CN117037197A

CN117037197A - Abnormal identification method, device, equipment and storage medium based on artificial intelligence

Info

Publication number: CN117037197A
Application number: CN202311097421.6A
Authority: CN
Inventors: 郁君俊
Original assignee: Ping An Health Insurance Company of China Ltd
Current assignee: Ping An Health Insurance Company of China Ltd
Priority date: 2023-08-28
Filing date: 2023-08-28
Publication date: 2023-11-10

Abstract

The application belongs to the field of artificial intelligence and the field of digital medical treatment, and relates to an anomaly identification method based on artificial intelligence, which comprises the following steps: preprocessing medical image materials of a nuclear security case to obtain a first image; performing layout analysis on the first image to obtain a second image; performing character cutting on the second image to obtain a third image; character feature extraction is carried out on the third image to obtain character data; performing layout recovery on the text data to obtain target text data and filling the target text data into an initial image; performing anomaly identification on the target text data to obtain anomaly index data; and marking the abnormal index data to obtain a target image and displaying the target image. The application also provides an anomaly identification device based on the artificial intelligence, a computer device and a storage medium. In addition, the application also relates to a blockchain technology, and the target image can be stored in the blockchain. The application can be applied to the abnormal index identification scene in the digital medical field, effectively improves the verification efficiency and improves the verification processing flexibility.

Description

Abnormal identification method, device, equipment and storage medium based on artificial intelligence

Technical Field

The application relates to the technical field of artificial intelligence development and the field of digital medical treatment, in particular to an anomaly identification method, an anomaly identification device, computer equipment and a storage medium based on artificial intelligence.

Background

The insurance policy refers to the process of judging and classifying the insurance risk after the policy is generated, and then determining whether to underwire or not and under what conditions. As insurance continues to be popular in today's life, underwriting is becoming an important business within insurance companies.

In the conventional artificial insurance system of the insurance enterprise, the insurance specialist needs to face different kinds of medical materials, such as physical examination report, discharge nubs, medicine list and the like. Taking physical examination report as an example, report formats of different physical examination institutions in different hospitals are different, and each report contains a large number of medical indexes and has long pages. When the physical examination report needs to be checked, a check expert needs to spend a great deal of effort to check abnormal indexes in the physical examination report so as to determine whether the person to be checked has a warranty benefit, and the manual check mode has the problems of low check efficiency and poor flexibility.

Disclosure of Invention

The embodiment of the application aims to provide an abnormal identification method, device, computer equipment and storage medium based on artificial intelligence, so as to solve the technical problems that the existing artificial verification method needs to spend a great deal of effort to distinguish abnormal indexes in a policy to determine whether a person to be protected has a protectable benefit, and thus the verification efficiency is low and the flexibility is poor.

In order to solve the technical problems, the embodiment of the application provides an anomaly identification method based on artificial intelligence, which adopts the following technical scheme:

receiving medical image materials of the nuclear security cases input by a user;

preprocessing the medical image material to obtain a corresponding first image;

performing layout analysis processing on the first image to obtain a second image;

performing character cutting processing on the second image to obtain a third image;

extracting character features of the third image to obtain corresponding text data;

performing layout recovery processing on the text data to obtain target text data, and filling the target text data into a preset initial image;

performing anomaly identification on the target text data in the initial image to obtain corresponding anomaly index data;

and marking the abnormal index data in the initial image to obtain a target image, and displaying the target image in a target page so that auditing personnel can audit the target image.

Further, the step of preprocessing the medical image material to obtain a corresponding first image specifically includes:

Performing binarization processing on the medical image material to obtain a first designated image;

noise removing is carried out on the first appointed image, and a second appointed image is obtained;

performing inclination correction on the second designated image to obtain a third designated image;

and taking the third designated image as the first image.

Further, the step of performing layout recovery processing on the text data to obtain target text data, and filling the target text data into a preset initial image specifically includes:

acquiring position information of the text data in the third image;

performing layout recovery processing on the text data based on the position information to obtain processed text data;

taking the processed text data as the target text data;

and filling the target text data into the initial image.

Further, the step of performing anomaly recognition on the target text data in the initial image to obtain corresponding anomaly index data specifically includes:

acquiring a target index matched with the index field from the target text data based on a preset index field;

Acquiring an index verification rule corresponding to the target index;

acquiring an index parameter value corresponding to the target index from the target text data;

checking the index parameter value based on the index checking rule, and judging whether the index parameter value accords with the index checking rule;

and if the index checking rule is not met, determining the index parameter value as abnormal index data.

Further, the step of marking the abnormal index data in the initial image to obtain a target image specifically includes:

determining color information;

performing highlighting processing on the abnormal index data in the initial image based on the color information to obtain a processed initial image;

and taking the processed initial image as the target image.

Further, after the step of marking the abnormal index data in the initial image to obtain a target image and displaying the target image in a target page so that an auditor performs audit processing on the target image, the method further includes:

receiving a target auditing result corresponding to the target image, which is generated by the auditing personnel;

Judging whether the target audit result is a return result or not;

if yes, acquiring the data description information carried in the return result;

generating corresponding data supplementary information based on the data description information;

pushing the profile supplemental information to the user.

Further, after the step of pushing the profile supplemental information to the user, the method further comprises:

receiving target data returned by the user based on the data supplementary information;

performing character recognition on the target data to generate a corresponding recognition result;

and generating a verification result corresponding to the verification case based on the identification result and the target image.

In order to solve the technical problems, the embodiment of the application also provides an anomaly identification device based on artificial intelligence, which adopts the following technical scheme:

the first receiving module is used for receiving medical image materials of the nuclear security case input by a user;

the first processing module is used for preprocessing the medical image material to obtain a corresponding first image;

the second processing module is used for carrying out layout analysis processing on the first image to obtain a second image;

the third processing module is used for performing character cutting processing on the second image to obtain a third image;

The extraction module is used for extracting character features of the third image to obtain corresponding text data;

the fourth processing module is used for carrying out layout recovery processing on the text data to obtain target text data, and filling the target text data into a preset initial image;

the identification module is used for carrying out abnormal identification on the target text data in the initial image to obtain corresponding abnormal index data;

the display module is used for marking the abnormal index data in the initial image to obtain a target image, and displaying the target image in a target page so that auditing personnel can conduct auditing treatment on the target image.

In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical schemes:

preprocessing the medical image material to obtain a corresponding first image;

In order to solve the above technical problems, an embodiment of the present application further provides a computer readable storage medium, which adopts the following technical schemes:

preprocessing the medical image material to obtain a corresponding first image;

Compared with the prior art, the embodiment of the application has the following main beneficial effects:

the embodiment of the application firstly receives medical image materials of the nuclear protection case input by a user; preprocessing the medical image material to obtain a corresponding first image; performing layout analysis processing on the first image to obtain a second image; performing character cutting processing on the second image to obtain a third image; then extracting character features of the third image to obtain corresponding text data; performing layout recovery processing on the text data to obtain target text data, and filling the target text data into a preset initial image; subsequently, carrying out anomaly identification on the target text data in the initial image to obtain corresponding anomaly index data; and finally, marking the abnormal index data in the initial image to obtain a target image, and displaying the target image in a target page so that auditing personnel can audit the target image. According to the embodiment of the application, the medical image material of the nuclear protection case is automatically processed and the abnormality identification is processed to generate the target image containing the abnormality index data after the marking processing, and the target image is displayed in the target page, so that an auditor can clearly review the abnormality index data in the target image, and further the audit processing of the nuclear protection case is completed according to the abnormality index data, the nuclear protection efficiency is effectively improved, the flexibility of the nuclear protection processing is improved, and the satisfaction of users is facilitated to be improved.

Drawings

In order to more clearly illustrate the solution of the present application, a brief description will be given below of the drawings required for the description of the embodiments of the present application, it being apparent that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without the exercise of inventive effort for a person of ordinary skill in the art.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

FIG. 2 is a flow chart of one embodiment of an artificial intelligence based anomaly identification method in accordance with the present application;

FIG. 3 is a schematic diagram of one embodiment of an artificial intelligence based anomaly identification device in accordance with the present application;

FIG. 4 is a schematic structural diagram of one embodiment of a computer device in accordance with the present application.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

In order to make the person skilled in the art better understand the solution of the present application, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to the accompanying drawings.

As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.

The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving Picture Experts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.

It should be noted that, the anomaly identification method based on artificial intelligence provided by the embodiment of the application is generally executed by a server/terminal device, and correspondingly, the anomaly identification device based on artificial intelligence is generally arranged in the server/terminal device.

The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.

Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to FIG. 2, a flowchart of one embodiment of an artificial intelligence based anomaly identification method in accordance with the present application is shown. The order of the steps in the flowchart may be changed and some steps may be omitted according to various needs. The abnormal identification method based on the artificial intelligence provided by the embodiment of the application can be applied to any scene needing abnormal index identification, and can be applied to products of the scenes, such as abnormal index identification in the digital medical field. The anomaly identification method based on artificial intelligence comprises the following steps:

Step S201, receiving medical image materials of the underwriting case input by the user.

In this embodiment, the electronic device (e.g., the server/terminal device shown in fig. 1) on which the anomaly identification method based on artificial intelligence operates may acquire the medical image material through a wired connection manner or a wireless connection manner. The specific execution subject may be an expert underwriting system within the electronic device. It should be noted that the wireless connection may include, but is not limited to, 3G/4G/5G connection, wiFi connection, bluetooth connection, wiMAX connection, zigbee connection, UWB (ultra wideband) connection, and other now known or later developed wireless connection. The user may be an applicant, and the case with the underwriting may be a case generated after processing of new contract, reapplication, security, claim settlement, and the like. In the medical application scenario, the medical image material may be a physical examination report, a discharge summary, a drug list, and the like. In addition, there are different storage formats and different compression modes for different types of image formats.

Step S202, preprocessing the medical image material to obtain a corresponding first image.

In this embodiment, the above-mentioned specific implementation process of preprocessing the medical image material to obtain the corresponding first image will be described in further detail in the following specific embodiments, which will not be described herein.

Step S203, performing layout analysis processing on the first image to obtain a second image.

In this embodiment, the layout analysis refers to a process of dividing the text in the first image into segments.

And step S204, performing character cutting processing on the second image to obtain a third image.

In this embodiment, the third image may be obtained by calling a character recognition software based on a character recognition algorithm (for example, OCR algorithm), and then performing character cutting processing on the second image using the character recognition software pair. The character recognition software is software with a character cutting function, and can effectively solve the problem that characters are adhered and broken in an image due to the limitation of photographing conditions.

Step S205, extracting character features of the third image to obtain corresponding text data.

In this embodiment, after the character recognition software is used to perform character cutting processing on the second image to obtain a third image, character feature extraction may be performed on the third image affected by factors such as pen breaking, adhesion, rotation, and the like, so as to obtain corresponding text data.

Step S206, performing layout recovery processing on the text data to obtain target text data, and filling the target text data into a preset initial image.

In this embodiment, the above-mentioned implementation process of performing layout recovery processing on the text data to obtain target text data, and filling the target text data into a preset initial image will be described in further detail in the following embodiments, which will not be described herein.

Step S207, performing anomaly identification on the target text data in the initial image, to obtain corresponding anomaly index data.

In this embodiment, before the anomaly identification is performed on the target text data, correction processing may be performed on the target text data according to the language context relationship of the target text data, so as to obtain corrected target text data, thereby effectively ensuring the data accuracy of the target text data. The specific implementation process of performing the anomaly identification on the target text data in the initial image to obtain the corresponding anomaly index data will be described in further detail in the following specific embodiments, which are not described herein.

And step S208, marking the abnormal index data in the initial image to obtain a target image, and displaying the target image in a target page so that an auditing person can audit the target image.

In this embodiment, the auditor is a underwriting expert, and the target page is a working page of an audit workbench used by the auditor. After the generated target image is displayed on the working page of the auditing workbench, the auditing personnel can review the target image through the working page and manually analyze the target image to generate a verification result corresponding to the verification case. The specific implementation process of marking the abnormal index data in the initial image to obtain the target image will be described in further detail in the following specific embodiments, which will not be described herein. In addition, the expert verification system has the characteristic of high recognition throughput, the expert verification system supports the one-time feeding of a large amount of image data, and the analyzed abnormal indexes are ready to be in place before the verification of the verification expert, so that the improvement greatly simplifies repeated recognition actions in the manual operation process, improves the circulation timeliness of human cases, helps enterprises to improve the service quality, and improves the customer satisfaction.

Firstly, receiving medical image materials of a nuclear protection case input by a user; preprocessing the medical image material to obtain a corresponding first image; performing layout analysis processing on the first image to obtain a second image; performing character cutting processing on the second image to obtain a third image; then extracting character features of the third image to obtain corresponding text data; performing layout recovery processing on the text data to obtain target text data, and filling the target text data into a preset initial image; subsequently, carrying out anomaly identification on the target text data in the initial image to obtain corresponding anomaly index data; and finally, marking the abnormal index data in the initial image to obtain a target image, and displaying the target image in a target page so that auditing personnel can audit the target image. According to the application, the medical image material of the nuclear protection case is automatically processed and the abnormality identification is processed to generate the target image containing the marked abnormality index data, and the target image is displayed in the target page, so that an auditor can clearly review the abnormality index data in the target image, and further the audit processing of the nuclear protection case is completed according to the abnormality index data, thereby effectively improving the nuclear protection efficiency, improving the flexibility of the nuclear protection processing, and being beneficial to improving the satisfaction of users.

In some alternative implementations, step S202 includes the steps of:

and performing binarization processing on the medical image material to obtain a first designated image.

In this embodiment, the binarization processing refers to processing a color picture into foreground information and background information, where the foreground information is simply defined as black and the background information is simply defined as white.

And removing noise from the first specified image to obtain a second specified image.

In this embodiment, according to actual service usage requirements, different noise characteristics are defined for different medical nuclear data in advance, and corresponding denoising is performed. The method comprises the steps of firstly obtaining a target type of the medical image material, then obtaining a target noise characteristic corresponding to the target type, and denoising noise characteristics matched with the target noise characteristic in the first designated image based on the target noise characteristic to obtain a second designated image.

And performing inclination correction on the second designated image to obtain a third designated image.

In the present embodiment, tilt correction means correction processing of a tilt image obtained at the time of photographing.

And taking the third designated image as the first image.

The method comprises the steps of performing binarization processing on the medical image material to obtain a first appointed image; then, noise removal is carried out on the first appointed image, and a second appointed image is obtained; and subsequently, performing inclination correction on the second designated image to obtain a third designated image, and taking the third designated image as the first image. The application can realize quick and accurate generation of the corresponding first image by performing binarization processing, noise removal and inclination correction on the medical image material to complete pretreatment on the medical image material.

In some alternative implementations of the present embodiment, step S206 includes the steps of:

and acquiring the position information of the text data in the third image.

In this embodiment, the position information may refer to paragraph information, order information, and the like of the text data in the third image.

And performing layout recovery processing on the text data based on the position information to obtain the processed text data.

In this embodiment, the layout recovery processing refers to adjusting the layout of the text data to ensure that the identified text and original image, i.e. the medical image material has unchanged paragraphs, unchanged positions and unchanged sequences compared with each other.

And taking the processed text data as the target text data.

And filling the target text data into the initial image.

The application obtains the position information of the text data in the third image; then, performing layout recovery processing on the text data based on the position information to obtain processed text data; then taking the processed text data as the target text data; and filling the target text data into the initial image. According to the application, by acquiring the position information of the text data in the third image and performing layout recovery processing on the text data according to the position information, accurate target text data can be quickly and accurately generated, so that a corresponding target image can be quickly constructed based on the target text data.

In some alternative implementations, step S207 includes the steps of:

and acquiring a target index matched with the index field from the target text data based on a preset index field.

In this embodiment, all indexes of the index database may be used to perform data matching on the target text data, and the specified index that is successfully matched may be screened out and used as the index field. The index database is constructed according to actual service use requirements and stores a plurality of indexes. Illustratively, in the field of digital medicine, the above-mentioned indicators may include: blood glucose level, heart rate, blood pressure, etc.

And acquiring an index checking rule corresponding to the target index.

In this embodiment, a preset rule base may be called first, and rules corresponding to the target index are screened from the rule base, so as to obtain the index verification rule. Wherein, for each different index, a reference data range corresponding to the index is pre-established, and an index verification rule corresponding to the index is generated based on the reference data range.

And acquiring an index parameter value corresponding to the target index from the target text data.

And verifying the index parameter value based on the index verification rule, and judging whether the index parameter value accords with the index verification rule.

In this embodiment, the reference data range included in the index verification rule may be obtained, and whether the index parameter value is within the reference data range may be determined, and if the index parameter value is within the reference data range, it may be determined that the index parameter meets the index verification rule; and if the index parameter value is not in the reference data range, judging that the index parameter does not accord with the index checking rule.

In this embodiment, if the index parameter value meets the index verification rule, it indicates that the index parameter value of the target index belongs to normal index data.

The method comprises the steps of obtaining target indexes matched with index fields from target text data based on preset index fields; then obtaining an index checking rule corresponding to the target index; then, acquiring an index parameter value corresponding to the target index from the target text data; subsequently, the index parameter value is verified based on the index verification rule, and whether the index parameter value accords with the index verification rule is judged; and if the index checking rule is not met, determining the index parameter value as abnormal index data. According to the method and the device, the index parameter value corresponding to the target index is verified based on the index field and the index verification rule, so that the abnormal identification of the target text data can be rapidly and accurately completed, the corresponding abnormal index data is obtained, and the acquisition efficiency and the acquisition intelligence of the abnormal index data are improved.

In some optional implementations, the marking the anomaly index data in the initial image in step S208 includes the steps of:

Color information is determined.

In this embodiment, the selection of the color information is not specifically limited, and may be set according to an actual service requirement or according to a personal use requirement of a user, for example, may be set to yellow, red, or the like.

And carrying out highlighting processing on the abnormal index data in the initial image based on the color information to obtain a processed initial image.

In this embodiment, after the highlighting process is performed on the abnormal index data, a reminder process such as thickening may be further performed on the abnormal index data.

And taking the processed initial image as the target image.

The application determines the color information; and subsequently, carrying out highlighting processing on the abnormal index data in the initial image based on the color information to obtain a processed initial image, and taking the processed initial image as the target image. The present application performs highlighting processing on abnormality index data in an initial image based on predetermined color information to intelligently generate a target image. The abnormal index data in the target image is highlighted, so that subsequent auditors can quickly and conveniently review the abnormal index data in the target image, and further audit the target image based on the abnormal index data, so that the verification efficiency of the medical image material for the verification case can be effectively improved, and the working experience of the auditors is improved.

In some optional implementations of this embodiment, after step S208, the electronic device may further perform the following steps:

and receiving a target auditing result corresponding to the target image, which is generated by the auditing personnel.

In this embodiment, the target audit result may include audit pass results or return results.

And judging whether the target auditing result is a return result or not.

In this embodiment, if the target audit result is a return result, the auditor, that is, the underwriting expert feels necessary in the process of audit, can issue a health function, a physical examination function, a supplementary data function, etc. to the corresponding insured person for return. Wherein, the return result carries the information description information.

If yes, acquiring the data description information carried in the return result.

In this embodiment, the information analysis may be performed on the return result to extract the data description information from the return result. For example, in a digital medical scenario, the profile description information may include a physical examination report.

And generating corresponding data supplementary information based on the data description information.

In this embodiment, the foregoing data description information may be filled into the corresponding position in the supplemental information template by calling a preset supplemental information template, so as to generate the corresponding data supplemental information. The supplementary information template is an information template which is pre-constructed according to the actual data supplementary service requirement.

Pushing the profile supplemental information to the user.

In this embodiment, the information may be obtained by obtaining the communication information of the user, and then pushing the data supplementary information to the user based on the communication information.

The application receives the target auditing result corresponding to the target image, which is generated by the auditing personnel; then judging whether the target audit result is a return result or not; if yes, acquiring the data description information carried in the return result; generating corresponding data supplementary information based on the data description information; and pushing the data supplementary information to the user. According to the application, after the target auditing result corresponding to the target image generated by the auditing personnel is detected to be the return result, the return result is intelligently analyzed, the corresponding data supplementary information is generated, and the data supplementary information is pushed to the user, so that the user is intelligently reminded of carrying out the corresponding data supplementary processing, and the accuracy of the nuclear protection processing on the nuclear protection case can be improved according to the supplementary data returned by the user.

In some optional implementations of this embodiment, after the step of pushing the profile supplemental information to the user, the electronic device may further perform the steps of:

And receiving target data returned by the user based on the data supplementary information.

And performing character recognition on the target data to generate a corresponding recognition result.

In this embodiment, a conventional word recognition algorithm, for example, an OCR algorithm, may be used to perform word recognition on the target data, so as to generate a corresponding recognition result.

In this embodiment, after the identification result is obtained, the identification result and the target image may be pushed to an auditing workbench of an auditor, so that the auditor may review the identification result and the target image based on the auditing workbench and generate a final verification result corresponding to the user's verification case.

The application receives the target data returned by the user based on the data supplementary information; then, performing character recognition on the target data to generate a corresponding recognition result; and generating a verification result corresponding to the verification case based on the identification result and the target image. According to the application, the corresponding identification result is generated by carrying out text identification on the target data returned by the user, and further, the verification result and the target image are analyzed together to generate the verification result corresponding to the verification case, so that the data accuracy of the verification result is effectively improved.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.

It is emphasized that the target image may also be stored in a blockchain node in order to further ensure privacy and security of the target image.

The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by computer readable instructions stored in a computer readable storage medium that, when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.

With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an anomaly identification device based on artificial intelligence, where the embodiment of the device corresponds to the embodiment of the method shown in fig. 2, and the device is particularly applicable to various electronic devices.

As shown in fig. 3, the anomaly identification device 300 based on artificial intelligence according to the present embodiment includes: a first receiving module 301, a first processing module 302, a second processing module 303, a third processing module 304, an extracting module 305, a fourth processing module 306, an identifying module 307, and a presentation module 308. Wherein:

a first receiving module 301, configured to receive medical image materials of a nuclear security case input by a user;

a first processing module 302, configured to pre-process the medical image material to obtain a corresponding first image;

a second processing module 303, configured to perform layout analysis processing on the first image to obtain a second image;

a third processing module 304, configured to perform character cutting processing on the second image to obtain a third image;

the extracting module 305 is configured to perform character feature extraction on the third image to obtain corresponding text data;

A fourth processing module 306, configured to perform layout recovery processing on the text data to obtain target text data, and fill the target text data into a preset initial image;

the identifying module 307 is configured to perform anomaly identification on the target text data in the initial image, so as to obtain corresponding anomaly index data;

the display module 308 is configured to perform marking processing on the abnormal index data in the initial image to obtain a target image, and display the target image in a target page, so that an auditor performs auditing processing on the target image.

In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the artificial intelligence-based anomaly identification method in the foregoing embodiment one by one, and are not described herein again.

In some alternative implementations of the present embodiment, the first processing module 302 includes:

the first processing sub-module is used for carrying out binarization processing on the medical image material to obtain a first appointed image;

the second processing sub-module is used for removing noise from the first specified image to obtain a second specified image;

the third processing sub-module is used for carrying out inclination correction on the second designated image to obtain a third designated image;

And the first determining submodule is used for taking the third specified image as the first image.

In some alternative implementations of the present embodiment, the fourth processing module 306 includes:

the first acquisition sub-module is used for acquiring the position information of the text data in the third image;

a fourth processing sub-module, configured to perform layout recovery processing on the text data based on the location information, to obtain processed text data;

the second determining submodule is used for taking the processed text data as the target text data;

and the filling sub-module is used for filling the target text data into the initial image.

In some alternative implementations of the present embodiment, the identification module 307 includes:

the second acquisition sub-module is used for acquiring target indexes matched with the index fields from the target text data based on preset index fields;

The third acquisition sub-module is used for acquiring an index verification rule corresponding to the target index;

a fourth obtaining sub-module, configured to obtain an index parameter value corresponding to the target index from the target text data;

the verification sub-module is used for verifying the index parameter value based on the index verification rule and judging whether the index parameter value accords with the index verification rule or not;

and the third determining submodule is used for determining the index parameter value as abnormal index data if the index checking rule is not met.

In some alternative implementations of the present embodiment, the presentation module 308 includes:

a fourth determination sub-module for determining color information;

a fifth processing sub-module, configured to highlight the abnormal index data in the initial image based on the color information, to obtain a processed initial image;

and a fifth determining sub-module, configured to take the processed initial image as the target image.

In some optional implementations of the present embodiment, the artificial intelligence based anomaly identification device further includes:

the second receiving module is used for receiving a target auditing result corresponding to the target image, which is generated by the auditing personnel;

the judging module is used for judging whether the target auditing result is a return result or not;

the acquisition module is used for acquiring the data description information carried in the return sales result if yes;

the first generation module is used for generating corresponding data supplementary information based on the data description information;

and the pushing module is used for pushing the data supplementary information to the user.

the third receiving module is used for receiving target data returned by the user based on the data supplementary information;

The second generation module is used for carrying out character recognition on the target data and generating a corresponding recognition result;

and the third generation module is used for generating a verification result corresponding to the verification case based on the identification result and the target image.

In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.

The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It should be noted that only computer device 4 having components 41-43 is shown in the figures, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.

The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.

The memory 41 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 4. Of course, the memory 41 may also comprise both an internal memory unit of the computer device 4 and an external memory device. In this embodiment, the memory 41 is typically used to store an operating system and various application software installed on the computer device 4, such as computer readable instructions of an anomaly identification method based on artificial intelligence. Further, the memory 41 may be used to temporarily store various types of data that have been output or are to be output.

The processor 42 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as executing computer readable instructions of the anomaly identification method based on artificial intelligence.

The network interface 43 may comprise a wireless network interface or a wired network interface, which network interface 43 is typically used for establishing a communication connection between the computer device 4 and other electronic devices.

in the embodiment of the application, firstly, medical image materials of a nuclear protection case input by a user are received; preprocessing the medical image material to obtain a corresponding first image; performing layout analysis processing on the first image to obtain a second image; performing character cutting processing on the second image to obtain a third image; then extracting character features of the third image to obtain corresponding text data; performing layout recovery processing on the text data to obtain target text data, and filling the target text data into a preset initial image; subsequently, carrying out anomaly identification on the target text data in the initial image to obtain corresponding anomaly index data; and finally, marking the abnormal index data in the initial image to obtain a target image, and displaying the target image in a target page so that auditing personnel can audit the target image. According to the embodiment of the application, the medical image material of the nuclear protection case is automatically processed and the abnormality identification is processed to generate the target image containing the abnormality index data after the marking processing, and the target image is displayed in the target page, so that an auditor can clearly review the abnormality index data in the target image, and further the audit processing of the nuclear protection case is completed according to the abnormality index data, the nuclear protection efficiency is effectively improved, the flexibility of the nuclear protection processing is improved, and the satisfaction of users is facilitated to be improved.

The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the artificial intelligence based anomaly identification method as described above.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.

It is apparent that the above-described embodiments are only some embodiments of the present application, but not all embodiments, and the preferred embodiments of the present application are shown in the drawings, which do not limit the scope of the patent claims. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a thorough and complete understanding of the present disclosure. Although the application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing description, or equivalents may be substituted for elements thereof. All equivalent structures made by the content of the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the scope of the application.

Claims

1. An anomaly identification method based on artificial intelligence is characterized by comprising the following steps:

preprocessing the medical image material to obtain a corresponding first image;

2. The method for identifying abnormalities based on artificial intelligence according to claim 1, wherein said step of preprocessing said medical image material to obtain a corresponding first image comprises:

and taking the third designated image as the first image.

3. The abnormal recognition method based on artificial intelligence according to claim 1, wherein the step of performing layout recovery processing on the text data to obtain target text data and filling the target text data into a preset initial image specifically comprises:

acquiring position information of the text data in the third image;

taking the processed text data as the target text data;

and filling the target text data into the initial image.

4. The abnormal recognition method based on artificial intelligence according to claim 1, wherein the step of performing abnormal recognition on the target text data in the initial image to obtain corresponding abnormal index data specifically comprises:

acquiring an index verification rule corresponding to the target index;

5. The abnormal recognition method based on artificial intelligence according to claim 1, wherein the step of marking the abnormal index data in the initial image to obtain a target image specifically comprises:

determining color information;

and taking the processed initial image as the target image.

6. The abnormal recognition method based on artificial intelligence according to claim 1, wherein the step of marking the abnormal index data in the initial image to obtain a target image and displaying the target image in a target page so that an auditor performs an audit process on the target image further comprises:

judging whether the target audit result is a return result or not;

pushing the profile supplemental information to the user.

7. The artificial intelligence based anomaly identification method of claim 6, further comprising, after the step of pushing the profile supplemental information to the user:

8. An artificial intelligence based anomaly identification device, comprising:

9. A computer device comprising a memory having stored therein computer readable instructions which when executed implement the steps of the artificial intelligence based anomaly identification method of any one of claims 1 to 7.

10. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the artificial intelligence based anomaly identification method of any one of claims 1 to 7.