Information processing method, device and system for sleep disorders

Abstract

The present invention discloses a method, device and system for processing information on sleep disorders, which acquire business data and perform structured processing, set fuzzy logic, establish a corresponding evaluation system based on fuzzy concepts input by business experts, establish at least one mapping relationship table between fuzzy concepts and fuzzy logic judgment results, build a neural network model through a deep learning algorithm, drive the fuzzy logic, establish and classify knowledge graphs based on the experience and knowledge of business experts, reason and describe the fuzzy concepts, iterate the reasoning and description results into the model to obtain the final judgment basis, process business needs to obtain and output results, and can effectively improve the decision-making efficiency of business experts while taking into account the accuracy of decisions without requiring a large number of existing samples.

Description

[0001] This application is a divisional application of the Chinese invention patent application with the application date of August 23, 2021, application number CN202110964802.4, and invention name “Business processing method, device and system based on fuzzy logic”. Technical Field

[0002] The present invention relates to the field of artificial intelligence, and in particular to a method, device and system for processing information on sleep disorders. Background Art

[0003] With the development of Internet technology, big data applications are becoming more and more popular. Various unstructured data have become very massive, and much of the data is used for domain specificity and professionalism, which leads to the need to rely on the personal experience of experts to judge and evaluate. However, in fact, most of the work can be solved by intelligent systems. Therefore, there is an urgent need for a new technology to improve the efficiency of auxiliary processing. Summary of the Invention

[0004] In response to the above-mentioned defects, the technical problem to be solved by the present invention is how to learn expert experience through intelligent technology and solve a large number of experts' primary judgment and decision-making problems, reduce the burden on experts, and improve overall business processing efficiency.

[0005] In view of the above-mentioned defects, the object of the present invention is to provide a method, system, electronic device, computer storage medium and program product for processing information on sleep disorders.

[0006] According to one aspect of the embodiments of this specification, a method for processing information on sleep disorders is provided, which is used on a server side to obtain the cause of the disease, the patient's examination data, and other event data and perform structured processing. The examination data includes text data and image data, which are identified and extracted by an NLP algorithm or an image recognition algorithm or deep learning, and a neural network model is constructed. Fuzzy logic is set to classify the data, and different classifications correspond to different dimensions and indicators. Nodes in the fuzzy logic are established corresponding to the dimensions and indicators, and different nodes have priorities. The dimensions and indicators of the evaluation system are graded and layered according to different business needs. The dimensions and There is also a mapping relationship between indicators. Different levels correspond to different membership equations. The rating indicator method is mainly to disperse the expert experience, adopt different business judgment methods for different business characteristics, establish a corresponding evaluation system based on the fuzzy concepts input by business experts, and establish at least one mapping relationship table between fuzzy concepts and fuzzy logic judgment results. A neural network model is constructed through a deep learning algorithm to drive the fuzzy logic. The knowledge graph is established and classified based on the experience and knowledge of business experts. The fuzzy concepts are inferred and described, and the inference and description results are iterated into the model to obtain the final judgment basis. The business needs are processed to obtain the output results.

[0007] Preferably, the above evaluation system establishes relevant dimensions and indicators, and different dimensions and indicators form a network information distribution.

[0008] Preferably, the above indicators include three levels: good, medium and poor.

[0009] Preferably, the above nodes are distributed in layers, and nodes with higher priorities are defuzzified first during the fuzzy logic reasoning process.

[0010] The present invention provides an information processing method for sleep disorders, which is applied to an Internet medical platform. The method collects the causes of diseases input by users, the examination data of patients, and other event data, and performs structured processing into standardized data. The examination data includes text data and image data. The text data and image data are identified and extracted by an NLP algorithm or an image recognition algorithm or deep learning, and a neural network model is constructed. Fuzzy logic is set to classify the data. Different classifications correspond to different dimensions and indicators. Nodes in the fuzzy logic are established corresponding to the dimensions and indicators. Different nodes have priorities. The dimensions and indicators of the evaluation system are graded and layered according to different business needs. There are also priorities between the dimensions and indicators. In the mapping relationship, different levels correspond to different membership equations. The rating indicator method is mainly to disperse the expert experience, adopt different business judgment methods for different business characteristics, input node information into the back-end server, and establish the corresponding evaluation system and the mapping relationship table between fuzzy concepts and fuzzy logic judgment results through the fuzzy concepts input by the back-end server business experts. The neural network model is constructed through the deep learning algorithm, the fuzzy logic is driven, and the knowledge graph is established and classified based on the experience and knowledge of business experts. The fuzzy concepts are inferred and described, and the inference and description results are iterated into the model to obtain the final judgment basis, process the business needs, and output the results to the user.

[0011] The present invention provides an information processing system for sleep disorders, including a server, a client and an Internet medical platform.

[0012] The user submits information through the client,

[0013] The Internet medical platform collects the causes of diseases, patient examination data and other event data input by users, and structures them into standardized data. The examination data includes text data and image data. The text data and image data are identified and extracted through NLP algorithms, image recognition algorithms or deep learning, and a neural network model is constructed. Fuzzy logic is set to classify the data. Different classifications correspond to different dimensions and indicators. Nodes in the fuzzy logic are established corresponding to the dimensions and indicators. Different nodes have priorities. The dimensions and indicators of the evaluation system are graded and layered according to different business needs. There is also a mapping relationship between the dimensions and indicators. Different levels correspond to different membership equations. The rating indicator method mainly distributes expert experience, adopts different business judgment methods for different business characteristics, and inputs node information to the back-end server.

[0014] The back-end server establishes a corresponding evaluation system and a mapping relationship table between fuzzy concepts and fuzzy logic judgment results for the fuzzy concepts input by business experts, builds a neural network model through a deep learning algorithm, drives fuzzy logic, establishes and classifies knowledge graphs based on the experience and knowledge of business experts, infers and describes fuzzy concepts, iterates the inference and description results into the model to obtain the final judgment basis, processes business needs to obtain output results and feeds back to the Internet medical platform.

[0015] Preferably, the above evaluation system establishes relevant dimensions and indicators, and different dimensions and indicators form a network information distribution.

[0016] The present invention provides an electronic device, comprising:

[0017] a processor; and a memory configured to store computer-executable instructions that, when executed, cause the processor to:

[0018] The cause of the disease, patient examination data, and other event data are obtained and structured. The examination data includes text data and image data. The text data and image data are identified and extracted using an NLP algorithm, image recognition algorithm, or deep learning algorithm to construct a neural network model. Fuzzy logic is set to classify the data. Different classifications correspond to different dimensions and indicators. Dimensions and indicators are mapped to establish nodes in the fuzzy logic. Different nodes have priorities. The dimensions and indicators of the evaluation system are graded and layered according to different business needs. There is also a mapping relationship between dimensions and indicators. Different levels correspond to different membership equations. The rating indicator method mainly uses decentralized processing of expert experience. Different business judgment methods are used for different business characteristics. A corresponding evaluation system is established based on the fuzzy concepts input by business experts. At least one mapping relationship table between fuzzy concepts and fuzzy logic judgment results is established. A neural network model is constructed using a deep learning algorithm to drive the fuzzy logic. A knowledge graph is established and classified based on the experience and knowledge of business experts. The fuzzy concepts are inferred and described. The inference and description results are iterated into the model to obtain the final judgment basis. The business needs are processed to obtain the output results.

[0019] The present invention provides a computer-readable storage medium having a computer program / instruction stored thereon, wherein the computer program / instruction implements the steps of the above method when executed by a processor.

[0020] The present invention obtains business data and performs structured processing, sets up fuzzy logic, establishes a corresponding evaluation system according to the fuzzified concepts input by business experts, establishes at least one mapping relationship table between fuzzy concepts and fuzzy logic judgment results, constructs a neural network model through a deep learning algorithm, drives the fuzzy logic, establishes and classifies knowledge graphs based on the experience and knowledge of business experts, infers and describes the fuzzy concepts, iterates the inference and description results into the model to obtain the final judgment basis, processes the business needs to obtain and output results, and can effectively improve the decision-making efficiency of business experts while taking into account the accuracy of decisions without the need for a large number of existing samples. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0022] Figure 1 A schematic diagram showing a framework of an embodiment of a method for processing information on sleep disorders according to the present invention is shown;

[0023] Figure 2 A schematic flow chart of an embodiment of a method for processing information on sleep disorders according to the present invention is shown;

[0024] Figure 3 A schematic diagram of a fuzzy logic flow diagram of an embodiment of a sleep disorder information processing method according to the present invention is shown;

[0025] Figure 4 A schematic diagram of an artificial intelligence embodiment flow chart of a sleep disorder information processing method according to the present invention is shown;

[0026] Figure 5 A flowchart of an embodiment of a sleep disorder information processing system according to the present invention is shown;

[0027] Figure 6 A schematic diagram of the external output process of the sleep disorder information processing system according to the present invention is shown. DETAILED DESCRIPTION

[0028] The features and exemplary embodiments of various aspects of the present invention will be described in detail below. In order to make the objects, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with the accompanying drawings and Examples. It should be understood that the specific embodiments described herein are only configured to explain the present invention and are not configured to limit the present invention. For those skilled in the art, the present invention can be implemented without the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present invention by illustrating examples of the present invention.

[0029] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, the elements defined by the phrase "comprising..." do not exclude the presence of other identical elements in the process, method, article, or device comprising the elements.

[0030] In some business scenarios, human experience is needed to assist in judgment. For example, in the manual interpretation of remote sensing imagery, based on the requirements of various disciplines (departments), interpretive symbols, practical experience, and knowledge are used to identify targets from remote sensing imagery, qualitatively and quantitatively extract relevant information such as the target's distribution, structure, and function, and then represent this information on a geographic basemap. For example, interpreting land use status involves first identifying land use types in an image and then calculating the area of ​​each type of land on the map. Visual interpretation of remote sensing imagery involves the interpreter identifying the required ground feature information through direct observation or with the help of simple tools (such as a magnifying glass).

[0031] Another example is in medicine, particularly in the diagnosis of sleep disorders. Symptoms vary from patient to patient, with some attributed to illness, others to lifestyle habits, and others to substance use. Expert judgment requires extensive preliminary work, including pathology testing, physical examinations, and case investigations. These expert-driven decisions lack sufficient historical data to rely on, requiring excessive manual intervention and analysis, which is both time-consuming and labor-intensive.

[0032] like Figure 1 As shown, an embodiment of the present specification provides an information processing method for sleep disorders, which is used on the server side to obtain business data and perform structured processing, set fuzzy logic, establish a corresponding evaluation system based on the fuzzy concepts input by business experts, establish at least one mapping relationship table between fuzzy concepts and fuzzy logic judgment results, build a neural network model through a deep learning algorithm, drive the fuzzy logic, establish and classify knowledge graphs based on the experience and knowledge of business experts, reason and describe the fuzzy concepts, iterate the reasoning and description results into the model to obtain the final judgment basis, process the business needs, and obtain and output results.

[0033] In some specific examples, the evaluation system establishes relevant dimensions and indicators, and different dimensions and indicators form a network information distribution.

[0034] The dimensions and indicators of the evaluation system are graded and layered according to different business needs, and there is a mapping relationship between the dimensions and indicators.

[0035] In some possible embodiments, the indicators include three levels: "good," "medium," and "poor." They can also be finer-grained, including five levels: "very good," "very good," "good," "medium," and "poor." In specific implementations, different levels correspond to different membership equations. The rating indicator approach primarily involves decentralized processing of expert experience. Different business judgment methods are used for different business characteristics.

[0036] In some possible embodiments, the business data includes the cause of the disease, the patient's examination data, and other event data.

[0037] In some specific embodiments, different dimensions and indicators form nodes for fuzzy logic processing, with different nodes having priorities. The node configuration can be used for processing within the algorithm model. Nodes are distributed in a hierarchical manner, with those with higher priorities being prioritized for defuzzification during the fuzzy logic reasoning process.

[0038] An embodiment of the present specification provides an information processing method for sleep disorders, which is applied to an Internet medical platform. The method collects information input by users and performs structured processing to form standardized data. Fuzzy logic is set to classify the data. Different classes correspond to different dimensions and indicators. Nodes in the fuzzy logic are established corresponding to the dimensions and indicators. The node information is input to a back-end server. A corresponding evaluation system and a mapping relationship table between the fuzzy concepts and the fuzzy logic judgment results are established based on the fuzzy concepts input by business experts of the back-end server. A neural network model is constructed through a deep learning algorithm to drive the fuzzy logic. A knowledge graph is established and classified based on the experience and knowledge of business experts. The fuzzy concepts are inferred and described. The inference and description results are iterated into the model to obtain the final judgment basis. The business needs are processed to obtain and output the results to the user.

[0039] In some embodiments, the business data includes the clarity, acquisition method, image channel, weather conditions, and other event data of the remote sensing image.

[0040] In some embodiments, other event data includes, but is not limited to, imaging methods (such as multispectral and radar images), imaging time, and historical meteorological conditions.

[0041] In some embodiments, the business data includes the cause of the disease, the patient's examination data, and other event data.

[0042] In some embodiments, the inspection data includes text data and image data. The text data is identified and extracted using algorithms such as NLP, and the image data is identified and extracted using image recognition algorithms.

[0043] In some embodiments, other event data includes, but is not limited to, whether caffeine is consumed, whether alcohol is consumed, and whether sensitive drugs are injected.

[0044] like Figure 2 As shown, an embodiment of this specification provides a method for processing sleep disorder information, including:

[0045] S101. Acquire business data through methods such as NLP, image recognition algorithms, and deep learning;

[0046] S102, setting fuzzy logic and generating a reasoning diagram;

[0047] S103. Build and classify knowledge graphs based on the experience and knowledge of business experts;

[0048] S104. Reason and describe fuzzy concepts;

[0049] S105, building a neural network model based on the training samples;

[0050] S106. Iterate the reasoning and description results into the model to obtain the final judgment basis, process the business requirements and obtain and output the results.

[0051] like Figure 3 As shown, in some embodiments, constructing fuzzy logic includes the following steps:

[0052] S201, setting fuzzy logic;

[0053] S202, generating a fuzzy logic reasoning diagram;

[0054] S203, calculating fuzzy logic reasoning;

[0055] S204: Set data judgment fuzziness.

[0056] like Figure 4 As shown, in some embodiments, deep learning includes the following steps:

[0057] S301. Acquire data through image recognition, NLP and other technologies;

[0058] S302, constructing a neural network model;

[0059] S303, judging by expert experience rules;

[0060] S304. Classify through knowledge graph.

[0061] An embodiment of the present invention provides a sleep disorder information processing system, including a server, a client, and an Internet service platform.

[0062] The user submits information through the client,

[0063] The Internet business platform collects user input information, structures it into standardized data, sets fuzzy logic, and classifies the data. Different classes correspond to different dimensions and indicators. Dimensions and indicators are mapped to nodes in the fuzzy logic, and the node information is input to the backend server.

[0064] The back-end server establishes a corresponding evaluation system and a mapping relationship table between fuzzy concepts and fuzzy logic judgment results for the fuzzy concepts input by the business experts, builds a neural network model through a deep learning algorithm, drives the fuzzy logic, builds and classifies the knowledge graph based on the experience and knowledge of the business experts, infers and describes the fuzzy concepts, iterates the inference and description results into the model to obtain the final judgment basis, processes the business needs to obtain output results, and feeds back to the Internet business platform;

[0065] The Internet service platform pushes the result to the user client.

[0066] In some embodiments, the evaluation system in the system establishes relevant dimensions and indicators, and different dimensions and indicators form a network information distribution.

[0067] In some embodiments, the dimensions in the system include sleep status, physical examination data, family genetic history, intake of substances, and lifestyle habits.

[0068] like Figure 4 As shown, an embodiment of the present specification provides an information processing system for sleep disorders, including a fuzzy logic subsystem and a deep learning subsystem. The fuzzy logic subsystem includes a fuzzy logic setting module, a fuzzy logic reasoning graph generation module, a fuzzy logic reasoning calculation module and a data judgment fuzzification processing module. The deep learning subsystem includes an image recognition module, a neural network model construction module, an expert experience rule judgment module and a knowledge graph classification module.

[0069] like Figure 5 As shown, the system also includes an AI driving module, a defuzzification processing module and a business decision output module.

[0070] One embodiment of the present disclosure provides a computer-readable storage medium having a computer program / instruction stored thereon, wherein the computer program / instruction, when executed by a processor, implements the following steps:

[0071] Acquire business data and perform structured processing, set up fuzzy logic, establish a corresponding evaluation system based on the fuzzy concepts input by business experts, establish at least one mapping relationship table between fuzzy concepts and fuzzy logic judgment results, build a neural network model through deep learning algorithms, drive fuzzy logic, build and classify knowledge graphs based on the experience and knowledge of business experts, reason and describe fuzzy concepts, iterate the reasoning and description results into the model to obtain the final judgment basis, process business needs and obtain and output results.

[0072] One embodiment of this specification provides a computer program product, including a computer program / instruction, characterized in that when the computer program / instruction is executed by a processor, the following steps are implemented:

[0073] Acquire business data and perform structured processing, set up fuzzy logic, establish a corresponding evaluation system based on the fuzzy concepts input by business experts, establish at least one mapping relationship table between fuzzy concepts and fuzzy logic judgment results, build a neural network model through deep learning algorithms, drive fuzzy logic, build and classify knowledge graphs based on the experience and knowledge of business experts, reason and describe fuzzy concepts, iterate the reasoning and description results into the model to obtain the final judgment basis, process business needs and obtain and output results.

[0074] An electronic device provided in one embodiment of the present disclosure includes:

[0075] processor; and

[0076] A memory configured to store computer-executable instructions that, when executed, cause the processor to:

[0077] Acquire business data and perform structured processing, set up fuzzy logic, establish a corresponding evaluation system based on the fuzzy concepts input by business experts, establish at least one mapping relationship table between fuzzy concepts and fuzzy logic judgment results, build a neural network model through deep learning algorithms, drive fuzzy logic, build and classify knowledge graphs based on the experience and knowledge of business experts, reason and describe fuzzy concepts, iterate the reasoning and description results into the model to obtain the final judgment basis, process business needs and obtain and output results.

[0078] One embodiment of the present disclosure provides a computer-readable storage medium having a computer program / instruction stored thereon, wherein the computer program / instruction, when executed by a processor, implements the following steps:

[0079] Collect user input information and process it into standardized data through structured processing. Set fuzzy logic and classify the data. Different classes correspond to different dimensions and indicators. Establish nodes in the fuzzy logic corresponding to the dimensions and indicators. Input node information to the back-end server. Establish a corresponding evaluation system and a mapping relationship table between fuzzy concepts and fuzzy logic judgment results based on the fuzzy concepts input by the back-end server business experts. Build a neural network model through deep learning algorithms to drive the fuzzy logic. Combine the experience and knowledge of business experts to establish and classify knowledge graphs. Infer and describe fuzzy concepts. It iterate the inference and description results into the model to obtain the final judgment basis. Process business needs to obtain and output results to users.

[0080] One embodiment of this specification provides a computer program product, including a computer program / instruction, characterized in that when the computer program / instruction is executed by a processor, the following steps are implemented:

[0081] Collect user input information and process it into standardized data through structured processing. Set fuzzy logic and classify the data. Different classes correspond to different dimensions and indicators. Establish nodes in the fuzzy logic corresponding to the dimensions and indicators. Input node information to the back-end server. Establish a corresponding evaluation system and a mapping relationship table between fuzzy concepts and fuzzy logic judgment results based on the fuzzy concepts input by the back-end server business experts. Build a neural network model through deep learning algorithms to drive the fuzzy logic. Combine the experience and knowledge of business experts to establish and classify knowledge graphs. Infer and describe fuzzy concepts. It iterate the inference and description results into the model to obtain the final judgment basis. Process business needs to obtain and output results to users.

[0082] An electronic device provided in one embodiment of the present disclosure includes:

[0083] processor; and

[0084] A memory configured to store computer-executable instructions that, when executed, cause the processor to:

[0085] Collect user input information and process it into standardized data through structured processing. Set fuzzy logic and classify the data. Different classes correspond to different dimensions and indicators. Establish nodes in the fuzzy logic corresponding to the dimensions and indicators. Input node information to the back-end server. Establish a corresponding evaluation system and a mapping relationship table between fuzzy concepts and fuzzy logic judgment results based on the fuzzy concepts input by the back-end server business experts. Build a neural network model through deep learning algorithms to drive the fuzzy logic. Combine the experience and knowledge of business experts to establish and classify knowledge graphs. Infer and describe fuzzy concepts. It iterate the inference and description results into the model to obtain the final judgment basis. Process business needs to obtain and output results to users.

[0086] The information processing method, system and device for sleep disorders of the present invention obtain business data and perform structured processing, set fuzzy logic, establish a corresponding evaluation system according to the fuzzy concepts input by business experts, establish at least one mapping relationship table between fuzzy concepts and fuzzy logic judgment results, build a neural network model through a deep learning algorithm, drive the fuzzy logic, build and classify knowledge graphs based on the experience and knowledge of business experts, reason and describe the fuzzy concepts, iterate the reasoning and description results into the model to obtain the final judgment basis, process the business needs to obtain and output the results, and can effectively improve the decision-making efficiency of business experts while taking into account the accuracy of decisions without the need for a large number of existing samples. Especially for the treatment of sleep disorders, in the face of various complex and diverse inducements, it can greatly reduce the primary workload of medical experts and use the experts' experience value mainly for more judgment-oriented matters.

[0087] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0088] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0089] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0090] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0091] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0092] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0093] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0094] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0095] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules 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 disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0096] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0097] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0098] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for processing sleep disorder information, used on a server side, to obtain the cause of a disease and patient examination data and perform structured processing. The examination data includes text data and image data. The text data is identified and extracted using a natural language processing (NLP) algorithm, and the image data is identified and extracted using an image recognition algorithm. Fuzzy logic is then applied to the data to classify the data. Different classifications correspond to different dimensions and indicators. Dimensions and indicators are mapped to establish nodes in the fuzzy logic, with priorities between the nodes. The dimensions and indicators of the evaluation system are graded and layered according to different business needs. Dimensions and indicators also have mapping relationships, and different levels correspond to different membership equations. The rating indicator approach is to decentralizedly process expert experience. Different business judgment methods are used for different business characteristics. An evaluation system is established based on fuzzy concepts input by business experts. At least one mapping relationship table between fuzzy concepts and fuzzy logic judgment results is established. A neural network model is constructed using a deep learning algorithm to drive fuzzy logic processing. A knowledge graph is established and classified based on the business expert experience. The fuzzy concepts are inferred and described. The inference and description results are iterated into the model to obtain a final judgment basis. The business needs are processed to obtain output results.

2. The information processing method for sleep disorders according to claim 1, wherein the evaluation system establishes relevant dimensions and indicators, and different dimensions and indicators form a network information distribution.

3. The method for processing sleep disorder information according to claim 2, wherein the indicator comprises three levels: good, medium, and poor.

4. The method for processing information about sleep disorders according to claim 1, wherein the nodes are distributed in layers, and nodes with higher priorities are defuzzified first during the fuzzy logic reasoning process.

5. A method for processing information on sleep disorders, applied to an Internet medical platform, collects the causes of diseases entered by users and the patient's examination data and structures them into standardized data. The examination data includes text data and image data. The text data is identified and extracted by an NLP algorithm, and the image data is identified and extracted by an image recognition algorithm. Fuzzy logic is set to classify the data. Different classifications correspond to different dimensions and indicators. Dimensions and indicators are mapped to establish nodes in the fuzzy logic. Different nodes have priorities. The dimensions and indicators of the evaluation system are graded and layered according to different business needs. There is also a mapping relationship between dimensions and indicators. Different The levels correspond to different membership equations. The rating indicator method is to disperse the expert experience, adopt different business judgment methods for different business characteristics, input node information to the back-end server, and establish a corresponding evaluation system and a mapping relationship table between fuzzy concepts and fuzzy logic judgment results through the fuzzy concepts input by the back-end server business experts. A neural network model is constructed through a deep learning algorithm to drive the fuzzy logic. The knowledge graph is established and classified based on the experience and knowledge of business experts. The fuzzy concepts are inferred and described, and the inference and description results are iterated into the model to obtain the final judgment basis. The business needs are processed and the results are output to the user.

6. A sleep disorder information processing system, including a server, a client, and an Internet medical platform. The user submits information through the client, The Internet medical platform collects the causes of diseases and patient examination data input by users and structures them into standardized data. The examination data includes text data and image data. The text data is identified and extracted by the NLP algorithm, and the image data is identified and extracted by the image recognition algorithm. Fuzzy logic is set to classify the data. Different classifications correspond to different dimensions and indicators. Dimensions and indicators are mapped to establish nodes in the fuzzy logic. Different nodes have priorities. The dimensions and indicators of the evaluation system are graded and layered according to different business needs. There is also a mapping relationship between dimensions and indicators. Different levels correspond to different membership equations. The indicator method for rating is to disperse expert experience, adopt different business judgment methods for different business characteristics, and input node information to the back-end server. The back-end server establishes a corresponding evaluation system and a mapping relationship table between fuzzy concepts and fuzzy logic judgment results for the fuzzy concepts input by business experts, builds a neural network model through a deep learning algorithm, drives fuzzy logic, establishes and classifies knowledge graphs based on the experience and knowledge of business experts, infers and describes fuzzy concepts, iterates the inference and description results into the model to obtain the final judgment basis, processes business needs to obtain output results and feeds back to the Internet medical platform.

7. According to the system of claim 6, the evaluation system establishes relevant dimensions and indicators, and different dimensions and indicators form a network information distribution.

8. An electronic device comprising: processor; and a memory configured to store computer-executable instructions that, when executed, cause the processor to: The cause of the disease and the patient's examination data are obtained and structured. The examination data includes text data and image data. The text data is identified and extracted using an NLP algorithm, and the image data is identified and extracted using an image recognition algorithm. Fuzzy logic is set to classify the data. Different classifications correspond to different dimensions and indicators. Dimensions and indicators are mapped to establish nodes in the fuzzy logic. Different nodes have priorities. The dimensions and indicators of the evaluation system are graded and layered according to different business needs. There is also a mapping relationship between dimensions and indicators. Different levels correspond to different membership equations. The rating indicator method is to disperse expert experience. Different business judgment methods are used for different business characteristics. A corresponding evaluation system is established based on the fuzzy concepts input by business experts. At least one mapping relationship table between fuzzy concepts and fuzzy logic judgment results is established. A neural network model is constructed using a deep learning algorithm to drive the fuzzy logic. A knowledge graph is established and classified based on the experience and knowledge of business experts. The fuzzy concepts are inferred and described. The inference and description results are iterated into the model to obtain the final judgment basis. The business needs are processed to obtain output results.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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