CN116756612A

CN116756612A - Data reporting method, device, equipment and storage medium based on artificial intelligence

Info

Publication number: CN116756612A
Application number: CN202310684378.7A
Authority: CN
Inventors: 刘雪花
Original assignee: Ping An Property and Casualty Insurance Company of China Ltd
Current assignee: Ping An Property and Casualty Insurance Company of China Ltd
Priority date: 2023-06-09
Filing date: 2023-06-09
Publication date: 2023-09-15

Abstract

The embodiment of the application belongs to the field of artificial intelligence and the field of financial science and technology, and relates to a data reporting method based on artificial intelligence, which comprises the following steps: acquiring the current time; judging whether the current time is in the reporting processing time period or not; if yes, acquiring the information of the reporting requirement corresponding to the target reporting mechanism; screening first project data corresponding to the reporting requirement information from a preset service diagram database based on the reporting requirement information; screening the first item data based on a preset abnormal screening model to obtain corresponding second item data; and sending the second project data report to a target reporting mechanism. The application also provides a data reporting device, computer equipment and a storage medium based on the artificial intelligence. In addition, the present application also relates to a blockchain technique, wherein the second project data can be stored in the blockchain. The application can be applied to the data reporting scene in the financial field, can improve the acquisition efficiency and the acquisition intelligence of the data to be reported, and improves the reporting efficiency of the data to be reported.

Description

Data reporting method, device, equipment and storage medium based on artificial intelligence

Technical Field

The application relates to the technical field of artificial intelligence and the technical field of finance, in particular to a data reporting method, a data reporting device, computer equipment and a storage medium based on artificial intelligence.

Background

In order to promote the application of the inspection analysis system in on-site inspection and off-site supervision of insurance companies, the insurance companies are urged to actively strengthen data management, the application data continuously improves the management level and the risk management capability of the insurance companies, the office of China's silver insurance Congress is informed about the standardized specifications of the printed insurance industry supervision data (property insurance company edition) and the standardized specifications of the insurance industry supervision data (reinsurance company edition), the insurance companies are required to report some project data of the previous month to the supervision departments at regular time, such as full quantity, increment and variation data, and each branch office is required to report some specific project data of the previous month to the home supervision authorities.

The existing data reporting mode of insurance companies usually adopts a manual processing mode, manual collection and arrangement and manual verification are needed, data to be reported is circulated in each department through a mail mode, so that a great deal of manpower and time are required to be spent for arrangement, collection and integration, data sources are disordered, the flow of data reporting is complex, the whole data processing flow is required to pass through manual verification and verification links layer by layer, the processing timeliness is low, and the phenomenon of untimely data reporting is easy to occur.

Disclosure of Invention

The embodiment of the application aims to provide a data reporting method, a device, computer equipment and a storage medium based on artificial intelligence, which are used for solving the technical problems that the existing data reporting mode adopting manual processing needs to spend a great deal of manpower and time for arrangement, collection and integration, data sources are disordered, the flow of data reporting is complex, and the timeliness of processing is low.

In order to solve the technical problems, the embodiment of the application provides a data reporting method based on artificial intelligence, which adopts the following technical scheme:

acquiring the current time;

judging whether the current time is in a preset reporting processing time period or not;

if yes, acquiring the information of the reporting requirement corresponding to a preset target reporting mechanism;

screening first project data corresponding to the reporting requirement information from a preset service diagram database based on the reporting requirement information; wherein the number of first item data includes a plurality;

screening the first item data based on a preset abnormal screening model to obtain corresponding second item data;

and sending the second item datagram to the target reporting mechanism.

Further, the step of screening the first item data corresponding to the reporting requirement information from a preset service graph database based on the reporting requirement information specifically includes:

extracting a business project object field and reporting period information from the reporting requirement information;

information retrieval is carried out on the service graph database based on the service item object field, and a target field matched with the service item object field is determined from the service graph database;

screening target graph data corresponding to the target field from the service graph database based on the reporting period information;

and taking the target graph data as the first project data.

Further, the step of screening the target graph data corresponding to the target field from the service graph database based on the reporting period information specifically includes:

based on the reporting period information, acquiring a data entity with an association relation with the target field from the service graph database; wherein the number of data entities comprises a plurality;

integrating all the data entities to obtain a corresponding target data entity set;

And taking the target data entity set as the target graph data.

Further, the step of screening the first item data based on a preset anomaly screening model to obtain corresponding second item data specifically includes:

inputting the first item data into the abnormality screening model, and performing abnormality analysis on the first item data through the abnormality screening model to obtain an abnormality probability value corresponding to each first item data;

screening abnormal item data from all the first item data based on the abnormal probability value;

removing the abnormal item data from the first item data to obtain normal item data;

and taking the normal project data as the second project data.

Further, before the step of screening the first item data based on the preset anomaly screening model to obtain corresponding second item data, the method further includes:

acquiring pre-constructed project sample data;

dividing the project sample data into training data and verification data according to a preset proportion;

training a preset machine learning model based on the training data to obtain a trained initial anomaly screening model;

Performing verification processing on the initial abnormal screening model based on the verification data to obtain corresponding analysis accuracy;

performing iterative optimization on the initial abnormal screening model based on the analysis accuracy and the training data to obtain a trained initial abnormal screening model;

and taking the trained initial abnormality screening model as the abnormality screening model.

Further, the step of sending the second item datagram to the target reporting mechanism specifically includes:

acquiring communication information of the target reporting mechanism;

determining a target delivery time corresponding to the target delivery mechanism;

and based on the communication information and the target reporting time, reporting the second item data to the target reporting mechanism.

constructing a data report file corresponding to the second item data;

compressing the data report file to obtain a target data report file;

and reporting the target data reporting file to the target reporting mechanism.

In order to solve the technical problems, the embodiment of the application also provides a data reporting device based on artificial intelligence, which adopts the following technical scheme:

The first acquisition module is used for acquiring the current time;

the judging module is used for judging whether the current time is in a preset reporting processing time period or not;

the second acquisition module is used for acquiring the information of the reporting requirement corresponding to the preset target reporting mechanism if yes;

the first screening module is used for screening first project data corresponding to the reporting requirement information from a preset service diagram database based on the reporting requirement information; wherein the number of first item data includes a plurality;

the second screening module is used for screening the first project data based on a preset abnormal screening model to obtain corresponding second project data;

and the sending module is used for sending the second item datagram to the target reporting mechanism.

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:

acquiring the current time;

and sending the second item datagram to the target reporting mechanism.

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:

acquiring the current time;

and sending the second item datagram to the target reporting mechanism.

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

the embodiment of the application firstly obtains the current time and judges whether the current time is in a preset reporting processing time period or not; if yes, acquiring the information of the reporting requirement corresponding to a preset target reporting mechanism; then, based on the report demand information, screening out first item data corresponding to the report demand information from a preset service diagram database; screening the first item data based on a preset abnormal screening model to obtain corresponding second item data; and finally, the second item data is sent to the target reporting mechanism. The embodiment of the application can automatically and rapidly acquire the project data to be reported corresponding to the reporting requirement information based on the service graph database and the use of the abnormality screening model, and further realize the automatic reporting processing of the project data to be reported based on the preset reporting processing time period, thereby effectively improving the acquisition efficiency and the acquisition intelligence of the data to be reported and improving the reporting efficiency of the data to be reported.

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 datagram method according to the present application;

FIG. 3 is a schematic diagram of one embodiment of an artificial intelligence based datagram apparatus according to 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 data reporting method based on the artificial intelligence provided by the embodiment of the application is generally executed by a server/terminal device, and correspondingly, the data reporting device based on the artificial intelligence is generally arranged 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 datagram method according to 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 data reporting method based on the artificial intelligence provided by the embodiment of the application can be applied to any scene needing data reporting, and can be applied to products of the scenes, such as data reporting in the field of financial insurance. The data reporting method based on artificial intelligence comprises the following steps:

Step S201, the current time is acquired.

In this embodiment, the electronic device (e.g., the server/terminal device shown in fig. 1) on which the data reporting method based on artificial intelligence operates may acquire the current time 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.

Step S202, judging whether the current time is within a preset reporting processing time period.

In this embodiment, the time value of the reporting processing time period is not particularly limited, and may be set according to the actual service usage requirement, and it is preferable that the reporting processing time period is within the first two days of the expiration date of the datagram.

Step S203, if yes, acquiring the reporting requirement information corresponding to the preset target reporting mechanism.

In this embodiment, the target reporting mechanism is a mechanism for receiving the second item data to be reported, and may be, for example, a local authority. The report request information may be information corresponding to an actual data report condition request of the target report mechanism. Wherein, the report demand information at least comprises a business project object field and report period information.

Step S204, based on the report demand information, first item data corresponding to the report demand information is screened out from a preset service diagram database; wherein the number of the first item data includes a plurality.

In this embodiment, the business map database may be a Neo4j map database. The specific implementation process of screening the first item data corresponding to the reporting requirement information from the preset service graph database based on the reporting requirement information is described in further detail in the following specific embodiments, which will not be described herein. For example, in a business scenario of datagram in the field of financial insurance, the item data may include business data, transaction data, payment data, and the like.

Step S205, screening the first item data based on a preset anomaly screening model to obtain corresponding second item data.

In this embodiment, the specific implementation process of screening the first item data based on the preset abnormal screening model to obtain the corresponding second item data will be described in further detail in the following specific embodiments, which will not be described herein.

Step S206, sending the second item datagram to the target reporting mechanism.

In this embodiment, the foregoing specific implementation process of sending the second item datagram to the target reporting mechanism will be described in further detail in the following specific embodiments, which will not be described herein.

Firstly, acquiring current time, and judging whether the current time is in a preset reporting processing time period or not; if yes, acquiring the information of the reporting requirement corresponding to a preset target reporting mechanism; then, based on the report demand information, screening out first item data corresponding to the report demand information from a preset service diagram database; screening the first item data based on a preset abnormal screening model to obtain corresponding second item data; and finally, the second item data is sent to the target reporting mechanism. The method and the device can automatically and rapidly acquire the project data to be reported corresponding to the reporting requirement information based on the service graph database and the use of the anomaly screening model, and further realize the automatic reporting processing of the project data to be reported based on the preset reporting processing time period, thereby effectively improving the acquisition efficiency and the acquisition intelligence of the data to be reported and improving the reporting efficiency of the data to be reported.

In some alternative implementations, step S204 includes the steps of:

and extracting a business project object field and reporting period information from the reporting requirement information.

In this embodiment, the reporting requirement information at least includes a service item object field and reporting period information. The service item object field and the reporting period information can be extracted from the reporting requirement information by analyzing the reporting requirement information. The business item object field is a field of item data to be reported. The reporting period information refers to time information of screening an initial data entity having an association relationship with the target field in the service graph database in a time dimension.

And carrying out information retrieval on the service graph database based on the service item object field, and determining a target field matched with the service item object field from the service graph database.

In this embodiment, the target field refers to a field in the service map database that is the same as the service item object field.

And screening target graph data corresponding to the target field from the service graph database based on the reporting period information.

In this embodiment, the specific implementation process of screening the target graph data corresponding to the target field from the service graph database based on the reporting period information will be described in further detail in the following specific embodiments, which will not be described herein.

And taking the target graph data as the first project data.

Extracting a business project object field and reporting period information from the reporting requirement information; then, based on the business project object field, carrying out information retrieval on the business map database, and determining a target field matched with the business project object field from the business map database; and screening target graph data corresponding to the target field from the service graph database based on the reporting period information, and taking the target graph data as the first project data. The application can realize the rapid and intelligent inquiry of the first project data from the service diagram database based on the service project object field and the reporting period information extracted from the reporting requirement information, and is favorable for accurately and finally outputting the second project data finally used for reporting based on the obtained target diagram data.

In some optional implementations of this embodiment, the screening, based on the reporting period information, the target graph data corresponding to the target field from the service graph database includes the following steps:

based on the reporting period information, acquiring a data entity with an association relation with the target field from the service graph database; wherein the number of data entities comprises a plurality.

In this embodiment, the reporting period information refers to time information of filtering in a time dimension for an initial data entity having an association relationship with the target field included in the service graph database. Specifically, an initial data entity with an association relation with the target field is firstly obtained from the service graph database, and then the data entity matched with the reporting period information is screened out from all the initial data entities based on the reporting period information. The setting of the reporting period information can be set according to actual use requirements.

And integrating all the data entities to obtain a corresponding target data entity set.

In this embodiment, the target data entity set includes all the data entities.

And taking the target data entity set as the target graph data.

The application obtains the data entity with association relation with the target field from the service graph database based on the reporting period information; and integrating all the data entities to obtain a corresponding target data entity set, and taking the target data entity set as the target graph data. The application can realize the rapid and intelligent inquiry of the required target graph data from the service graph database based on the service item object field and the reporting period information extracted from the reporting requirement information, and is favorable for accurately and finally outputting the final project data for reporting based on the obtained target graph data.

In some alternative implementations, step S205 includes the steps of:

and inputting the first item data into the abnormality screening model, and performing abnormality analysis on the first item data through the abnormality screening model to obtain an abnormality probability value corresponding to each first item data.

In this embodiment, for the training creation process of the anomaly screening model, the present application will be described in further detail in the following specific embodiments, which will not be described here.

And screening abnormal item data from all the first item data based on the abnormal probability value.

In this embodiment, a preset probability threshold may be obtained, and if the abnormal probability value of the item data output by the abnormal screening model is greater than the probability threshold, it is determined that the item data belongs to abnormal item data; and if the abnormal probability value of the item data output by the abnormal screening model is smaller than the probability threshold value, judging that the item data belongs to normal item data. The value of the probability threshold is not specifically limited, and may be set according to actual service usage requirements.

And eliminating the abnormal item data from the first item data to obtain normal item data.

And taking the normal project data as the second project data.

According to the application, through inputting each first item data into the abnormality screening model, carrying out abnormality analysis on each first item data through the abnormality screening model to obtain an abnormality probability value corresponding to each first item data; then, based on the abnormal probability value, abnormal item data are screened out from all the first item data; and eliminating the abnormal item data from the first item data to obtain normal item data, and taking the normal item data as the second item data. According to the application, the first item data is subjected to the abnormality analysis processing by using the pre-trained abnormality screening model, so that the abnormality analysis results corresponding to all the first item data can be obtained rapidly and accurately, the processing efficiency and the result accuracy of abnormality detection on the first item data are improved, and the subsequent determination of the second item data which is finally used for reporting can be accurately determined from the first item data based on the obtained abnormality analysis results.

In some alternative implementations, before step S205, the electronic device may further perform the following steps:

and obtaining pre-constructed project sample data.

In this embodiment, the item sample data may be constructed from item data in a preset period of time extracted from a service graph database. Specifically, abnormal features are extracted from project data in a preset time period, and data cleaning, missing value supplementing and data dimension reduction processing are performed on data corresponding to the abnormal features to obtain project sample data. The final item sample data includes an anomaly tag (whether the anomaly data belong to the anomaly data 1 and not the anomaly data 0) corresponding to the item data. In addition, the value of the preset time period is not particularly limited, and may be, for example, within the first 1 year from the current time. The item sample data is provided with an abnormal result tag. In addition, a nearest neighbor resampling method can be adopted, random disturbance is added on the nearest neighbor sample basis of each item of sample data, new item of sample data is generated, and the total number of sample data belonging to the abnormal data class and the total number of sample data not belonging to the abnormal data class are balanced in number, so that overfitting is avoided.

And dividing the project sample data into training data and verification data according to a preset proportion.

In this embodiment, the value of the preset ratio is not specifically limited, and may be set according to the actual use requirement, for example, may be set to 7:3.

and training a preset machine learning model based on the training data to obtain a trained initial anomaly screening model.

In this embodiment, the machine learning model may specifically be a logistic regression model. Analyzing the training data by adopting a logistic regression algorithm, training the constructed machine learning model, and obtaining a trained initial anomaly screening model. The obtained trained initial anomaly screening model is a rule of a result of which characteristic data learned by machine learning belong to anomaly data.

And carrying out verification processing on the initial abnormal screening model based on the verification data to obtain corresponding analysis accuracy.

In this embodiment, by substituting the verification data into the initial anomaly screening model, an anomaly probability value corresponding to each sample data in the verification data can be obtained, the anomaly probability value being in the range of 0 to 1, and the greater the score, the higher the anomaly degree.

And carrying out iterative optimization on the initial abnormal screening model based on the analysis accuracy and the training data to obtain a trained initial abnormal screening model.

In this embodiment, the analysis accuracy may be obtained by comparing the abnormal analysis result of the verification data with the actual result, and further iterating and optimizing the initial abnormal screening model, simplifying the abnormal data characteristics, and adjusting the parameter size until the analysis accuracy is adjusted to be optimal. Wherein, model tuning requires a large number of experiments to find the most suitable parameter values and characteristics.

The method comprises the steps of obtaining pre-constructed project sample data; then dividing the project sample data into training data and verification data according to a preset proportion; training a preset machine learning model based on the training data to obtain a trained initial anomaly screening model; subsequently, based on the verification data, carrying out verification processing on the initial abnormal screening model to obtain corresponding analysis accuracy; and finally, carrying out iterative optimization on the initial abnormal screening model based on the analysis accuracy and the training data to obtain a trained initial abnormal screening model, and taking the trained initial abnormal screening model as the abnormal screening model, thereby completing the construction of the abnormal screening model for carrying out abnormal analysis on the project data. The method and the device can be used for carrying out the abnormality analysis processing on the first item data by using the abnormality screening model, so that abnormality analysis results corresponding to all the first item data can be obtained rapidly and accurately, the processing efficiency and the result accuracy of abnormality detection on the first item data are improved, and the method and the device are favorable for determining second item data which are finally used for reporting from the first item data accurately based on the obtained abnormality analysis results.

Further, in some alternative implementation manners, in order to solve the huge communication cost input by each party in the project demand changing process, improve the quality of project demand realization, improve the transparency of the reporting flow, reduce the false report and missing report caused by the information asynchronism of each related party, ensure the stable and high-quality operation of the project, an automatic data management circulation solution based on a graph database is also provided.

1) Project relationship person table:

all objects (object attributes comprise tables, fields and reporting batches) are combed by the project, each object is provided with related people, and if the related people change in post, the change is allowed after the system is provided with a catcher before the change, so that the project responsible person is always in the latest state. When the object is changed, each relevant party can be informed in time, the relevant party can carry out the next step after confirmation, and all communication processes are automatically reserved for subsequent traceability.

2) Project demand card land:

and managing the requirements of all sources of the project, initiating a requirement change application to the object, wherein the requirement link is required to be transferred to all related personnel of the object for review, if a problem exists, communication confirmation is required to be carried out on a requirement communication place corresponding to the card, a development link can be entered after the review, and after the development link is completed, a requirement initiator carries out test acceptance. All information related to the demand card is integrated into the object demand information table automatically.

3) And (3) synchronizing the reporting flow information:

at the beginning of each batch of report, project manager needs to submit report type demand card to system, set related people in each link of report task operation, message generation, rule check, business acceptance, pdf file seal, uploading to supervisory server, supervisory warehouse entry, etc.,

4) Department module table:

the department can initiate a demand change flow, and the related person corresponding to the object can automatically receive notification of the evaluation message.

5) The mechanism module is as follows:

the mechanism can download related report data in the system, after checking, verifying and finding problems, initiating defect cards to the reporting batch corresponding to the table object, and the corresponding related person automatically receives the notification of the defect processing confirming message.

6) Each of the affiliated authorities:

the method mainly collects personalized requirements and personalized checking rules proposed by each affiliated area in each affiliated area supervision area, combines public requirements of each affiliated area, and expands the public requirements into an integral checking rule base of the project. The garden contains personalized checking rule list requirement, after the mechanism uploads the requirement, a requirement chain is automatically generated, personnel involved in a requirement object evaluate the requirement together, the requirement passing through the evaluation enters a development pool to be developed in a scheduled mode, after the development verification is online, the garden requirement can be closed, and the personnel is automatically notified to the proposer for verification.

7) And (3) demand generation: the system automatically associates the requirements of each source party with each other, automatically merges the requirements communication records by the system, and the project group requirements can form project requirement documents by slightly sorting the merged requirements without tracking and planning in the whole process of the requirement communication.

8) The project group development flow comprises the following steps:

and (3) carrying out scheduling and online on the demand of each party, confirming the online on the system, and enabling the institution verification personnel to receive the system notification, and finishing verification within a specified time, wherein the verified defect is required to be returned to development and online again.

Through the scheme, the reporting objects (data sheets and data items), reporting butt-joint persons (headquarters, institutions and supervisors), project environments (data storage ends and data reporting ends) and reporting processes (reporting nodes and reporting materials) related to the project are uniformly managed and controlled, the project demand changing (from the supervision side and from the inside of the danger producing system) process is fully automatically managed and controlled, the closed loop process of project operation is realized, the labor cost required by the project operation is effectively reduced, the standardization of the project process is greatly improved, and the overall quality of the project is improved.

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

and acquiring the communication information of the target reporting mechanism.

In this embodiment, the target reporting means is means for receiving the second item data to be reported.

And determining a target delivery time corresponding to the target delivery mechanism.

In this embodiment, the time value of the target reporting time is not specifically limited, and may be set according to the actual service usage requirement. For example, the target delivery time is a service processing idle time period of the target delivery mechanism, such as a time period other than a working time period and a rest time period.

In this embodiment, when the current time is up to the target delivery time, the second item datagram is delivered to the target delivery mechanism.

The application obtains the communication information of the target reporting mechanism; then determining a target delivery time corresponding to the target delivery mechanism; and further, based on the communication information and the target reporting time, reporting the second item data to the target reporting mechanism. According to the method and the device for reporting the project data, the target reporting time of reporting the project data is controlled, so that the problem that the reporting task is concentrated to the target reporting mechanism simultaneously to cause overlarge pressure of the server can be effectively solved, the intelligence of reporting the project data is improved, the pressure of the target reporting mechanism is favorably relieved, and the stability of the target reporting mechanism is enhanced.

and constructing a datagram file corresponding to the second item data.

In this embodiment, a datagram file including all the second item data passing the anomaly detection may be created, and if the data amount of the second item data is small, all the second item data may be stored in one datagram file to be exported; and if the data volume of the second item data is larger, storing all the second item data into a plurality of data reporting files respectively, and exporting a plurality of data reporting files respectively.

And compressing the data report file to obtain a target data report file.

In this embodiment, a file compression format suitable for uploading may be obtained in advance, and then the data report file is compressed based on the file compression format, so as to obtain the target data report file.

And reporting the target data reporting file to the target reporting mechanism.

In this embodiment, the communication information of the target reporting mechanism may be obtained, and then the target data reporting file may be sent to the target reporting mechanism corresponding to the communication information.

The application constructs a data report file corresponding to the second item data; then compressing the data report file to obtain a target data report file; and then report the said goal data report file to the said goal report organization, in order to realize adopting the report form of the compressed file to report the relevant project data to the organization of goal report, have improved the processing standardization and intelligence of the project data report.

It should be emphasized that, to further ensure the privacy and security of the second item data, the second item data may also be stored in a node of a blockchain.

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.

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 shown in fig. 2, the present application provides an embodiment of an artificial intelligence-based data reporting device, where the embodiment of the device corresponds to the embodiment of the method shown in fig. 2, and the device may be specifically applied to various electronic devices.

As shown in fig. 3, the artificial intelligence based datagram device 300 according to the present embodiment includes: a judging module 301, a first determining module 302, a first obtaining module 303, a second determining module 304, a screening module 305, a second obtaining module 306 and a third determining module 307. Wherein:

A first obtaining module 301, configured to obtain a current time;

a judging module 302, configured to judge whether the current time is within a preset reporting processing time period;

the second obtaining module 303 is configured to obtain, if yes, reporting requirement information corresponding to a preset target reporting mechanism;

the first screening module 304 is configured to screen, based on the reporting requirement information, first item data corresponding to the reporting requirement information from a preset service graph database; wherein the number of first item data includes a plurality;

the second screening module 305 is configured to screen the first item data based on a preset abnormal screening model to obtain corresponding second item data;

and a sending module 306, configured to send the second item datagram to the target reporting mechanism.

In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the method for reporting data based on artificial intelligence in the foregoing embodiment, which is not described herein again.

In some alternative implementations of the present embodiment, the first screening module 304 includes:

the extraction submodule is used for extracting a business project object field and reporting period information from the reporting requirement information;

The first determining submodule is used for carrying out information retrieval on the service graph database based on the service item object field and determining a target field matched with the service item object field from the service graph database;

the first screening sub-module is used for screening target graph data corresponding to the target field from the service graph database based on the reporting period information;

and the second determining submodule is used for taking the target graph data as the first project data.

In some optional implementations of this embodiment, the first screening submodule includes:

the acquisition unit is used for acquiring a data entity with an association relation with the target field from the service graph database based on the reporting period information; wherein the number of data entities comprises a plurality;

the integration unit is used for integrating all the data entities to obtain a corresponding target data entity set;

and the determining unit is used for taking the target data entity set as the target graph data.

In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data reporting 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 second screening module 305 includes:

the analysis submodule is used for inputting the first item data into the abnormality screening model, and carrying out abnormality analysis on the first item data through the abnormality screening model to obtain an abnormality probability value corresponding to the first item data respectively;

the second screening sub-module is used for screening abnormal item data from all the first item data based on the abnormal probability value;

the rejecting sub-module is used for rejecting the abnormal item data from the first item data to obtain normal item data;

and a third determining sub-module, configured to take the normal item data as the second item data.

In some optional implementations of this embodiment, the artificial intelligence based datagram delivery device further includes:

The third acquisition module is used for acquiring pre-constructed project sample data;

the dividing module is used for dividing the project sample data into training data and verification data according to a preset proportion;

the training module is used for training a preset machine learning model based on the training data to obtain a trained initial abnormal screening model;

the verification module is used for carrying out verification processing on the initial abnormal screening model based on the verification data to obtain corresponding analysis accuracy;

the optimization module is used for carrying out iterative optimization on the initial abnormal screening model based on the analysis accuracy and the training data to obtain a trained initial abnormal screening model;

and the determining module is used for taking the trained initial abnormality screening model as the abnormality screening model.

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

the acquisition sub-module is used for acquiring the communication information of the target reporting mechanism;

A fourth determining submodule, configured to determine a target delivery time corresponding to the target delivery mechanism;

and the first sending sub-module is used for sending the second item data report to the target reporting mechanism based on the communication information and the target reporting time.

a construction sub-module for constructing a datagram file corresponding to the second item data;

the compression sub-module is used for compressing the data reporting file to obtain a target data reporting file;

and the second sending sub-module is used for reporting the target data reporting file to the target reporting mechanism.

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 artificial intelligence based datagram 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 executing computer readable instructions of the artificial intelligence based datagram 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.

in the embodiment of the application, the current time is firstly obtained, and whether the current time is in a preset reporting processing time period is judged; if yes, acquiring the information of the reporting requirement corresponding to a preset target reporting mechanism; then, based on the report demand information, screening out first item data corresponding to the report demand information from a preset service diagram database; screening the first item data based on a preset abnormal screening model to obtain corresponding second item data; and finally, the second item data is sent to the target reporting mechanism. The embodiment of the application can automatically and rapidly acquire the project data to be reported corresponding to the reporting requirement information based on the service graph database and the use of the abnormality screening model, and further realize the automatic reporting processing of the project data to be reported based on the preset reporting processing time period, thereby effectively improving the acquisition efficiency and the acquisition intelligence of the data to be reported and improving the reporting efficiency of the data to be reported.

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 datagram 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. The data reporting method based on the artificial intelligence is characterized by comprising the following steps of:

acquiring the current time;

and sending the second item datagram to the target reporting mechanism.

2. The method for reporting data based on artificial intelligence according to claim 1, wherein the step of screening first item data corresponding to the reporting requirement information from a preset service graph database based on the reporting requirement information specifically comprises:

and taking the target graph data as the first project data.

3. The method for reporting data based on artificial intelligence according to claim 2, wherein the step of screening target graph data corresponding to the target field from the service graph database based on the reporting period information specifically comprises:

and taking the target data entity set as the target graph data.

4. The method for reporting data based on artificial intelligence according to claim 1, wherein the step of screening the first item data based on a preset anomaly screening model to obtain corresponding second item data specifically comprises:

and taking the normal project data as the second project data.

5. The method for reporting data based on artificial intelligence according to claim 1, further comprising, before the step of screening the first item data based on a preset anomaly screening model to obtain corresponding second item data:

acquiring pre-constructed project sample data;

6. The method of claim 1, wherein the step of sending the second item datagram to the target delivery mechanism comprises:

acquiring communication information of the target reporting mechanism;

7. The method of claim 1, wherein the step of sending the second item datagram to the target delivery mechanism comprises:

constructing a data report file corresponding to the second item data;

compressing the data report file to obtain a target data report file;

and reporting the target data reporting file to the target reporting mechanism.

8. An artificial intelligence based datagram delivery device comprising:

the first acquisition module is used for acquiring the current time;

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