CN116453125A - Data input method, device, equipment and storage medium based on artificial intelligence - Google Patents
Data input method, device, equipment and storage medium based on artificial intelligence Download PDFInfo
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- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
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Abstract
The embodiment of the application belongs to the field of artificial intelligence, and relates to a data input method based on artificial intelligence, which comprises the following steps: if a material input request is received, analyzing a target material from the material input request, and acquiring the material class of the target material; if the material class belongs to the standard material class, acquiring the data type of the target material; invoking a target extraction model corresponding to the data type, and extracting information from the target material based on the target extraction model to obtain corresponding text information; carrying out credibility check on the text information; and if the text information passes the credibility check, executing the input processing of the target material. The application also provides a data input device, computer equipment and a storage medium based on the artificial intelligence. In addition, the present application relates to blockchain technology in which target materials may be stored. Through this application, effectively improved the processing efficiency of material check-up, guaranteed the accuracy of material check-up, promoted the efficiency that the material was typeeed.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence technologies, and in particular, to an artificial intelligence-based data entry method, apparatus, computer device, and storage medium.
Background
In the insurance claim scene of insurance companies, for claim materials submitted by users, it is generally necessary to perform material verification on the claim materials, and perform material entry processing after the verification is passed. However, the materials submitted by users are usually verified by the existing insurance companies in a manual mode, namely, related logging personnel perform material verification and logging on the materials submitted by the users, so that the workload of the logging personnel is large, the accuracy of material verification cannot be guaranteed, and the efficiency of material logging is low.
Disclosure of Invention
An aim of the embodiment of the application is to provide a data input method, device, computer equipment and storage medium based on artificial intelligence, so as to solve the technical problems that the workload of input personnel is great, the accuracy of material verification cannot be ensured, and the efficiency of material input is low in the existing mode of adopting a manual mode to perform material verification.
In order to solve the technical problems, the embodiment of the application provides a data input method based on artificial intelligence, which adopts the following technical scheme:
Judging whether a material input request submitted by a user is received or not; wherein the material entry request carries a target material;
if the material input request is received, analyzing the target material from the material input request, and acquiring the material class of the target material;
judging whether the material class belongs to a standard material class or not;
if the data type belongs to the standard material category, acquiring the data type of the target material;
invoking a target extraction model corresponding to the data type, and extracting information from the target material based on the target extraction model to obtain corresponding text information;
carrying out credibility verification on the text information;
and if the text information passes the credibility check, executing the input processing of the target material.
Further, the step of verifying the credibility of the text information specifically includes:
acquiring a first field corresponding to a preset field type from the text information;
carrying out standardization processing on the first field to obtain a second field; wherein the number of second fields includes a plurality;
acquiring business rules respectively corresponding to the second fields;
Judging whether each second field accords with a corresponding service rule;
if yes, judging that the text information passes the credibility check, otherwise, judging that the text information does not pass the credibility check.
Further, the step of obtaining the data type of the target material specifically includes:
acquiring preset position information;
extracting keywords from the target material based on the position information to obtain corresponding target keywords;
and determining the data type of the target material based on the target keyword.
Further, before the step of acquiring the data type of the target material, the method further includes:
invoking a preset quality detection model;
detecting the quality of the target material based on the quality detection model to obtain a corresponding quality detection result;
and if the quality detection result is that the quality of the target material passes the verification, executing the step of acquiring the data type of the target material.
Further, before the step of calling the preset quality detection model, the method further includes:
acquiring preset material training data;
training a preset deep learning model based on the material training data to obtain a trained deep learning model;
Constructing material test data based on the material training data;
verifying the trained deep learning model based on the deep learning model, and judging whether the obtained test passing rate is greater than a preset passing rate threshold value or not;
if yes, judging that the trained deep learning model passes verification, and taking the trained deep learning model as the quality detection model.
Further, before the step of calling the target extraction model corresponding to the data type and extracting information from the target material based on the target extraction model to obtain corresponding text information, the method further includes:
determining a compression mode corresponding to the target extraction model;
compressing the model size of the target extraction model based on the compression mode to obtain a compressed target extraction model;
and storing the compressed target extraction model.
Further, after the step of determining whether the material class belongs to the standard material class, the method further includes:
if the material category does not belong to the standard material category, acquiring preset material uploading reminding information;
acquiring the standard material category;
Generating target material reminding information based on the material uploading reminding information and the standard material category;
and displaying the target material reminding information.
In order to solve the technical problem, the embodiment of the application also provides a data input device based on artificial intelligence, which adopts the following technical scheme:
the first judging module is used for judging whether a material input request submitted by a user is received or not; wherein the material entry request carries a target material;
the first acquisition module is used for analyzing the target material from the material input request and acquiring the material category of the target material if the material input request is received;
the second judging module is used for judging whether the material category belongs to a standard material category or not;
the second acquisition module is used for acquiring the data type of the target material if the data type belongs to the standard material category;
the extraction module is used for calling a target extraction model corresponding to the data type, and extracting information from the target material based on the target extraction model to obtain corresponding text information;
the verification module is used for verifying the credibility of the text information;
And the processing module is used for executing the input processing of the target material if the text information passes the credibility check.
In order to solve the above technical problems, the embodiments of the present application further provide a computer device, which adopts the following technical schemes:
judging whether a material input request submitted by a user is received or not; wherein the material entry request carries a target material;
if the material input request is received, analyzing the target material from the material input request, and acquiring the material class of the target material;
judging whether the material class belongs to a standard material class or not;
if the data type belongs to the standard material category, acquiring the data type of the target material;
invoking a target extraction model corresponding to the data type, and extracting information from the target material based on the target extraction model to obtain corresponding text information;
carrying out credibility verification on the text information;
and if the text information passes the credibility check, executing the input processing of the target material.
In order to solve the above technical problems, embodiments of the present application further provide a computer readable storage medium, which adopts the following technical solutions:
Judging whether a material input request submitted by a user is received or not; wherein the material entry request carries a target material;
if the material input request is received, analyzing the target material from the material input request, and acquiring the material class of the target material;
judging whether the material class belongs to a standard material class or not;
if the data type belongs to the standard material category, acquiring the data type of the target material;
invoking a target extraction model corresponding to the data type, and extracting information from the target material based on the target extraction model to obtain corresponding text information;
carrying out credibility verification on the text information;
and if the text information passes the credibility check, executing the input processing of the target material.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
when receiving a material input request submitted by a user, the embodiment of the application firstly analyzes the target material from the material input request and acquires the material category of the target material; then judging whether the material category belongs to a standard material category or not; if the data type belongs to the standard material category, acquiring the data type of the target material; then, a target extraction model corresponding to the data type is called, and information extraction is carried out on the target material based on the target extraction model to obtain corresponding text information; subsequently, carrying out credibility verification on the text information; and if the text information passes the credibility check, executing the input processing of the target material. According to the embodiment of the application, the verification mode of carrying out material verification based on the combination of the target extraction model and the credibility verification replaces the existing manual verification mode of carrying out material verification, so that the workload of the input personnel is greatly reduced, the processing efficiency of the material verification is effectively improved, the accuracy of the material verification is ensured, and the efficiency of material input is improved.
Drawings
For a clearer description of the solution in the present application, a brief description will be given below of the drawings that are needed in the description of the embodiments of the present application, it being obvious 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 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 data entry method according to the present application;
FIG. 3 is a schematic structural view of one embodiment of an artificial intelligence based data entry device according to the present application;
FIG. 4 is a schematic structural diagram of one embodiment of a computer device according to 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 and claims of the present application and in the description of the figures above are intended to cover non-exclusive inclusions. 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 present 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 better understand the technical solutions of the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application 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 data input method based on artificial intelligence provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, the data input device based on artificial intelligence is generally disposed in the server/terminal device.
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 flow chart of one embodiment of an artificial intelligence based data entry method according to the present application is shown. The artificial intelligence-based data input method comprises the following steps:
Step S201, judging whether a material input request submitted by a user is received; wherein the material entry request carries a target material.
In this embodiment, the electronic device (e.g., the server/terminal device shown in fig. 1) on which the artificial intelligence-based data entry method operates may acquire the target material through a wired connection manner or a wireless connection manner. 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. Wherein the user may submit a material entry request carrying the target material at the C-terminal (APP or applet) of the electronic device. The target material may include claim material, core material, or the like.
Step S202, if the material input request is received, analyzing the target material from the material input request, and obtaining a material class of the target material.
In this embodiment, the material class of the target material may include a claim material, a core material, or other materials.
Step S203, judging whether the material category belongs to a standard material category.
In this embodiment, the determination of the standard material class may be determined according to the actual service requirement, and may include, for example, a claim material and a core material.
Step S204, if the standard material category is included, the data type of the target material is obtained.
In this embodiment, the data types may include medical invoices, outpatient records, physical examination reports, bill of fees, inspection reports, and the like. The above specific implementation process of obtaining the data type of the target material will be described in further detail in the following specific embodiments, which will not be described herein.
Step S205, a target extraction model corresponding to the data type is called, and information extraction is carried out on the target material based on the target extraction model to obtain corresponding text information.
In this embodiment, for materials belonging to different data types, different extraction models are trained in advance according to the data types. Specifically, for identification of medical invoice: all texts on the invoice are recognized by the universal OCR model, and then the invoice templates of different areas and different hospitals trained in the earlier stage are combined to enter respective extraction models, so that the accuracy of invoice recognition can be greatly improved. For the recognition of the bill of charge, because the formats of the table format are more, an OCR training table analysis model is needed, and compared with a general OCR model, the table analysis model has more definite division of the table lines, and can accurately distinguish the characters from the table lines.
And S206, performing credibility verification on the text information.
In this embodiment, the above specific implementation process of verifying the credibility of the text information is described in further detail in the following specific embodiments, which will not be described herein.
Step S207, if the text information passes the credibility check, the input processing of the target material is executed.
In this embodiment, if the text information passes the reliability check, which indicates that the reliability of the target material is high, the target material may be regarded as a material satisfying the entry condition, and the entry process for the target material may be performed. If the text information passes the credibility check, the input check of the whole input (bill input and detail input) of the target material is required to be added, and the input process of the target material is executed only after the input check passes.
When a material input request submitted by a user is received, the material input request is firstly analyzed from the material input request to obtain the target material, and the material class of the target material is obtained; then judging whether the material category belongs to a standard material category or not; if the data type belongs to the standard material category, acquiring the data type of the target material; then, a target extraction model corresponding to the data type is called, and information extraction is carried out on the target material based on the target extraction model to obtain corresponding text information; subsequently, carrying out credibility verification on the text information; and if the text information passes the credibility check, executing the input processing of the target material. According to the method, the verification mode of material verification is replaced by the material verification mode based on the combination of the target extraction model and the credibility verification, so that the workload of the input personnel is greatly reduced, the processing efficiency of the material verification is effectively improved, the accuracy of the material verification is ensured, and the efficiency of material input is improved.
In some alternative implementations, step S206 includes the steps of:
and acquiring a first field corresponding to a preset field type from the text information.
In this embodiment, the field type is a field type predetermined according to an actual service requirement, and may specifically include a disease name, an invoice name, a hospital name, an invoice number, an amount, and the like.
Carrying out standardization processing on the first field to obtain a second field; wherein the number of second fields includes a plurality.
In this embodiment, the normalization processing for the fields may include alignment processing for the fields. For example, for a disease name, the disease name is aligned to a standard name according to a national standard disease name (national standard 2.0). For hospital names, the names are required to be aligned according to the hospital libraries of various companies, and the alignment purpose is to perform subsequent better data analysis and processing, so that different names of the same disease are aligned with each other.
And acquiring business rules respectively corresponding to the second fields.
In this embodiment, the service rule is a field verification standard specification pre-constructed according to an actual service requirement. For example, business rules may include: the number of digits of the invoice number is the same as the standard digits, the format of the invoice number is the same as the standard format, the total sum of the bill and the detail sum is the same, and the like.
And judging whether each second field accords with the corresponding business rule.
If yes, judging that the text information passes the credibility check, otherwise, judging that the text information does not pass the credibility check.
In this embodiment, if each of the second fields does not meet the corresponding service rule, an entry check for the whole entry (bill entry and detail entry) of the target material needs to be added, and only after the entry check passes, the entry process for the target material is executed.
The method comprises the steps of obtaining a first field corresponding to a preset field type from the text information; then, carrying out standardization processing on the first field to obtain a second field; then, obtaining business rules respectively corresponding to the second fields; subsequently judging whether each second field accords with the corresponding business rule; if yes, judging that the text information passes the credibility check, otherwise, judging that the text information does not pass the credibility check. The method and the device can realize the reliability check of the text information rapidly and accurately based on the invocation of the business rule, ensure the accuracy of the generated reliability check result, and are favorable for accurately executing the processing of the target material based on the obtained reliability check result.
In some alternative implementations of the present embodiment, step S204 includes the steps of:
and acquiring preset position information.
In this embodiment, the location information may specifically refer to location information corresponding to a summary or a title in a material.
And extracting keywords from the target material based on the position information to obtain corresponding target keywords.
In this embodiment, the target keywords may include keywords such as medical invoice, outpatient record, physical examination report, bill of charge, and inspection report.
And determining the data type of the target material based on the target keyword.
In this embodiment, the target keyword may refer to a data type of a corresponding target material, and the obtained target keyword may be directly used as the data type of the target material.
The method comprises the steps of obtaining preset position information; then extracting keywords from the target material based on the position information to obtain corresponding target keywords; and further determining the data type of the target material based on the target keyword. The method and the device can quickly and accurately determine the data type of the target material based on the use of the position information, and are favorable for accurately calling a corresponding target extraction model based on the obtained data type of the target material to extract information from the target material to obtain corresponding text information.
In some alternative implementations, before the step S204, the electronic device may further perform the following steps:
and calling a preset quality detection model.
In this embodiment, for the process of constructing the quality detection model, this application will be described in further detail in the following specific embodiments, which will not be described herein.
And detecting the quality of the target material based on the quality detection model to obtain a corresponding quality detection result.
In this embodiment, the quality detection model may be a model employing OCR image localization and image quality detection techniques. The quality of the target material can be classified by the quality detection model, and a classification result corresponding to the target material, namely the quality detection result, is output. The classification results may include blur, angular skew, quality pass verification (normal), among others.
And if the quality detection result is that the quality of the target material passes the verification, executing the step of acquiring the data type of the target material.
In this embodiment, if the quality detection result indicates that the target material has a blur or an angle deviation, it is determined that the quality of the target material is not verified, and the user is prompted to reload the material with the excessive mass transfer amount.
The method comprises the steps of calling a preset quality detection model; then detecting the quality of the target material based on the quality detection model to obtain a corresponding quality detection result; and if the quality detection result is that the quality of the target material passes the verification, executing the step of acquiring the data type of the target material. The quality detection method and the quality detection system can rapidly and accurately detect the quality of the target material and generate the corresponding quality detection result based on the quality detection model, and only when the quality of the target material passes verification, the step of acquiring the data type of the target material is executed subsequently to continue the data input process, so that the waste map rate of the material to be input is greatly reduced, the robustness of the system is improved, and the normalization and the intelligence of the material input processing are improved.
In some alternative implementations, before the step of calling the preset quality detection model, the electronic device may further perform the following steps:
and acquiring preset material training data.
In this embodiment, the material training data may be a pre-collected claim or a core material, and a quality tag corresponding to the claim or the core material. The quality label comprises blurring, angle deflection and quality passing verification.
And training a preset deep learning model based on the material training data to obtain a trained deep learning model.
In this embodiment, the selection of the deep learning model is not particularly limited, and may be set according to actual use requirements, and it is preferable to consider a deep learning model built by using a deep learning frame based on mobilet-v 2.
Material test data is constructed based on the material training data.
In this embodiment, the data of the preset proportion may be randomly selected from the material training data as the material test data. The value of the preset ratio is not particularly limited, and may be set according to actual use requirements, for example, may be set to 0.2.
And verifying the trained deep learning model based on the deep learning model, and judging whether the obtained test passing rate is larger than a preset passing rate threshold value.
In this embodiment, the value of the test passing rate is not particularly limited, and may be set according to actual use requirements.
If yes, judging that the trained deep learning model passes verification, and taking the trained deep learning model as the quality detection model.
In this embodiment, if the obtained test passing rate is smaller than a preset passing rate threshold, the material training data is adjusted to retrain the deep learning model until a quality detection model meeting the requirements is obtained.
The method comprises the steps of obtaining preset material training data; training a preset deep learning model based on the material training data to obtain a trained deep learning model; then building material test data based on the material training data; subsequently verifying the trained deep learning model based on the deep learning model, and judging whether the obtained test passing rate is larger than a preset passing rate threshold value or not; if yes, judging that the trained deep learning model passes verification, taking the trained deep learning model as the quality detection model to complete construction of the quality detection model, enabling quality of target materials to be rapidly and accurately detected and corresponding quality detection results to be generated by using the quality detection model, and only when the quality of the target materials passes verification, executing a step of acquiring data types of the target materials to continue a data input process, so that waste pattern rate of materials to be input is greatly reduced, robustness of a system is improved, and normalization and intelligence of material input processing are improved.
In some optional implementations of this embodiment, before step S205, the electronic device may further perform the following steps:
and determining a compression mode corresponding to the target extraction model.
In this embodiment, the requirement of the compression method is not particularly limited, and may be set according to the actual requirement of use, for example, a quantization method, a network pruning method, or a combination method of quantization and network pruning may be included.
And carrying out compression processing on the model size of the target extraction model based on the compression mode to obtain a compressed target extraction model.
In the embodiment, the model size of the target extraction model is compressed, so that the model size is about 2m under the condition of ensuring the model processing precision of the target extraction model, and the deployment requirement of the target extraction model on the terminal equipment is ensured.
And storing the compressed target extraction model.
In this embodiment, the storage mode of the target extraction model is not specifically limited, for example, a database storage or a blockchain storage mode may be adopted to ensure the privacy and security of the target extraction model.
The compression mode corresponding to the target extraction model is determined; then, compressing the model size of the target extraction model based on the compression mode to obtain a compressed target extraction model; and storing the compressed target extraction model. According to the method and the device, the model size of the target extraction model is compressed based on the compression mode corresponding to the target extraction model, so that the model size of the target extraction model is effectively reduced under the condition that the model processing precision of the target extraction model is ensured, the deployment requirement of the target extraction model on terminal equipment is ensured, and the deployment intelligence of the target extraction model is improved.
In some optional implementations of this embodiment, after step S203, the electronic device may further perform the following steps:
and if the material category does not belong to the standard material category, acquiring preset material uploading reminding information.
In this embodiment, the material upload reminding information may be an information template that is pre-written and generated according to actual service requirements and reminds the relevant user to upload correct material data again.
And obtaining the standard material category.
And generating target material reminding information based on the material uploading reminding information and the standard material category.
In this embodiment, the target material reminding information may be generated by integrating the material uploading reminding information with the standard material category.
And displaying the target material reminding information.
In this embodiment, the display mode of the target material reminding information is not particularly limited, and for example, the target material reminding information can be displayed on the current interface in a mode of an information frame.
When the material category is detected not to belong to the standard material category, firstly, acquiring preset material uploading reminding information; then obtaining the standard material category; generating target material reminding information based on the material uploading reminding information and the standard material category; and the target material reminding information is displayed subsequently, so that the user is reminded intelligently to upload correct material data again based on the target material reminding information, and the use experience of the user is improved.
It is emphasized that the target material may also be stored in a blockchain node in order to further ensure privacy and security of the target material.
The blockchain referred to in the application 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.
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.
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 of FIG. 2 described above, the present application provides an embodiment of an artificial intelligence based data entry apparatus, corresponding to the method embodiment of FIG. 2, which is particularly applicable in a variety of electronic devices.
As shown in fig. 3, the artificial intelligence based data entry device 300 according to the present embodiment includes: a first judging module 301, a first acquiring module 302, a second judging module 303, a second acquiring module 304, an extracting module 305, a checking module 306 and a processing module 307.
Wherein:
a first judging module 301, configured to judge whether a material input request submitted by a user is received; wherein the material entry request carries a target material;
a first obtaining module 302, configured to, if the material input request is received, analyze the target material from the material input request, and obtain a material class of the target material;
a second judging module 303, configured to judge whether the material class belongs to a standard material class;
a second obtaining module 304, configured to obtain a data type of the target material if the data type belongs to the standard material class;
The extraction module 305 is configured to invoke a target extraction model corresponding to the data type, and extract information from the target material based on the target extraction model to obtain corresponding text information;
a verification module 306, configured to perform reliability verification on the text information;
and the processing module 307 is used for executing the input processing of the target material if the text information passes the credibility check.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data entry method based on artificial intelligence in the foregoing embodiment one by one, which is not described herein again.
In some alternative implementations of the present embodiment, the verification module 306 includes:
the first acquisition sub-module is used for acquiring a first field corresponding to a preset field type from the text information;
the processing sub-module is used for carrying out standardized processing on the first field to obtain a second field; wherein the number of second fields includes a plurality;
the second obtaining submodule is used for obtaining the business rules respectively corresponding to the second fields;
the judging submodule is used for judging whether each second field accords with the corresponding business rule;
And the judging submodule is used for judging that the text information passes the credibility check if yes, and judging that the text information does not pass the credibility check if not.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data entry method based on artificial intelligence in the foregoing embodiment one by one, which is not described herein again.
In some optional implementations of this embodiment, the second obtaining module 304 includes:
the third acquisition sub-module is used for acquiring preset position information;
the extraction sub-module is used for extracting keywords from the target material based on the position information to obtain corresponding target keywords;
and the determining submodule is used for determining the data type of the target material based on the target keyword.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data input method based on artificial intelligence in the foregoing embodiment one by one, which is not described herein again.
In some optional implementations of the present embodiment, the artificial intelligence based data entry device further comprises:
the calling module is used for calling a preset quality detection model;
the detection module is used for detecting the quality of the target material based on the quality detection model to obtain a corresponding quality detection result;
And the execution module is used for executing the step of acquiring the data type of the target material if the quality detection result is that the quality of the target material passes verification.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data entry method based on artificial intelligence in the foregoing embodiment one by one, which is not described herein again.
In some optional implementations of the present embodiment, the artificial intelligence based data entry device further comprises:
the third acquisition module is used for acquiring preset material training data;
the training module is used for training a preset deep learning model based on the material training data to obtain a trained deep learning model;
a building module for building material test data based on the material training data;
the third judging module is used for verifying the trained deep learning model based on the deep learning model and judging whether the obtained test passing rate is larger than a preset passing rate threshold value or not;
and the first determining module is used for judging that the trained deep learning model passes verification if yes, and taking the trained deep learning model as the quality detection model.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data entry method based on artificial intelligence in the foregoing embodiment one by one, which is not described herein again.
In some optional implementations of the present embodiment, the artificial intelligence based data entry device further comprises:
the second determining module is used for determining a compression mode corresponding to the target extraction model;
the compression module is used for carrying out compression processing on the model size of the target extraction model based on the compression mode to obtain a compressed target extraction model;
and the storage module is used for storing the compressed target extraction model.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data entry method based on artificial intelligence in the foregoing embodiment one by one, which is not described herein again.
In some optional implementations of the present embodiment, the artificial intelligence based data entry device further comprises:
a fourth obtaining module, configured to obtain preset material upload reminding information if the material class does not belong to the standard material class;
a fifth obtaining module, configured to obtain the standard material class;
The generation module is used for generating target material reminding information based on the material uploading reminding information and the standard material category;
and the display module is used for displaying the target material reminding information.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data entry method based on artificial intelligence in the foregoing embodiment one by one, which is not described herein again.
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 for an artificial intelligence based data entry method. 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 computer readable instructions for executing the artificial intelligence based data entry method.
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.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
when receiving a material input request submitted by a user, the embodiment of the application firstly analyzes the target material from the material input request and acquires the material category of the target material; then judging whether the material category belongs to a standard material category or not; if the data type belongs to the standard material category, acquiring the data type of the target material; then, a target extraction model corresponding to the data type is called, and information extraction is carried out on the target material based on the target extraction model to obtain corresponding text information; subsequently, carrying out credibility verification on the text information; and if the text information passes the credibility check, executing the input processing of the target material. According to the embodiment of the application, the verification mode of carrying out material verification based on the combination of the target extraction model and the credibility verification replaces the existing manual verification mode of carrying out material verification, so that the workload of the input personnel is greatly reduced, the processing efficiency of the material verification is effectively improved, the accuracy of the material verification is ensured, and the efficiency of material input is 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 an artificial intelligence based data entry method as described above.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
when receiving a material input request submitted by a user, the embodiment of the application firstly analyzes the target material from the material input request and acquires the material category of the target material; then judging whether the material category belongs to a standard material category or not; if the data type belongs to the standard material category, acquiring the data type of the target material; then, a target extraction model corresponding to the data type is called, and information extraction is carried out on the target material based on the target extraction model to obtain corresponding text information; subsequently, carrying out credibility verification on the text information; and if the text information passes the credibility check, executing the input processing of the target material. According to the embodiment of the application, the verification mode of carrying out material verification based on the combination of the target extraction model and the credibility verification replaces the existing manual verification mode of carrying out material verification, so that the workload of the input personnel is greatly reduced, the processing efficiency of the material verification is effectively improved, the accuracy of the material verification is ensured, and the efficiency of material input is improved.
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 (such as ROM/RAM, magnetic disk, optical disk), comprising several 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 described in the embodiments of the present application.
It is apparent that the embodiments described above are only some embodiments of the present application, but not all embodiments, the preferred embodiments of the present application are given in the drawings, but not limiting the patent scope of the present application. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a more thorough understanding of the present disclosure. Although the present 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, or equivalents may be substituted for elements thereof. All equivalent structures made by the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the protection scope of the application.
Claims (10)
1. A data entry method based on artificial intelligence, comprising the steps of:
judging whether a material input request submitted by a user is received or not; wherein the material entry request carries a target material;
if the material input request is received, analyzing the target material from the material input request, and acquiring the material class of the target material;
judging whether the material class belongs to a standard material class or not;
if the data type belongs to the standard material category, acquiring the data type of the target material;
invoking a target extraction model corresponding to the data type, and extracting information from the target material based on the target extraction model to obtain corresponding text information;
carrying out credibility verification on the text information;
and if the text information passes the credibility check, executing the input processing of the target material.
2. The artificial intelligence based data entry method of claim 1, wherein the step of verifying the credibility of the text information specifically comprises:
acquiring a first field corresponding to a preset field type from the text information;
Carrying out standardization processing on the first field to obtain a second field; wherein the number of second fields includes a plurality;
acquiring business rules respectively corresponding to the second fields;
judging whether each second field accords with a corresponding service rule;
if yes, judging that the text information passes the credibility check, otherwise, judging that the text information does not pass the credibility check.
3. The artificial intelligence based data entry method of claim 1, wherein the step of obtaining the data type of the target material comprises:
acquiring preset position information;
extracting keywords from the target material based on the position information to obtain corresponding target keywords;
and determining the data type of the target material based on the target keyword.
4. The artificial intelligence based data entry method of claim 1, further comprising, prior to the step of obtaining the data type of the target material:
invoking a preset quality detection model;
detecting the quality of the target material based on the quality detection model to obtain a corresponding quality detection result;
And if the quality detection result is that the quality of the target material passes the verification, executing the step of acquiring the data type of the target material.
5. The artificial intelligence based data entry method of claim 4, further comprising, prior to the step of invoking the preset quality detection model:
acquiring preset material training data;
training a preset deep learning model based on the material training data to obtain a trained deep learning model;
constructing material test data based on the material training data;
verifying the trained deep learning model based on the deep learning model, and judging whether the obtained test passing rate is greater than a preset passing rate threshold value or not;
if yes, judging that the trained deep learning model passes verification, and taking the trained deep learning model as the quality detection model.
6. The artificial intelligence based data entry method of claim 1, further comprising, prior to the step of invoking a target extraction model corresponding to the data type, extracting information from the target material based on the target extraction model to obtain corresponding text information:
Determining a compression mode corresponding to the target extraction model;
compressing the model size of the target extraction model based on the compression mode to obtain a compressed target extraction model;
and storing the compressed target extraction model.
7. The artificial intelligence based data entry method of claim 1, further comprising, after the step of determining whether the class of materials belongs to a standard class of materials:
if the material category does not belong to the standard material category, acquiring preset material uploading reminding information;
acquiring the standard material category;
generating target material reminding information based on the material uploading reminding information and the standard material category;
and displaying the target material reminding information.
8. An artificial intelligence based data entry device comprising:
the first judging module is used for judging whether a material input request submitted by a user is received or not; wherein the material entry request carries a target material;
the first acquisition module is used for analyzing the target material from the material input request and acquiring the material category of the target material if the material input request is received;
The second judging module is used for judging whether the material category belongs to a standard material category or not;
the second acquisition module is used for acquiring the data type of the target material if the data type belongs to the standard material category;
the extraction module is used for calling a target extraction model corresponding to the data type, and extracting information from the target material based on the target extraction model to obtain corresponding text information;
the verification module is used for verifying the credibility of the text information;
and the processing module is used for executing the input processing of the target material if the text information passes the credibility check.
9. A computer device comprising a memory having stored therein computer readable instructions which when executed implement the steps of the artificial intelligence based data entry method of any 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 data entry method of any of claims 1 to 7.
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| CN116861042B (en) * | 2023-09-05 | 2023-12-05 | 国家超级计算天津中心 | Information verification method, device, equipment and medium based on material database |
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