service requirement to obtain and output a result.
As shown in FIG. 3, in some embodiments, constructing fuzzy logic includes the steps of:
s201, setting fuzzy logic;
s202, generating a fuzzy logic inference graph;
s203, calculating fuzzy logic reasoning;
and S204, judging and fuzzifying the setting data.
As shown in fig. 4, in some embodiments, deep learning includes the steps of:
s301, acquiring data through technologies such as image recognition and NLP;
s302, constructing a neural network model;
s303, judging through an expert experience rule;
and S304, classifying through a knowledge graph.
An embodiment of the present specification provides an information processing system for sleep disorder, which includes a server, a client and an internet service platform,
the user submits the information through the client-side,
the Internet service platform collects information input by a user, carries out structuralization processing on the information into standardized data, sets fuzzy logic, classifies the data, corresponds to different dimensions and indexes according to different classifications, establishes nodes in the fuzzy logic according to the dimensions and the indexes, and inputs node information to a backend server;
the back-end server establishes a corresponding evaluation system and a fuzzy concept and fuzzy logic judgment result mapping relation table for the fuzzy concept input by the service expert, establishes a neural network model through a deep learning algorithm, drives and processes the fuzzy logic, establishes a knowledge map and classifies the knowledge map in combination with experience knowledge of the service expert, infers and describes the fuzzy concept, iterates the inference and description results into the model to obtain a final judgment basis, processes service requirements to obtain an output result and feeds the output result back to the Internet service platform;
and the Internet service platform pushes the result to the user client.
In some embodiments, the evaluation system in the system establishes relevant dimensions and indexes, and different dimensions and indexes form a network information distribution.
In some embodiments, the system in-dimension includes sleep status, physical examination data, family genetic history, ingested material, and lifestyle habits.
As shown in fig. 4, an information processing system for sleep disorders according to an embodiment of the present disclosure includes a fuzzy logic subsystem and a deep learning subsystem, where the fuzzy logic subsystem includes a fuzzy logic setting module, a fuzzy logic inference graph generating module, a fuzzy logic inference calculation module, and a data judgment fuzzification processing module, and the deep learning subsystem includes an image recognition module, a neural network model building module, an expert experience rule judgment module, and a knowledge graph classification module.
As shown in fig. 5, the system further includes an AI driving module, a defuzzification processing module, and a business decision output module.
One embodiment of the present specification provides a computer readable storage medium having a computer program/instructions stored thereon, wherein the computer program/instructions, when executed by a processor, implement the steps of:
acquiring service data and carrying out structured processing, setting fuzzy logic, establishing a corresponding evaluation system according to a fuzzification concept input by a service expert, establishing at least one fuzzification concept and fuzzy logic judgment result mapping relation table, establishing a neural network model through a deep learning algorithm, carrying out drive processing on the fuzzy logic, establishing a knowledge map and classifying by combining experience knowledge of the service expert, reasoning and describing the fuzzification concept, iterating the reasoning and description result into the model to obtain a final judgment basis, and processing the service requirement to obtain and output a result.
One embodiment of the present specification provides a computer program product comprising computer programs/instructions that when executed by a processor implement the steps of:
acquiring service data and performing structured processing, setting fuzzy logic, establishing a corresponding evaluation system according to a fuzzification concept input by a service expert, establishing at least one fuzzification concept and fuzzy logic judgment result mapping relation table, establishing a neural network model through a deep learning algorithm, performing driving processing on the fuzzy logic, establishing a knowledge map and classifying by combining experience knowledge of the service expert, reasoning and describing the fuzzification concept, iterating the reasoning and description result into the model to obtain a final judgment basis, and processing the service requirement to obtain and output a result.
One embodiment of the present specification provides an electronic apparatus including:
a processor; and
a memory arranged to store computer-executable instructions that, when executed, cause the processor to:
acquiring service data and performing structured processing, setting fuzzy logic, establishing a corresponding evaluation system according to a fuzzification concept input by a service expert, establishing at least one fuzzification concept and fuzzy logic judgment result mapping relation table, establishing a neural network model through a deep learning algorithm, performing driving processing on the fuzzy logic, establishing a knowledge map and classifying by combining experience knowledge of the service expert, reasoning and describing the fuzzification concept, iterating the reasoning and description result into the model to obtain a final judgment basis, and processing the service requirement to obtain and output a result.
One embodiment of the present specification provides a computer-readable storage medium having a computer program/instructions stored thereon, wherein the computer program/instructions, when executed by a processor, implement the steps of:
the method comprises the steps of collecting information input by a user, carrying out structuralization processing on the information into standardized data, setting fuzzy logic, grading the data, wherein different grades correspond to different dimensions and indexes, correspondingly establishing nodes in the fuzzy logic according to the dimensions and the indexes, inputting node information to a rear-end server, establishing a corresponding evaluation system and a fuzzy concept and fuzzy logic judgment result mapping relation table according to a fuzzy concept input by a rear-end server service expert, establishing a neural network model according to a deep learning algorithm, carrying out driving processing on the fuzzy logic, establishing a knowledge graph and classifying according to service expert experience knowledge, reasoning and describing the fuzzy concept, iterating a reasoning and describing result into the model to obtain a final judgment basis, processing service requirements to obtain and outputting results to the user.
One embodiment of the present specification provides a computer program product comprising computer programs/instructions that when executed by a processor implement the steps of:
the method comprises the steps of collecting information input by a user, carrying out structuralization processing on the information into standardized data, setting fuzzy logic, grading the data, wherein different grades correspond to different dimensions and indexes, correspondingly establishing nodes in the fuzzy logic according to the dimensions and the indexes, inputting node information to a rear-end server, establishing a corresponding evaluation system and a fuzzy concept and fuzzy logic judgment result mapping relation table according to a fuzzy concept input by a rear-end server service expert, establishing a neural network model according to a deep learning algorithm, carrying out driving processing on the fuzzy logic, establishing a knowledge graph and classifying according to service expert experience knowledge, reasoning and describing the fuzzy concept, iterating a reasoning and describing result into the model to obtain a final judgment basis, processing service requirements to obtain and outputting results to the user.
One embodiment of the present specification provides an electronic apparatus including:
a processor; and
a memory arranged to store computer-executable instructions that, when executed, cause the processor to:
the method comprises the steps of collecting information input by a user, conducting structuralization processing to obtain standardized data, setting fuzzy logic, grading the data, enabling different grades to correspond to different dimensions and indexes, enabling the dimensions and the indexes to correspond to nodes in fuzzification logic, inputting node information to a rear-end server, establishing a corresponding evaluation system and a fuzzification concept and fuzzy logic judgment result mapping relation table through fuzzification concepts input by service experts of the rear-end server, establishing a neural network model through a deep learning algorithm, conducting driving processing on the fuzzy logic, establishing a knowledge graph and classifying by combining experience knowledge of the service experts, conducting reasoning and description on the fuzzification concepts, iterating reasoning and describing results into the model to obtain a final judgment basis, processing service requirements to obtain and output results to the user.
The invention relates to an information processing method, a system and equipment for sleep disorder, which are used for acquiring service data, performing structured processing, setting fuzzy logic, establishing a corresponding evaluation system according to a fuzzification concept input by a service expert, establishing at least one fuzzification concept and fuzzy logic judgment result mapping relation table, constructing a neural network model through a deep learning algorithm, driving and processing the fuzzy logic, establishing a knowledge map and classifying by combining experience knowledge of the service expert, reasoning and describing the fuzzification concept, iterating the reasoning and describing results into the model to obtain a final judgment basis, and processing service requirements to obtain and output results.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or apparatus comprising the element.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the system embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and reference may be made to the partial description of the method embodiment for relevant points.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art to which the present application pertains. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (9)
1. An information processing method of sleep disorder is used for a server side, and comprises the steps of obtaining etiology of diseases, examination data of patients and other event data and conducting structural processing, wherein the examination data comprises text data and picture data, the text data and the picture data are identified and extracted through an NLP algorithm or an image recognition algorithm or deep learning, a neural network model is built, fuzzy logic is set, the data are graded, different grades correspond to different dimensions and indexes, the dimensions and the indexes correspond to nodes in fuzzy logic, priorities are arranged among the different nodes, the dimensions and the indexes of an evaluation system are graded and layered according to different service requirements, mapping relations still exist among the dimensions and the indexes, different levels correspond to different subordinate equations, the grading index mode mainly comprises the fact that expert experience is scattered, different service judging methods are adopted for different service characteristics, a corresponding evaluation system is built according to fuzzy concepts input by service experts, at least one fuzzy concept and fuzzy logic judgment result mapping relation table is built, the neural network model is built through the deep learning algorithm, the fuzzy logic is driven to process, knowledge map is built in combination with the service characteristics, knowledge concepts are described, the fuzzy concept is classified and the expert is classified and output to the final fuzzy reasoning judgment result, and the fuzzy inference is obtained according to the fuzzy inference and the service reasoning result.
2. The information processing method of sleep disorders according to claim 1, wherein the evaluation system establishes relevant dimensions and indexes, and different dimensions and indexes form a net information distribution.
3. The information processing method of sleep disorders according to claim 2, wherein the index includes good, middle and bad third grades.
4. The information processing method for sleep disorders according to claim 1, wherein the nodes are distributed in layers, and the nodes with higher priority are preferentially defuzzified in the fuzzy logic reasoning process.
5. An information processing method for sleep disorder is applied to an internet medical platform, causes of diseases input by users, examination data of patients and other event data are collected and subjected to structured processing to form standardized data, the examination data comprise text data and picture data, the text data and the picture data are identified and extracted through an NLP algorithm or an image recognition algorithm or deep learning, a neural network model is constructed, fuzzy logic is set, the data are classified, different classifications correspond to different dimensions and indexes, the dimensions and the indexes correspond to nodes in fuzzy logic, priorities are arranged among the different nodes, the dimensions and the indexes of an evaluation system are classified and layered according to different service requirements, mapping relations also exist among the dimensions and the indexes, the different hierarchies correspond to different membership equations, the classified index modes mainly disperse experience processing is conducted on the experience of the classification, different service judging methods are adopted for reasoning different service characteristics, node information is input to a back-end server, the evaluation system corresponding to the fuzzy concept inputted by the fuzzy concept of the back-end server and a mapping relation between the fuzzy concept and a result table are constructed, the neural network model is constructed by combining the fuzzy logic, the expert is driven to perform fuzzy processing on the experience, and the expert classification processing and the final classification processing is carried out on the expert, and the expert classification processing results to obtain the business classification results and the expert classification results.
6. An information processing system for sleep disorder comprises a server, a client and an internet medical platform,
the user submits the information through the client-side,
the internet medical platform collects etiology of diseases input by a user, inspection data of patients and other event data are subjected to structured processing to be standardized data, the inspection data comprise text data and picture data, the text data and the picture data are identified and extracted through an NLP algorithm or an image recognition algorithm or deep learning, a neural network model is constructed, fuzzy logic is set, the data are classified, different classifications correspond to different dimensions and indexes, the dimensions and the indexes correspond to nodes in the fuzzy logic, priorities are arranged among the different nodes, the dimensions and the indexes of an evaluation system are classified and layered according to different business requirements, mapping relations also exist among the dimensions and the indexes, different levels correspond to different membership equations, the grading index mode mainly comprises dispersing expert experience, different business judgment methods are adopted according to different business characteristics, and node information is input to a rear-end server;
the back-end server establishes a corresponding evaluation system and a fuzzy concept and fuzzy logic judgment result mapping relation table for the fuzzy concepts input by the service experts, establishes a neural network model through a deep learning algorithm, drives and processes the fuzzy logic, establishes a knowledge map and classifies the knowledge map in combination with experience knowledge of the service experts, infers and describes the fuzzy concepts, iterates the inference and description results into the model to obtain a final judgment basis, processes service requirements to obtain an output result and feeds the output result back to the Internet medical platform.
7. The system of claim 6, wherein the evaluation system establishes relevant dimensions and metrics, and wherein different dimensions and metrics form a mesh information distribution.
8. An electronic device, comprising:
a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to:
the method comprises the steps of obtaining etiology of diseases, examination data of patients and other event data and conducting structural processing, wherein the examination data comprises text data and picture data, the text data and the picture data are identified and extracted through an NLP algorithm or an image identification algorithm or deep learning, a neural network model is built, fuzzy logic is set, the data are classified, different classifications correspond to different dimensions and indexes, the dimensions and the indexes correspond to nodes in fuzzy logic, priorities are arranged among the different nodes, the dimensions and the indexes of an evaluation system are classified and layered according to different service requirements, mapping relations also exist among the dimensions and the indexes, different hierarchies correspond to different membership equations, the grading index mode is mainly used for conducting decentralized processing on expert experience, different service judging methods are used for different service characteristics, a corresponding evaluation system is built according to fuzzy concepts input by service experts, at least one fuzzy concept and fuzzy logic judgment result mapping relation table is built, the neural network model is built through the deep learning algorithm, the fuzzy logic is driven processing, expert knowledge is built and classified in combination with service expert knowledge, knowledge is conducted and is described, and iterative processing results are obtained according to the service reasoning and output requirements.
9. A computer-readable storage medium on which a computer program/instructions are stored, characterized in that the computer program/instructions, when executed by a processor, implement the steps of the method according to one of claims 1 to 5.
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