1. A data processing method based on deep learning is applied to a server side, picture materials uploaded by a user are collected through terminal equipment and sent to a data center processing system, the pictures are detected, classified and identified, business data modeling of each picture is completed in the data center processing system, business information is restored, a natural language processing pre-training model is used for recognizing text entities in the business professional field, behaviors and paths of the user are restored, an integral behavior picture database of the user is formed, and independent entities are generated and are processed in a standardized mode through a knowledge map and a search algorithm.

2. The deep learning-based data processing method according to claim 1, further comprising preparing a diagnosis decision factor, performing a factor reasoning decision, simulating insurance claim review by using a rule engine, and performing decision tree logic execution to obtain an intelligent review conclusion.

3. The deep learning based data processing method of claim 1, further comprising calculating a recommendation decision factor by which to calculate a decision and using a recommendation algorithm to derive an insurance recommendation combination.

4. The deep learning based data processing method according to claim 1, further comprising establishing a data model, and giving a relevant proposal through model calculation.

5. The deep learning based data processing method according to one of claims 1 to 4, wherein the text recognition of the business professional field comprises the type, name, occurrence time and index information of the material.

6. The data processing method based on deep learning of claim 1, wherein the pictures uploaded by the user are labeled with serial numbers and then subjected to detection classification, and the picture classification includes but is not limited to documentary evidence, description reports and data records.

7. The data processing method based on deep learning of claim 1, wherein the specific time, user name, service provider name and service result on the picture are identified based on image detection and identification technology, full text identification is performed on the picture, and after the text content is further extracted, a logical context is formed through semantic identification.

8. A data processing method based on deep learning is applied to an internet platform, user authorization permission is obtained based on terminal equipment, picture materials uploaded by a user are collected and sent to a data center processing system of a background server, the data center processing system detects, classifies and identifies pictures, business data modeling of each picture is completed, business information is restored, a natural language processing pre-training model is used for recognizing text entities in the business professional field, consumption behaviors and paths of the user are restored, an integral behavior picture database of the user is formed, an independent entity is generated and standardized by using a knowledge map and a search algorithm, and a specific solution is output to the user by combining with application products of the internet platform.

9. The method of claim 8, wherein the internet platform simulates insurance claim settlement audit by using a rule engine through a diagnosis decision model, and performs decision tree logic execution to obtain an intelligent audit conclusion.

10. The method of claim 8, wherein the internet platform calculates a recommendation decision factor by which to calculate a decision using a recommendation algorithm to derive an insurance recommendation combination.

11. The method of claim 8, wherein the internet platform provides relevant suggested solutions through model calculations based on the business data model.

12. The method of claim 8, wherein the internet platform detects and classifies pictures uploaded by users based on the pictures being labeled with serial numbers, and the picture classification includes but is not limited to documentary evidence, instruction reports, and data records.

13. The method as claimed in claim 8, wherein the internet platform identifies specific time, user name, service provider name, and business result on the picture based on image detection and identification technology, performs full text identification on the picture, further extracts text content, and forms logical context through semantic identification.

14. A data processing system based on deep learning comprises at least one terminal device, at least one Internet platform and at least one server, the internet platform acquires picture materials uploaded by a user based on the terminal equipment, acquires user authorization permission, acquires the picture materials uploaded by the user and sends the picture materials to a data center processing system of a background server, the data center processing system detects, classifies and identifies the pictures, business data modeling of each picture is completed, business information is restored, a natural language processing pre-training model is used for recognizing text entities in the business professional field, behaviors and paths of the user are restored, an integral behavior picture database of the user is formed, an independent entity is generated and standardized by using a knowledge map and a search algorithm, and a specific solution is output to the user by combining application products of the internet platform.

15. The system of claim 14, wherein the pictures uploaded by the user are sequentially numbered and then classified for inspection, and the picture classification includes but is not limited to documentary evidence, instruction report, and data record.

16. The system of claim 14 or 15, wherein the specific time, user name, service provider name, and business result on the picture are identified based on image detection and identification technology, the picture is subjected to full text identification, and after the text content is further extracted, a logical context is formed by semantic identification.

17. 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 7.

18. A computer program product comprising computer programs/instructions, characterized in that the computer programs/instructions, when executed by a processor, implement the steps of the method according to one of claims 1 to 7.

19. 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 user authorization permission based on terminal equipment, collecting picture materials uploaded by a user and sending the picture materials to a data center processing system of a background server, detecting, classifying and identifying the pictures by the data center processing system, completing business data modeling of each picture, restoring business information, utilizing a natural language to process a pre-training model, performing text entity identification in the business professional field, restoring consumption behaviors and paths of the user, forming an integral behavior picture database of the user, utilizing a knowledge map and a search algorithm to generate an independent entity and perform standardized processing, and combining application products of an internet platform to output a specific solution to the user.

Background

Along with the popularization of internet technology, more and more applications are generated, various kinds of internet +' are in endless, the artificial intelligence technology also finds many application scenes along with internet application, and the intelligent application scenes based on the mobile internet technology are many, such as intelligent travel, intelligent retail, intelligent medical treatment and the like, but the technology is greatly challenged due to the specialty of each business field and the difficulty of various kinds of knowledge. The professional vocabularies of industries such as various medicines, chemicals, biology, materials, beauty and cosmetics are very numerous and various, some terms are more difficult to recognize, and the work is the most complex and time-consuming part in the related field.

Disclosure of Invention

Aiming at the defects, the technical problem to be solved by the invention is how to realize automatic execution and intelligent decision by means of sensing and identifying various information of a user by means of an artificial intelligence technology and a natural language processing technology and modeling a subsequent decision flow.

In view of the above-mentioned drawbacks, an object of the present invention is to provide a data processing method, system and electronic device based on deep learning, a computer storage medium and a program product.

The method is applied to a server side, picture materials uploaded by a user are collected through terminal equipment and are sent to a data center processing system, the pictures are detected, classified and identified, the data center processing system is used for modeling the service data of each picture, restoring service information, processing a pre-training model by using natural language, identifying text entities in the professional field of service, restoring behaviors and paths of the user, forming an integral behavior image database of the user, and generating independent entities and carrying out standardized processing by using a knowledge graph and a search algorithm.

Preferably, the method further comprises the steps of preparing diagnosis decision factors, simulating insurance claim settlement audit by using a rule engine through factor reasoning decision, and performing decision tree logic execution to obtain an intelligent audit conclusion.

Preferably, the method further comprises the steps of calculating a recommendation decision factor, calculating a decision through the factor, and obtaining the insurance recommendation combination by using a recommendation algorithm.

Preferably, the method further comprises the step of establishing a data model, and giving out related suggested schemes through model calculation.

Preferably, the text identification in the business professional field includes a type, a name, an occurrence time, and index information of the material.

Preferably, the pictures uploaded by the user are marked with serial numbers and then detected and classified, and the picture classification includes but is not limited to a certificate, an instruction report and a data record.

Preferably, the specific time, the user name, the name of the service provider and the service result on the picture are identified based on an image detection and identification technology, the picture is subjected to full text identification, the text content is further extracted, and then a logical context is formed through semantic identification.

The invention provides a data processing method based on deep learning, which is applied to an internet platform, acquires user authorization permission based on terminal equipment, acquires picture materials uploaded by a user and sends the picture materials to a data center processing system of a background server, the data center processing system detects, classifies and identifies pictures, completes business data modeling of each picture, restores business information, processes a pre-training model by using natural language, identifies text entities in the business professional field, restores consumption behaviors and paths of the user, forms an integral behavior picture database of the user, generates independent entities and carries out standardized processing by using a knowledge map and a search algorithm, and outputs a specific solution to the user by combining an application product of the internet platform.

Preferably, the internet platform simulates insurance claim settlement audit by using a rule engine through a diagnosis decision model, and performs decision tree logic execution to obtain an intelligent audit conclusion.

Preferably, the internet platform calculates a recommendation decision factor, calculates a decision through the factor, and obtains an insurance recommendation combination by using a recommendation algorithm.

Preferably, the internet platform provides a relevant proposal scheme through model calculation based on a business data model.

Preferably, the internet platform performs detection and classification based on the fact that the pictures uploaded by the user are marked with serial numbers, and the picture classification includes but is not limited to certification documents, description reports and data records.

Preferably, the internet platform identifies specific time, user name, service provider name and service result on the picture based on image detection and identification technology, performs full text identification on the picture, further extracts text content, and forms logical context through semantic identification.

The invention provides a data processing system based on deep learning, which comprises at least one terminal device, at least one internet platform and at least one server, the internet platform acquires picture materials uploaded by a user based on the terminal equipment, acquires user authorization permission, acquires the picture materials uploaded by the user and sends the picture materials to a data center processing system of a background server, the data center processing system detects, classifies and identifies the pictures, business data modeling of each picture is completed, business information is restored, a natural language processing pre-training model is used for recognizing text entities in the business professional field, behaviors and paths of the user are restored, an integral behavior picture database of the user is formed, an independent entity is generated and standardized by using a knowledge map and a search algorithm, and a specific solution is output to the user by combining application products of the internet platform.

Preferably, the pictures uploaded by the user are marked with serial numbers and then detected and classified, and the picture classification includes but is not limited to certificate documents, description reports and data records.

Preferably, the specific time, the user name, the name of the service provider and the service result on the picture are identified based on an image detection and identification technology, the picture is subjected to full text identification, the text content is further extracted, and then a logical context is formed through semantic identification.

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 present invention provides a computer program product comprising computer programs/instructions, characterized in that the computer programs/instructions, when executed by a processor, implement the steps of the above-mentioned method.

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 user authorization permission based on terminal equipment, collecting picture materials uploaded by a user and sending the picture materials to a data center processing system of a background server, detecting, classifying and identifying the pictures by the data center processing system, completing business data modeling of each picture, restoring business information, utilizing a natural language to process a pre-training model, performing text entity identification in the business professional field, restoring consumption behaviors and paths of the user, forming an integral behavior picture database of the user, utilizing a knowledge map and a search algorithm to generate an independent entity and perform standardized processing, and combining application products of an internet platform to output a specific solution to the user.

Based on the deep learning principle, the method processes various related picture data uploaded by a user through picture recognition, automatically recognizes text information and sequences the text information, completes restoration of related business information of the user by combining a knowledge graph of related professional fields after model training, can construct an integral behavior image, provides objective data for subsequent product application, and effectively solves the problems of data loss or data counterfeiting.

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 schematic structural diagram illustrating an embodiment of a deep learning-based data processing method according to the present invention;

FIG. 2 is a schematic structural diagram of another embodiment of the deep learning-based data processing method of the present invention;

FIG. 3 is a schematic structural diagram of another embodiment of the deep learning-based data processing method of the present invention;

FIG. 4 is a flow diagram illustrating an embodiment of a deep learning based data processing system of the present invention;

FIG. 5 is a flow diagram of another embodiment of a deep learning based data processing system of the present invention;

FIG. 6 is a flow chart of another embodiment of the deep learning based data processing system according to the present invention.

Detailed Description

Features and exemplary embodiments of various aspects 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 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 phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

An embodiment of the present specification provides a data processing method based on deep learning, which is applied to a server, collects picture materials uploaded by a user through a terminal device and sends the picture materials to a data center processing system, detects, classifies and identifies pictures, completes modeling of service data of each picture in the data center processing system, restores service information, processes a pre-training model by using natural language, performs text entity identification in the professional field of service, restores behaviors and paths of the user, forms an integral behavior picture database of the user, and generates an independent entity and performs standardized processing by using a knowledge graph and a search algorithm.

In a specific example, the text recognition in the professional field comprises the text recognition of a remote sensing image, including the area of a land, the recognition of ground objects, infrared spectrum information, geographic coordinates and the like.

In one specific example, the text recognition in the area of expertise includes a test report of the detection of soil constituents, including various chemical elements, proportions, etc.

In a specific example, the text recognition of the professional field includes a detection report of pesticide residues, including pesticide residue type, composition, proportion, harmfulness, concentration and the like.

In a specific example, the text recognition of the professional field includes names of various traditional Chinese medicines, categories, properties of traditional Chinese medicines, and the like.

In one specific example, the text recognition of the area of expertise includes biological molecular formula, name, structural formula, biological material preparation procedure.

In a specific example, the text recognition of the professional field includes chemical structural formula, chemical name, chemical preparation parameters, conditions.

Taking a chemical examination report as an example, the pictures uploaded by the user are labeled with serial numbers and then subjected to detection classification, wherein the picture classification includes but is not limited to an examination scheme, an examination result, a molecular structural formula and a particle electron microscope picture.

Taking the mapping and navigation field as an example, the images uploaded by the user are labeled with serial numbers and then detected and classified, wherein the image classification includes but is not limited to geographic information, aviation/aerospace images, spliced information images, radar image images and the like.

In a specific example, the specific time, the hospital, the patient name, the doctor name, and the clinical diagnosis result on the picture are identified based on the image detection and identification technology, the picture is subjected to full text identification, and after the text content is further extracted, a logical context is formed through semantic identification.

In a specific example, the text recognition of the specialized field includes disease type, name, whether surgery is performed, diagnosis item, time of occurrence, disease indicator.

Taking the disease report as an example, the images uploaded by the user are labeled with serial numbers and then subjected to detection classification, wherein the image classification includes but is not limited to diagnosis, case report, prescription and hospitalization record.

In a specific example, the specific time, the hospital, the patient name, the doctor name, and the clinical diagnosis result on the picture are identified based on the image detection and identification technology, the picture is subjected to full text identification, and after the text content is further extracted, a logical context is formed through semantic identification.

In a specific example, 10 pictures are uploaded by a user, and are respectively marked 1-10 according to serial numbers, and through image detection and classification, the picture 1 is a diagnosis certificate, the pictures 2-4 are pathological reports, the pictures 5 and 6 are prescriptions, and the pictures 7-10 are hospitalization records.

It should be noted that the pictures uploaded by the user are disordered and cannot be uploaded in sequence, and the file names of the pictures are firstly identified through image detection, then sorted according to the page numbers on the pictures, and finally sorted according to the standard.

In a specific example, 20 pictures are uploaded by a user, the sequence of the pictures is disordered, the head positions of the pictures are firstly identified through an image identification technology, diagnosis proofs, case reports, prescriptions, hospitalization records and the like are identified, the pictures of the diagnosis proofs are identified according to numbers at the lower part or the footer and then sorted, and if the information cannot be identified or is difficult to identify, the content can be sorted through context semantic identification.

As shown in fig. 1, an embodiment of the present specification provides a deep learning-based data processing method, including:

s101, collecting picture materials uploaded by a user and sending the picture materials to a data center processing system;

s102, detecting, classifying and identifying the pictures;

s103, completing the modeling of the service data of each picture and restoring service information;

s104, recognizing text entities in the professional business field, and restoring behaviors and paths of users;

and S105, generating an independent entity by using a knowledge graph and a search algorithm and carrying out standardization processing.

An embodiment of the specification provides a data processing method based on deep learning, which is applied to a server side, acquires picture materials uploaded by a user through terminal equipment and sends the picture materials to a data center processing system, the pictures are detected, classified and identified, business data modeling of each picture is completed in the data center processing system, business information is restored, a natural language processing pre-training model is used for text entity identification in the medical professional field, medical behaviors and paths of the user are restored, an integral medical database is formed, and an independent entity is generated and standardized through a knowledge map and a search algorithm. And preparing diagnosis decision factors, simulating insurance claim settlement examination by using a rule engine through factor reasoning decision, and performing decision tree logic execution to obtain an intelligent examination conclusion.

In a specific example, according to the medical image data of the user, the conclusion of insurance claim settlement is automatically given by combining the previous insurance application and insurance clauses, including whether the insurance is paid or not, the amount of the payment and the like. The relevant conclusions are automatically pushed to the collaborating insurance companies or insurance platforms.

An embodiment of the specification provides a data processing method based on deep learning, which is applied to a server side, acquires picture materials uploaded by a user through terminal equipment and sends the picture materials to a data center processing system, the pictures are detected, classified and identified, business data modeling of each picture is completed in the data center processing system, medical information is restored, a natural language processing pre-training model is used for text entity identification in the medical professional field, medical behaviors and paths of the user are restored, an integral medical database is formed, and an independent entity is generated and standardized through a knowledge map and a search algorithm. And calculating a recommendation decision factor, calculating a decision through the factor, and obtaining an insurance recommendation combination by using a recommendation algorithm.

In one specific example, the insurance includes health insurance, human insurance, and the various insurance includes health condition notification.

As shown in fig. 2, an embodiment of the present specification provides a deep learning-based data processing method, including:

s201, collecting picture materials uploaded by a user and sending the picture materials to a data center processing system;

s202, detecting, classifying and identifying the pictures;

s203, completing the modeling of the business data of each picture, and restoring medical information;

s204, text entity recognition in the medical professional field is carried out, and medical behaviors and paths of the user are restored;

s205, generating an independent entity by using a knowledge graph and a search algorithm and carrying out standardized processing;

s206, providing insurance claims and recommending insurance services for the user according to the hospitalization portrait data of the user.

An embodiment of the specification provides a data processing method based on deep learning, which is applied to a server side, acquires picture materials uploaded by a user through terminal equipment and sends the picture materials to a data center processing system, the pictures are detected, classified and identified, business data modeling of each picture is completed in the data center processing system, medical information is restored, a natural language processing pre-training model is used for text entity identification in the medical professional field, medical behaviors and paths of the user are restored, an integral medical database is formed, and an independent entity is generated and standardized through a knowledge map and a search algorithm. And establishing a rehabilitation data model, and providing related rehabilitation instruments and a suggested scheme through model calculation.

In a specific example, the corresponding rehabilitation apparatus or suggestion is given through the recommendation algorithm model and imported into the corresponding third-party electronic commerce platform, such as the Ali health, the Jingdong health and the like.

As shown in fig. 3, an embodiment of the present specification provides a deep learning-based data processing method, including:

s301, collecting picture materials uploaded by a user and sending the picture materials to a data center processing system;

s302, detecting, classifying and identifying the pictures;

s303, completing the modeling of the business data of each picture, and restoring medical information;

s304, text entity recognition in the medical professional field is carried out, and medical behaviors and paths of the user are restored;

s305, generating an independent entity by using a knowledge graph and a search algorithm and carrying out standardized processing;

and S306, providing rehabilitation and health care product recommendation service for the user according to the hospitalization portrait data of the user.

An embodiment of the specification provides a data processing method based on deep learning, which is applied to an internet platform, acquires user authorization permission based on terminal equipment, acquires picture materials uploaded by a user and sends the picture materials to a data center processing system of a background server, the data center processing system detects, classifies and identifies pictures, completes business data modeling of each picture, restores medical information, utilizes a natural language processing pre-training model to recognize text entities in the medical professional field, restores medical behaviors and paths of the user, forms an integral medical image database of the user, utilizes a knowledge map and a search algorithm to generate independent entities and perform standardized processing, and combines application products of the internet platform to output a specific solution to the user.

In some embodiments, the internet platform simulates insurance claim settlement audit by using a rule engine through a diagnosis decision model, performs decision tree logic execution, and obtains and outputs an intelligent audit conclusion to a user.

In some embodiments, the internet platform calculates a recommendation decision factor, calculates a decision by the factor, and obtains an insurance recommendation combination by using a recommendation algorithm.

In some embodiments, the internet platform provides relevant rehabilitation instruments and suggested protocols through model calculations based on the rehabilitation data model.

In some embodiments, the internet platform performs detection classification based on serial number tagging of pictures uploaded by the user, including but not limited to, diagnostic evidence, case reports, prescriptions, and hospital records.

In some embodiments, the internet platform identifies specific time, hospital, patient name, doctor name, and clinical diagnosis result on the picture based on image detection and identification technology, performs full text identification on the picture, further extracts text content, and forms logical context through semantic identification.

As shown in fig. 4, an embodiment of the present specification provides a data processing system based on deep learning, which includes at least one terminal device, at least one internet platform, and at least one server, where the terminal device collects picture materials uploaded by a user, the internet platform obtains user authorization based on the terminal device, collects the picture materials uploaded by the user, and sends the picture materials to a data center processing system of a backend server, the data center processing system detects, classifies, and identifies the pictures, completes business data modeling for each picture, restores medical information, utilizes a natural language processing pre-training model to perform text entity identification in the medical professional field, restores medical behaviors and paths of the user, forms an overall medical image database of the user, utilizes a knowledge graph and a search algorithm to generate an independent entity, and performs standardized processing, and outputting a specific solution to a user by combining an application product of the Internet platform.

As shown in fig. 5, an embodiment of the present specification provides a data processing system based on deep learning, which includes at least one terminal device, at least one internet platform, at least one insurance platform or insurance company, and at least one server, where the terminal device acquires picture materials uploaded by a user, the internet platform acquires user authorization permission based on the terminal device, acquires the picture materials uploaded by the user, and sends the picture materials to a data center processing system of the background server, the data center processing system detects, classifies, and identifies pictures, completes modeling of business data of each picture, restores medical information, performs text entity identification in a medical professional field by using a natural language processing pre-training model, restores medical behaviors and paths of the user, forms an overall medical picture of the user, and uses a knowledge graph and a search algorithm, separate entities are generated and processed in a standardized manner. The Internet platform carries out data sharing with an insurance platform or an insurance company, and the Internet platform automatically gives conclusions of insurance claims including whether the insurance claims are paid or not, the amount of the claims and the like according to the hospitalizing image data of the user and by combining earlier insurance application and insurance clauses. The relevant conclusions are automatically pushed to the collaborating insurance companies or insurance platforms.

In a specific example, the internet platform performs data sharing with an insurance platform or an insurance company, and the internet platform recommends corresponding insurance including health insurance, life insurance and the like to the user according to the hospitalizing image data of the user and in combination with an insurance actuarial model of the insurance platform or the insurance company, wherein the various insurance includes health condition notification.

As shown in fig. 6, an embodiment of the present specification provides a data processing system based on deep learning, which includes at least one terminal device, at least one internet platform, at least one insurance platform or insurance company, and at least one server, where the terminal device collects picture materials uploaded by a user, the internet platform obtains user authorization permission based on the terminal device, collects the picture materials uploaded by the user, and sends the picture materials to a data center processing system of the background server, the data center processing system detects, classifies, and identifies pictures, completes modeling of business data of each picture, restores medical information, performs text entity identification in a medical professional field by using a natural language processing pre-training model, restores medical behaviors and paths of the user, forms an overall medical picture of the user, and uses a knowledge graph and a search algorithm, separate entities are generated and processed in a standardized manner. The internet platform is connected with a third-party e-commerce platform, and gives corresponding rehabilitation instruments or suggestions through a recommendation algorithm model according to the hospitalizing portrait data of the user and introduces product links of the corresponding third-party e-commerce platform, such as Ali health, Jingdong health and the like.

In some specific examples, such as nerve decline, sleep disorder, cardiovascular disease, etc., products or health products that help relieve stress and improve sleep quality, such as electrocardiographs, health pillows, melatonin, etc., can be given.

In some specific examples, the system marks serial numbers on the pictures uploaded by the user before performing detection classification, wherein the picture classification includes but is not limited to diagnosis, case report, prescription and hospitalization record.

In some specific examples, the system identifies specific time, hospital, patient name, doctor name, and clinical diagnosis result on the picture based on image detection and identification technology, performs full text identification on the picture, further extracts text content, and forms logical context through semantic identification.

One embodiment of the present specification provides a computer readable storage medium having stored thereon a computer program/instructions which, when executed by a processor, implement the method of: the method comprises the steps of collecting picture materials uploaded by a user through terminal equipment, sending the picture materials to a data center processing system, detecting, classifying and identifying the pictures, completing business data modeling of each picture in the data center processing system, restoring medical information, utilizing a natural language to process a pre-training model, identifying text entities in the medical professional field, restoring medical behaviors and paths of the user, forming an integral medical image database of the user, and utilizing a knowledge map and a search algorithm to generate independent entities and carry out standardized processing.

One embodiment of the present specification provides a computer program product comprising computer programs/instructions which, when executed by a processor, implement the method of: the method comprises the steps of collecting picture materials uploaded by a user through terminal equipment, sending the picture materials to a data center processing system, detecting, classifying and identifying the pictures, completing business data modeling of each picture in the data center processing system, restoring medical information, utilizing a natural language to process a pre-training model, identifying text entities in the medical professional field, restoring medical behaviors and paths of the user, forming an integral medical image database of the user, and utilizing a knowledge map and a search algorithm to generate independent entities and carry out standardized processing.

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 obtaining user authorization permission based on terminal equipment, collecting picture materials uploaded by a user and sending the picture materials to a data center processing system of a background server, detecting, classifying and identifying the pictures by the data center processing system, completing business data modeling of each picture, restoring medical information, utilizing a natural language to process a pre-training model, performing text entity identification in the medical professional field, restoring medical behaviors and paths of the user, forming an integral medical image database of the user, utilizing a knowledge graph and a search algorithm to generate an independent entity and perform standardized processing, and combining an application product of an internet platform to output a specific solution to the user.

Based on the deep learning principle, the medical related data uploaded by the user is identified and processed through the picture, the text information is automatically identified and sequenced, the medical information of the user is restored by combining a knowledge map in the medical professional field after model training, an integral medical image can be constructed, objective data are provided for subsequent product application, and the problem of data loss or data counterfeiting is effectively solved.

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 Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that 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 phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises 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, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

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. 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.