CN115731028A

CN115731028A - Early warning method, early warning device, electronic equipment and computer readable medium

Info

Publication number: CN115731028A
Application number: CN202211534940.XA
Authority: CN
Inventors: 陈尚志; 朱祖恩; 陈浩欣; 魏晓聪
Original assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Current assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Priority date: 2022-12-02
Filing date: 2022-12-02
Publication date: 2023-03-03

Abstract

The application discloses an early warning method, an early warning device, electronic equipment and a computer readable medium, which relate to the technical field of big data processing, wherein one specific embodiment comprises the steps of receiving an early warning request, obtaining a corresponding data source identifier, and then calling a data source corresponding to the data source identifier to obtain data to be processed; extracting corresponding dimension characteristics in the data to be processed based on preset dimensions, and further determining a service type corresponding to the dimension characteristics; and executing an early warning program corresponding to the service type to determine an early warning type corresponding to the dimension characteristic, and further generating and outputting early warning information. The operation of the post-credit early warning service can be standardized, the risk which is possibly generated can be pre-judged in advance, and the accuracy rate and the fund recall rate of the early warning service are improved.

Description

Early warning method, early warning device, electronic equipment and computer readable medium

Technical Field

The present application relates to the field of big data processing technologies, and in particular, to an early warning method, an early warning device, an electronic device, and a computer-readable medium.

Background

Most of the existing bank post-credit warning systems do not analyze the system according to the service characteristics and the current situation of projects during development, so that the existing post-credit warning systems often cannot accurately identify the projects which may cause problems, and cannot make a prediction on risks in advance.

Disclosure of Invention

In view of this, embodiments of the present application provide an early warning method, an early warning apparatus, an electronic device, and a computer-readable medium, which can solve the problem that the existing post-credit early warning system often cannot accurately identify a project that may cause a problem, so that a risk cannot be predicted in advance.

In order to achieve the above object, according to an aspect of the embodiments of the present application, there is provided an early warning method, including:

receiving an early warning request, acquiring a corresponding data source identifier, and calling a data source corresponding to the data source identifier to acquire data to be processed;

extracting corresponding dimension characteristics in the data to be processed based on preset dimensions, and further determining service types corresponding to the dimension characteristics;

and executing an early warning program corresponding to the service type to determine an early warning type corresponding to the dimension characteristic, and further generating and outputting early warning information.

Optionally, based on a preset dimension, extracting a corresponding dimension feature in the data to be processed, including:

extracting corresponding first range dimension characteristics in the data to be processed based on the first range dimension;

extracting corresponding second range dimension characteristics in the data to be processed based on the second range dimension; wherein the content of the first and second substances,

the first range corresponding to the first range dimension is larger than the second range corresponding to the second range dimension.

Optionally, determining a service type corresponding to the dimension feature includes:

and capturing the negative public opinion data of the current day, further matching the negative public opinion data with the dimension characteristics, and determining the corresponding service type according to the matching result.

Optionally, determining a corresponding service type according to the matching result includes:

responding to the matching result that the service type corresponding to the dimension characteristics is negative public opinion service;

and responding to the mismatching result, acquiring a preset workflow to determine a next node, transferring to the next node to execute the matching logic of the next node, and further determining the service type corresponding to the dimensional feature according to the execution result of the matching logic.

Optionally, obtaining the preset workflow includes:

and calling the monitoring model to obtain a preset workflow in the monitoring model.

Optionally, generating and outputting the warning information includes:

determining the number of second ranges comprised by the first range;

and generating and outputting early warning information according to the quantity and the early warning type.

Optionally, generating and outputting the warning information according to the number and the warning type, including:

determining a second range corresponding to the early warning type;

and generating and outputting the early warning information with the same quantity according to the early warning type and the corresponding second range.

In addition, this application still provides an early warning device, includes:

the receiving unit is configured to receive the early warning request, acquire the corresponding data source identifier and further call the data source corresponding to the data source identifier to acquire the data to be processed;

the service type determining unit is configured to extract corresponding dimension characteristics in the data to be processed based on preset dimensions, and further determine service types corresponding to the dimension characteristics;

and the early warning unit is configured to execute an early warning program corresponding to the service type so as to determine an early warning type corresponding to the dimension characteristic, and further generate and output early warning information.

In particular, the traffic type determination unit is further configured to:

a first range corresponding to the first range dimension is greater than a second range corresponding to the second range dimension.

In particular, the traffic type determination unit is further configured to:

responding to the matching result as matching, and determining the service type corresponding to the dimension characteristics as negative public opinion service;

In particular, the traffic type determination unit is further configured to:

Specifically, the early warning unit is further configured to:

determining the number of second ranges comprised by the first range;

Specifically, the early warning unit is further configured to:

determining a second range corresponding to the early warning type;

and generating and outputting the same number of early warning information according to the early warning type and the corresponding second range.

In addition, this application still provides an early warning electronic equipment, includes: one or more processors; a storage device for storing one or more programs which, when executed by one or more processors, cause the one or more processors to implement the method of early warning as described above.

In addition, the present application also provides a computer readable medium, on which a computer program is stored, which when executed by a processor implements the above-mentioned early warning method.

To achieve the above object, according to still another aspect of embodiments of the present application, there is provided a computer program product.

A computer program product according to an embodiment of the present application includes a computer program, and when the computer program is executed by a processor, the early warning method according to an embodiment of the present application is implemented.

One embodiment of the above invention has the following advantages or benefits: the method comprises the steps of obtaining a corresponding data source identifier by receiving an early warning request, and calling a data source corresponding to the data source identifier to obtain data to be processed; extracting corresponding dimension characteristics in the data to be processed based on preset dimensions, and further determining service types corresponding to the dimension characteristics; and executing an early warning program corresponding to the service type to determine an early warning type corresponding to the dimension characteristic, and further generating and outputting early warning information. The operation of the post-credit early warning service can be standardized, the risk which is possibly generated can be pre-judged in advance, and the accuracy rate and the fund recall rate of the early warning service are improved.

Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.

Drawings

The drawings are included to provide a further understanding of the application and are not to be construed as limiting the application. Wherein:

FIG. 1 is a schematic diagram of a main flow of an early warning method according to one embodiment of the present application;

FIG. 2 is a schematic diagram of a main flow of an early warning method according to one embodiment of the present application;

FIG. 3 is a schematic flow chart of a method for early warning according to an embodiment of the present application;

fig. 4 is a schematic view of an application scenario of an early warning method according to an embodiment of the present application;

fig. 5 is a schematic diagram of the main units of an early warning device according to an embodiment of the present application;

FIG. 6 is an exemplary system architecture diagram to which embodiments of the present application may be applied;

fig. 7 is a schematic structural diagram of a computer system suitable for implementing the terminal device or the server according to the embodiment of the present application.

Detailed Description

The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness. It should be noted that, in the technical solution of the present application, the aspects of collecting, analyzing, using, transmitting, storing, etc. of the related user personal information all conform to the regulations of relevant laws and regulations, are used for legal and reasonable purposes, are not shared, leaked or sold outside the aspects of legal use, etc., and are under the supervision and management of the supervision department. Necessary measures should be taken for the personal information of the user to prevent illegal access to such personal information data, ensure that personnel who have access to the personal information data comply with the regulations of relevant laws and regulations, and ensure the security of the personal information of the user. Once these user personal information data are no longer needed, the risk should be minimized by limiting or even prohibiting data collection and/or deleting data.

User privacy is protected by de-identifying data when used, including in certain related applications, such as by removing a particular identifier, controlling the amount or specificity of stored data, controlling how data is stored, and/or other methods of de-identifying when used.

Fig. 1 is a schematic diagram of a main flow of an early warning method according to an embodiment of the present application, and as shown in fig. 1, the early warning method includes:

step S101, receiving the early warning request, obtaining a corresponding data source identifier, and then calling a data source corresponding to the data source identifier to obtain the data to be processed.

In this embodiment, an execution subject (for example, a server) of the warning method may receive the warning request through a wired connection or a wireless connection. The early warning request may carry a data source identifier, and after the execution main body obtains the data source identifier, the execution main body may use a corresponding data source based on the data source table to obtain the data to be processed. The data source may be a loan data source corresponding to a building project. The data to be processed may include, for example, loan data corresponding to a target floor project in the floor projects, where the loan data may include data of borrowers, credit line, loan products, loan amount, overdue or not, credit investigation information, basic information of development enterprises, and the like.

Step S102, extracting corresponding dimension characteristics in the data to be processed based on preset dimensions, and further determining the service type corresponding to the dimension characteristics.

The preset dimension may be, for example, two dimensions of a building and a building, and the preset dimension is not specifically limited in the embodiment of the present application. The data processing and output can be performed on data to be processed from two dimensions of a building and a building, and the feature extraction model can be called to extract dimensional features corresponding to the two dimensions of the building and the building in the data to be processed, for example, the feature extraction model can comprise key time point progress features corresponding to the two dimensions of the building and the building, post-delivery certificate timeliness features, negative public opinion features, mortgage registration non-handling rate features, mortgage value trend features, feature of whether the balance of a deposit is sufficient or not, shutdown slow-construction pre-guide features and the like.

And determining a corresponding service type according to the dimension characteristics, wherein the service type can be an early warning service or a non-early warning service. The non-early warning service may include post-credit fund use monitoring service, post-credit fund use voucher uploading service, and the like, and the non-early warning service is not specifically limited in the embodiment of the present application.

Specifically, extracting corresponding dimension features in the data to be processed based on preset dimensions includes:

extracting corresponding first range dimension characteristics in the data to be processed based on the first range dimension; wherein the first range dimension may be, for example, a floor dimension. The first range dimension feature may be, for example, a floor dimension feature.

Extracting corresponding second range dimension characteristics in the data to be processed based on the second range dimension; the second range dimension may be, for example, a building dimension, and the second range dimension characteristic may be, for example, a building dimension characteristic.

Wherein a first range corresponding to the first range dimension is larger than a second range corresponding to the second range dimension. For example, the dimension of the building is larger than the dimension of the building.

And step S103, executing an early warning program corresponding to the service type to determine an early warning type corresponding to the dimension characteristic, and further generating and outputting early warning information.

When the service type is an early warning service, an early warning program corresponding to the early warning service can be called to determine a corresponding early warning type according to the dimension characteristics, wherein the early warning type can be, for example, an early warning grade, and the early warning grade can be divided into a first-level early warning, a second-level early warning and a third-level early warning, wherein the severity of the third-level early warning is higher than that of the second-level early warning.

Specifically, the execution subject may execute the early warning program corresponding to the service type to call the classification model, input the dimension characteristic into the classification model, and output the corresponding early warning level, that is, the early warning type. The early warning information is generated according to the early warning type, for example, the early warning information may be "the after-credit early warning level is three-level early warning, please handle in time", and the content and the form of the early warning information are not specifically limited in the embodiments of the present application.

In the embodiment, the corresponding data source identifier is acquired by receiving the early warning request, and then the data source corresponding to the data source identifier is called to acquire the data to be processed; extracting corresponding dimension characteristics in the data to be processed based on preset dimensions, and further determining a service type corresponding to the dimension characteristics; and executing an early warning program corresponding to the service type to determine an early warning type corresponding to the dimension characteristic, and further generating and outputting early warning information. The operation of the post-credit early warning service can be standardized, the risk which is possibly generated can be pre-judged in advance, and the accuracy rate and the fund recall rate of the early warning service are improved.

Fig. 2 is a schematic main flow diagram of a warning method according to an embodiment of the present application, and as shown in fig. 2, the warning method includes:

step S201, receiving the early warning request, obtaining a corresponding data source identifier, and then calling a data source corresponding to the data source identifier to obtain the data to be processed.

The early warning request may carry a data source identifier, and after the execution main body obtains the data source identifier, the execution main body may use a corresponding data source to obtain the data to be processed based on the data source table. The data source may be a loan data source corresponding to the car selling project. The data to be processed may include, for example, loan data corresponding to a target car selling item in the car selling items, where the loan data may include data of borrowers, credit line, loan products, loan amount, overdue or not, credit investigation information, basic car selling enterprise information, and the like.

Step S202, extracting corresponding dimension characteristics in the data to be processed based on preset dimensions.

The preset dimensions may be, for example, a vehicle type and a vehicle price, and the execution subject may extract corresponding dimensional features in the data to be processed based on the vehicle type and the vehicle price. The dimensional features may include high-level features and low-level features related to the cart type and the cart price. The high-level features may be abstract features such as an overall upgrade space of the vehicle and an attractive force of the vehicle, and the low-level features may be concrete features such as a shape and a color of the vehicle.

And step S203, capturing the negative public opinion data of the current day, further matching the negative public opinion data with the dimensional characteristics, and determining the corresponding service type according to the matching result.

The execution subject may capture negative public opinion data of the current day, such as vehicle quality problem, vehicle delivery problem, etc., of the received early warning request, and when the matching of the negative public opinion data and the dimensional feature is successful, determine the corresponding service type as a negative public opinion processing service.

And step S204, executing an early warning program corresponding to the service type to determine an early warning type corresponding to the dimension characteristic, and further generating and outputting early warning information.

And when the service type is the negative public opinion processing service, executing an early warning program corresponding to the negative public opinion processing service to determine an early warning type, namely an early warning grade, corresponding to the negative public opinion processing service, and generating and outputting corresponding early warning information according to the early warning grade. The operation of the post-credit early warning service can be standardized, and the accuracy rate and the fund recall rate of the early warning service are improved.

Fig. 3 is a schematic main flow diagram of an early warning method according to an embodiment of the present application, and as shown in fig. 2, the early warning method includes:

step S301, receiving the early warning request, obtaining the corresponding data source identification, and then calling the data source corresponding to the data source identification to obtain the data to be processed.

The data source may be, for example, a credit data source. The data to be processed may be, for example, credit rating data of the user.

Step S302, extracting corresponding dimension characteristics in the data to be processed based on preset dimensions.

The preset dimension may be, for example, a salary dimension and an asset dimension of the user participating in the credit loan. The execution subject can extract corresponding dimension characteristics in the data to be processed based on salary dimensions and asset dimensions. The dimension features may include fusion features corresponding to a high-level feature and a low-level feature corresponding to a salary dimension in the data to be processed and may include fusion features corresponding to a high-level feature and a low-level feature corresponding to an asset dimension in the data to be processed.

Step S303, capturing negative public opinion data of the current day, and further matching the negative public opinion data with the dimension characteristics.

The day negative public opinion data can be overdue data of users participating in credit loan, and the overdue data is matched with the obtained dimension characteristics.

And S304, in response to the matching result being matching, determining the service type corresponding to the dimension characteristic as the negative public opinion service.

And when the negative public opinion data is matched with the dimension characteristics, determining the service type corresponding to the dimension characteristics as the negative public opinion service.

Step S305, in response to the mismatch of the matching result, acquiring a preset workflow to determine a next node, and transferring to the next node to execute the matching logic of the next node, thereby determining the service type corresponding to the dimensional feature according to the execution result of the matching logic.

And when the overdue data are not matched with the dimension characteristics, acquiring a preset workflow. The execution programs of all nodes in the preset workflow are respectively as follows: the method comprises a project key time node progress overdue dimension detection program, a project delivery long-term failure to handle evidence detection program, a project major negative public opinion detection program, a mortgage registration due-to-do and non-do rate detection program, a mortgage value gliding detection program, a cooperative project guarantee fund balance deficiency detection program, a project shutdown and slow-construction pilot detection program and the like. After the preset workflow is obtained, determining an execution program of a next node corresponding to the current detection program, for example, when the current detection program is a program major negative public opinion detection program, the execution program of the corresponding next node is a mortgage registration non-handling rate detection program, the execution main body can introduce the dimension characteristics into the execution program of the next node, namely the mortgage registration non-handling rate detection program, to determine whether the two programs are matched, if so, determining that the service type corresponding to the dimension characteristics is a mortgage registration non-handling rate detection service, if not, continuously introducing the dimension characteristics into the execution program of the next node, namely a mortgage value gliding detection program, to determine whether the two programs are matched, if so, continuously introducing the dimension characteristics into a cooperative project guarantee fund balance insufficiency detection program, until the execution program of the matched node is found to determine that the two programs are not matched.

Specifically, obtaining a preset workflow includes: and calling the monitoring model to obtain a preset workflow in the monitoring model.

And step S306, executing an early warning program corresponding to the service type to determine an early warning type corresponding to the dimension characteristic, and further generating and outputting early warning information.

Specifically, generating and outputting early warning information comprises: determining the number of second ranges comprised by the first range; and generating and outputting early warning information according to the quantity and the early warning type.

Illustratively, the first range may be, for example, a floor range corresponding to the credit data source. The second range may be, for example, a building range of each building corresponding to the building range corresponding to the credit data source.

Specifically, according to the number and the early warning type, generating and outputting early warning information, comprising:

and determining a second range corresponding to the early warning type, wherein when the early warning type is a major negative public opinion secondary early warning, the corresponding second range can be the range of each building correspondingly.

The execution subject may generate the warning information according to the warning type, the first range, and the number of the second ranges corresponding to the first range. The number of the warning information may be the same as the number of the second range. For example, when the first range is a building range, and the second range is a building range, and when there are 5 second ranges, it is necessary to generate and output 5 pieces of warning information according to 1 first range (for example, 1 building) and the corresponding 5 second ranges (for example, 5 buildings corresponding to 1 building). Illustratively, the output presentation is performed according to risk items (namely, the early warning type of the application), the building project and the building dimension of the project. Namely, one risk item, one building project number and one building output one piece of early warning information, and a plurality of pieces of early warning information are correspondingly output if one building project relates to a plurality of risk items and the same building project has a plurality of dealers (corresponding to a plurality of project numbers).

Further, the data output item may further include: statistical time, first-level branch code, first-level branch name, second-level branch code, second-level branch name, company code, company name, project number, group enterprise name, group enterprise number, partner name, social unification credit code, partner actual controller, partner legal representative, partner stakeholder, partner protocol number, cooperation status, cooperation start date, cooperation deadline, first loan issuance date, last loan issuance date, loan number, overdue balance, overdue number, overdue rate, bad balance, bad number, bad rate, forenotice mortgage registration number, official mortgage registration number, forenotice mortgage registration rate, official mortgage registration rate, appointment capping time, appointment completion time, appointment delivery time, monitoring type, progress type, problem type (building problem, project problem), whether a building is newly added on the month. Project doubt can be evaluated by using as few fields as possible through the whole data output item, and the labor cost of business personnel is reduced.

Fig. 4 is a schematic view of an application scenario of the warning method according to an embodiment of the present application. The early warning method is applied to a post-credit early warning scene. As shown in fig. 4, the early warning method firstly extracts data from different data sources, the data sources are data components of different systems and external data, the data sources respectively comprise loan data, project data, building data, guarantee contract data and external data, and the loan data, the project data, the building data, the guarantee contract data and the external data are input into a monitoring model for data processing after preprocessing of data deduplication and cleaning. According to the embodiment of the application, various monitoring models are designed aiming at different scenes and risk factors of a building project, early warning monitoring is carried out from two detection dimensions of the building and the building, finally, data (namely, an integrated output item including a suspected building and a suspected building) processed by the models are transmitted to a background and a front end of an early warning system to be displayed to early warning service personnel, and an early warning service process is completed. The monitoring model can comprise a building project key time point schedule overdue early warning model, a building project long-term failure to transact certificate early warning model after delivery, a building project major negative public opinion early warning model, a building project mortgage registration rate low early warning model, a building project mortgage value gliding early warning model, a building project guarantee fund shortage early warning model and a building project shutdown slow-building early warning model.

In an example, the embodiment of the application designs and realizes 7 risk item early warning models according to the service scenes and characteristics of the building project, and performs data processing and output according to two different dimensions of the building and the building, wherein the early warning models have the following details:

(1) Early warning that node progress of key time of cooperative building project is overdue

And (3) capturing the items of the building tray items in the system, namely appointed capping time, appointed completion time or appointed house-handing time, before the monitoring time or the monitoring time every month, rejecting the items with the ratio of the past cooperation ending time or the stock loan done formal mortgage higher than 90%, and generating an early warning item list.

(2) Early warning of failure to transact certificates for a long time after delivery of cooperative building projects

And after the capturing system is captured every month and exceeds 2 years after the appointed delivery date of the project, or after the first credit of the project is released for 3 years, if the formal mortgage registration rate is still 0, early warning is carried out on the building project.

(3) Big negative public opinion early warning of building project

And automatically capturing developers or floor projects with negative public opinions and target cooperation floor projects every day to be matched, and if the matching is successful, early warning the developers, the floor projects and the target cooperation floor projects.

(4) Early warning of high rate of non-handling due to building project mortgage registration

If the same building project should be dealt with the building project with the unpaid notice (record) mortgage registration rate being more than or equal to fifty percent, or the same building project should be dealt with the building project with the unpaid official mortgage registration rate being more than or equal to fifty percent, the building project is pre-warned.

(5) Underground engineering mortgage value glide early warning

And obtaining the latest collateral confirmation value of the inventory real estate development loan project with the classification of 'real estate class under construction' less than the evaluation value of the latest construction project collateral, and the latest pledge rate of the construction project collateral greater than the highest pledge rate set by the real estate development loan project, and then early warning the floor project.

(6) Cooperative project guarantee fund insufficient balance early warning

And searching the monthly balance of the deposit account of the new generation account, searching corresponding information of the cooperative project or the building in the individual loan system, revealing a specific management bank of the individual loan, and early warning the building project if the monthly balance of the deposit account is less than the minimum deposit limit of the deposit account of the cooperative building project.

(7) Forewarning of stopping, slow building and forewarning of building project

And monitoring the construction progress of the project engineering on the building under construction, the attendance number of workers on the construction site, the water and electricity consumption of the construction site, the use of engineering machinery such as a tower crane and the like, the supervision fund transfer corresponding engineering progress and the like according to months.

Statistics were then made for the following cases:

A. comparing the change conditions of attendance checking of workers and the like: for the same building project, monitoring data of two adjacent months (the month is more than the last month) are captured and compared, and the number of workers checking the attendance is reduced by more than 30 percent (about the specific amount of change, the people need to be discussed and determined).

B. Comparing progress advancing conditions of the projects: the method comprises the steps of picking up corresponding project progress information of a building project with a payment record for monitoring monthly supervision account fund, picking up a capping time and a completion time (actual completion time) from a system as a project for monitoring the monthly, comparing the capping time in the system as the project for monitoring the monthly, judging whether the actual project progress is capped or not, judging whether the completion time in the system is monthly or not, and judging whether the actual project progress is completed or not.

C. Worker wage distribution condition: the condition of the generation of the bank payroll supervision special account is monitored, whether the wages of workers are normally issued is monitored by monitoring the condition of the generation of the bank payroll supervision special account, the related data is compared with the previous month, and the sending amount is reduced by more than 50%.

D. Monitoring project state, namely monitoring the project state condition, wherein the state is the declaration condition of other abnormal states such as shutdown and the like.

E. Monitoring the fund of the bank account, capturing the balance of the fund of the account in two adjacent months (this month is more than the last month), and comparing, wherein the fund transfer exceeds more than 50 percent.

And matching the floor meeting the arbitrary conditions with the floor of the target cooperation project, identifying the floor matched with the floor of the target cooperation project, and generating an early warning list based on all floor projects meeting the conditions. According to the method and the device for processing the data of the building project, the detailed suspicious point information of the building project can be obtained through processing of the monitoring model, the information comprises a plurality of fields, however, some fields are possibly less helpful for suspicious point verification, and on the other hand, the information between the fields can also be redundant. In order to facilitate business personnel to check the doubtful point information of the building project, the embodiment of the application designs a core output item aiming at the doubtful point information of the building project by summarizing the information with larger risk correlation of the building project and combining the loan business characteristics of the building project, can evaluate the doubtful point of the building project by using fields as few as possible, and reduces the labor cost of the business personnel. And (4) carrying out output display according to the risk items, the building project and the project building dimension. Namely, one risk item, one building project number and one building output one piece of early warning information, and a plurality of items are correspondingly output as if one building project relates to a plurality of risk items and the same building project has a plurality of dealers (corresponding to a plurality of project numbers). The post-credit monitoring business personnel can have clearer work flow and frame. The operation of the post-credit early warning service can be standardized, and the efficiency is improved. The accuracy and recall rate of the early warning service after the project is credited are improved. External factors are introduced into the early warning service, and risk assessment is carried out on the project from multiple angles. Based on the integration output item, the doubtful points of the building project can be evaluated by using fields as few as possible, and the labor cost of business personnel is reduced.

Fig. 5 is a schematic diagram of main units of the warning device according to the embodiment of the application. As shown in fig. 5, the warning apparatus 500 includes a receiving unit 501, a service type determining unit 502, and a warning unit 503.

The receiving unit 501 is configured to receive the early warning request, acquire the corresponding data source identifier, and then call the data source corresponding to the data source identifier to acquire the data to be processed.

The service type determining unit 502 is configured to extract corresponding dimension features from the data to be processed based on a preset dimension, and then determine a service type corresponding to the dimension features.

The early warning unit 503 is configured to execute an early warning program corresponding to the service type to determine an early warning type corresponding to the dimensional feature, and then generate and output early warning information.

In some embodiments, the traffic type determination unit 502 is further configured to: extracting corresponding first range dimension characteristics in the data to be processed based on the first range dimension; extracting corresponding second range dimension characteristics in the data to be processed based on the second range dimension; wherein a first range corresponding to the first range dimension is larger than a second range corresponding to the second range dimension.

In some embodiments, the traffic type determination unit 502 is further configured to: and capturing the negative public opinion data of the current day, further matching the negative public opinion data with the dimension characteristics, and determining the corresponding service type according to the matching result.

In some embodiments, the traffic type determination unit 502 is further configured to: responding to the matching result that the service type corresponding to the dimension characteristics is negative public opinion service; and responding to the mismatching result, acquiring a preset workflow to determine the next node, transferring to the next node to execute the matching logic of the next node, and further determining the service type corresponding to the dimensional feature according to the execution result of the matching logic.

In some embodiments, the traffic type determination unit 502 is further configured to: and calling the monitoring model to obtain a preset workflow in the monitoring model.

In some embodiments, the pre-warning unit 503 is further configured to: determining the number of second ranges comprised by the first range; and generating and outputting early warning information according to the quantity and the early warning type.

In some embodiments, the pre-warning unit 503 is further configured to: determining a second range corresponding to the early warning type; and generating and outputting the early warning information with the same quantity according to the early warning type and the corresponding second range.

It should be noted that the early warning method and the early warning apparatus have corresponding relationships in specific implementation contents, and therefore repeated contents are not described again.

Fig. 6 shows an exemplary system architecture 600 to which the warning method or warning apparatus according to the embodiment of the present application may be applied.

As shown in fig. 6, the system architecture 600 may include

terminal devices

601, 602, 603, a network 604, and a server 605. The network 604 serves to provide a medium for communication links between the

terminal devices

601, 602, 603 and the server 605. Network 604 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.

A user may use the

terminal devices

601, 602, 603 to interact with the server 605 via the network 604 to receive or send messages or the like. The

terminal devices

601, 602, 603 may have installed thereon various communication client applications, such as shopping applications, web browser applications, search applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).

The

terminal devices

601, 602, 603 may be various electronic devices having an early warning processing screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 605 may be a server providing various services, such as a background management server (for example only) providing support for pre-warning requests submitted by users using the

terminal devices

601, 602, 603. The background management server can receive the early warning request, acquire the corresponding data source identification and then call the data source corresponding to the data source identification to acquire the data to be processed; extracting corresponding dimension characteristics in the data to be processed based on preset dimensions, and further determining a service type corresponding to the dimension characteristics; and executing an early warning program corresponding to the service type to determine an early warning type corresponding to the dimension characteristic, and further generating and outputting early warning information. The operation of the post-credit early warning service can be standardized, the risk which is possibly generated can be prejudged in advance, and the accuracy rate and the fund recall rate of the early warning service are improved.

It should be noted that the warning method provided in the embodiment of the present application is generally executed by the server 605, and accordingly, the warning device is generally disposed in the server 605.

It should be understood that the number of terminal devices, networks, and servers in fig. 6 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

Referring now to FIG. 7, shown is a block diagram of a computer system 700 suitable for use in implementing a terminal device of an embodiment of the present application. The terminal device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.

As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU) 701, which can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM703, various programs and data necessary for the operation of the computer system 700 are also stored. The CPU701, the ROM702, and the RAM703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a liquid crystal credit authorization query processor (LCD), and the like, and a speaker and the like; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.

In particular, according to embodiments disclosed herein, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments disclosed herein include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program executes the above-described functions defined in the system of the present application when executed by the Central Processing Unit (CPU) 701.

It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a receiving unit, a service type determining unit, and an early warning unit. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.

As another aspect, the present application also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be separate and not assembled into the device. The computer readable medium carries one or more programs, and when the one or more programs are executed by one device, the device receives an early warning request, acquires a corresponding data source identifier, and further calls a data source corresponding to the data source identifier to acquire to-be-processed data; extracting corresponding dimension characteristics in the data to be processed based on preset dimensions, and further determining service types corresponding to the dimension characteristics; and executing an early warning program corresponding to the service type to determine an early warning type corresponding to the dimension characteristic, and further generating and outputting early warning information.

The computer program product of the present application comprises a computer program, which when executed by a processor implements the warning method in the embodiments of the present application.

According to the technical scheme of the embodiment of the application, the operation of the post-credit early warning service can be standardized, the risk which is possibly generated can be pre-judged in advance, and the accuracy rate and the fund recall rate of the early warning service are improved.

The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An early warning method, comprising:

extracting corresponding dimension characteristics in the data to be processed based on preset dimensions, and further determining a service type corresponding to the dimension characteristics;

2. The method according to claim 1, wherein the extracting corresponding dimension features in the data to be processed based on the preset dimension includes:

extracting corresponding first range dimension characteristics in the data to be processed based on a first range dimension;

extracting corresponding second range dimension characteristics in the data to be processed based on a second range dimension; wherein the content of the first and second substances,

a first range corresponding to the first range dimension is larger than a second range corresponding to the second range dimension.

3. The method of claim 1, wherein the determining the traffic type corresponding to the dimensional feature comprises:

and capturing negative public opinion data of the current day, further matching the negative public opinion data with the dimension characteristics, and determining a corresponding service type according to a matching result.

4. The method of claim 3, wherein the determining the corresponding service type according to the matching result comprises:

responding to the matching result, and determining that the service type corresponding to the dimension characteristics is negative public opinion service;

and responding to the mismatching result, acquiring a preset workflow to determine a next node, circulating to the next node to execute the matching logic of the next node, and determining the service type corresponding to the dimensional feature according to the execution result of the matching logic.

5. The method of claim 4, wherein the obtaining the preset workflow comprises:

and calling a monitoring model to obtain a preset workflow in the monitoring model.

6. The method of claim 2, wherein generating and outputting the warning information comprises:

determining the number of the second ranges that the first range encompasses;

7. The method of claim 6, wherein generating and outputting early warning information according to the quantity and the early warning type comprises:

determining a second range corresponding to the early warning type;

and generating and outputting the early warning information with the same quantity as the quantity according to the early warning type and the corresponding second range.

8. An early warning device, comprising:

the receiving unit is configured to receive the early warning request, acquire a corresponding data source identifier and then call a data source corresponding to the data source identifier to acquire data to be processed;

the service type determining unit is configured to extract corresponding dimension characteristics in the data to be processed based on preset dimensions, and further determine a service type corresponding to the dimension characteristics;

9. The apparatus of claim 8, wherein the traffic type determination unit is further configured to:

10. The apparatus of claim 8, wherein the traffic type determination unit is further configured to:

11. The apparatus of claim 10, wherein the traffic type determination unit is further configured to:

and responding to the mismatching result, acquiring a preset workflow to determine a next node, and circulating to the next node to execute the matching logic of the next node, so as to determine the service type corresponding to the dimensional feature according to the execution result of the matching logic.

12. The apparatus of claim 11, wherein the traffic type determining unit is further configured to:

13. The apparatus of claim 9, wherein the early warning unit is further configured to:

determining the number of the second ranges that the first range encompasses;

14. An early warning electronic device, comprising:

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.

15. A computer-readable medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method of any one of claims 1-7.

16. A computer program product comprising a computer program, characterized in that the computer program realizes the method according to any of claims 1-7 when executed by a processor.