CN112712270B - Information processing method, device, equipment and storage medium - Google Patents

Information processing method, device, equipment and storage medium Download PDF

Info

Publication number
CN112712270B
CN112712270B CN202011639532.1A CN202011639532A CN112712270B CN 112712270 B CN112712270 B CN 112712270B CN 202011639532 A CN202011639532 A CN 202011639532A CN 112712270 B CN112712270 B CN 112712270B
Authority
CN
China
Prior art keywords
risk
content
complaint
problem information
information
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202011639532.1A
Other languages
Chinese (zh)
Other versions
CN112712270A (en
Inventor
陈俊霖
杨海军
徐倩
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
WeBank Co Ltd
Original Assignee
WeBank Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by WeBank Co Ltd filed Critical WeBank Co Ltd
Priority to CN202011639532.1A priority Critical patent/CN112712270B/en
Publication of CN112712270A publication Critical patent/CN112712270A/en
Application granted granted Critical
Publication of CN112712270B publication Critical patent/CN112712270B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0282—Rating or review of business operators or products
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services

Abstract

The invention discloses an information processing method, an information processing device, information processing equipment and a storage medium, wherein the method comprises the following steps: acquiring a plurality of pieces of problem information of user complaints in a preset time period, wherein each piece of problem information comprises: at least one complaint object and complaint content aiming at each complaint object, inputting the plurality of pieces of problem information into a pre-trained risk early warning model for cluster analysis, and obtaining and outputting a risk analysis result, wherein the risk analysis result comprises: at least one risk object, and risk content for each risk object, the at least one risk object being an object of the at least one complaint object, the risk content being content of the complaint content of the corresponding risk object. According to the invention, the risk object and the risk object in the problem information can be found in time, the timeliness of risk finding is improved, and the problem that the property safety of the user is damaged is avoided.

Description

Information processing method, device, equipment and storage medium
Technical Field
The present invention relates to the field of financial science and technology, and in particular, to an information processing method, apparatus, device, and storage medium.
Background
The financial supervisory institution is an institution for supervising and managing a financial system according to legal regulations, and can supervise and manage a financial market according to regulations, issue commands and regulations related to financial supervision and management and business, supervise and manage legal compliance operation of the financial institution, and the like.
In the prior art, financial authorities such as financial authorities, letters bureaus and the like can also receive incoming letters of users and process related letters, and when the financial authorities receive complaint letters of users, the common practice is as follows: the problem of specific reflection of the complaint letter is determined by professionals, for example, certain financial institutions and financial transaction platforms can have illegal phenomena, and then solutions of related events of users are replied, and public security departments are contacted for processing the serious events.
In practical application, as the financial supervision institutions only contact the public security department for processing relatively serious events, some illegal financial institutions and illegal financial transaction platforms can be discovered after serious economic losses are caused to some users, and therefore, the problems of untimely risk discovery and damage to property safety of users can exist in the existing letter and visit processing method.
Disclosure of Invention
The invention mainly aims to provide an information processing method, an information processing device and a storage medium, and aims to solve the problems that risks are not found timely and property safety of users is damaged in the existing letter processing method.
In order to achieve the above object, the present invention provides an information processing method including:
Acquiring a plurality of pieces of problem information of user complaints in a preset time period, wherein each piece of problem information comprises: at least one complaint object and complaint content for each complaint object;
Inputting the plurality of pieces of problem information into a pre-trained risk early warning model for cluster analysis to obtain a risk analysis result, wherein the risk analysis result comprises the following steps: at least one risk object that is an object of the at least one complaint object and a risk content of each risk object that is a content of the complaint content of the corresponding risk object;
and outputting the risk analysis result.
In one possible implementation, the method further includes:
determining at least one to-be-pushed processing department of the risk analysis result;
Processing the risk analysis results by using a risk result processing strategy aiming at each to-be-pushed processing department to obtain risk push content aiming at each to-be-pushed processing department;
and pushing the risk push content aiming at each to-be-pushed processing department to the risk processing equipment of the corresponding to-be-pushed processing department.
In another possible implementation, the method further includes:
Determining at least one industry associated with the risk analysis result according to at least one risk object included in the risk analysis result and the risk content of each risk object;
determining an industry risk level of the risk analysis result in each industry according to the risk analysis result and a preset industry risk level strategy corresponding to each industry in the at least one industry;
pushing the risk analysis result and the industry risk level in each industry to risk processing equipment of a corresponding industry processing department.
In another possible implementation manner, the inputting the plurality of pieces of problem information into a pre-trained risk early-warning model for cluster analysis to obtain a risk analysis result includes:
Extracting keywords from the plurality of pieces of problem information by using the risk early warning model, and determining at least two keywords in the plurality of pieces of problem information;
clustering the at least two keywords based on a preset clustering rule, and determining at least one clustering object set;
If the at least one clustered object set contains objects with the number of the objects being greater than or equal to the preset risk threshold, determining at least one risk object in the at least one clustered object set;
Screening at least one piece of problem information aiming at each risk object from the plurality of pieces of problem information;
And carrying out content analysis on the at least one piece of problem information of each risk object to determine the risk content of each risk object.
Optionally, the clustering the at least two keywords based on a preset clustering rule, determining at least one clustered object set includes:
clustering the at least two keywords based on the platform type, and determining at least one clustering object set; or alternatively
And clustering the at least two keywords based on the enterprise name, and determining at least one clustering object set.
In yet another possible implementation, the method further includes:
determining a risk level of the risk analysis result;
And when the risk level is greater than a preset risk level, pushing the risk analysis result to risk processing equipment.
In yet another possible implementation, the method further includes:
acquiring a plurality of pieces of historical problem information;
processing the plurality of pieces of historical problem information to obtain a training sample set of the risk early warning model, wherein the training sample set comprises: a plurality of pieces of history marking problem information having risk marking objects and/or risk marking contents;
and training a preset network model by using the training sample set to obtain the risk early warning model.
Optionally, training a preset network model by using the training sample set to obtain the risk early-warning model includes:
word segmentation processing is respectively carried out on each piece of history marking problem information in the training sample set, so that a word set corresponding to each piece of history marking problem information is obtained;
Sequentially inputting a word set corresponding to each piece of history marking problem information into the preset network model, and adjusting parameters of the preset network model until the preset network model sequentially outputs a risk marking object corresponding to each piece of history marking problem information and risk marking contents of the risk marking object;
and determining the trained preset network model as the risk early warning model.
The present invention also provides an information processing apparatus including:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a plurality of pieces of problem information of user complaints in a preset time period, and each piece of problem information comprises: at least one complaint object and complaint content for each complaint object;
the processing module is used for inputting the plurality of pieces of problem information into a pre-trained risk early warning model for cluster analysis to obtain a risk analysis result, and the risk analysis result comprises: at least one risk object that is an object of the at least one complaint object and a risk content of each risk object that is a content of the complaint content of the corresponding risk object;
And the output module is used for outputting the risk analysis result.
In one possible implementation, the processing module is further configured to:
determining at least one to-be-pushed processing department of the risk analysis result;
Processing the risk analysis results by using a risk result processing strategy aiming at each to-be-pushed processing department to obtain risk push content aiming at each to-be-pushed processing department;
The output module is further configured to push the risk push content for each to-be-pushed processing department to the risk processing device of the corresponding to-be-pushed processing department.
In another possible implementation manner, the processing module is further configured to:
Determining at least one industry associated with the risk analysis result according to at least one risk object included in the risk analysis result and the risk content of each risk object;
determining an industry risk level of the risk analysis result in each industry according to the risk analysis result and a preset industry risk level strategy corresponding to each industry in the at least one industry;
the output module is further used for pushing the risk analysis result and the industry risk level in each industry to risk processing equipment of a corresponding industry processing department.
In yet another possible implementation manner, the processing module is specifically configured to:
Extracting keywords from the plurality of pieces of problem information by using the risk early warning model, and determining at least two keywords in the plurality of pieces of problem information;
clustering the at least two keywords based on a preset clustering rule, and determining at least one clustering object set;
If the at least one clustered object set contains objects with the number of the objects being greater than or equal to the preset risk threshold, determining at least one risk object in the at least one clustered object set;
Screening at least one piece of problem information aiming at each risk object from the plurality of pieces of problem information;
And carrying out content analysis on the at least one piece of problem information of each risk object to determine the risk content of each risk object.
Optionally, the processing module is configured to cluster the at least two keywords based on a preset clustering rule, and determine at least one clustered object set, specifically:
The processing module is specifically configured to cluster the at least two keywords based on a platform type, and determine at least one clustered object set; or alternatively
The processing module is specifically configured to cluster the at least two keywords based on the enterprise name, and determine at least one clustered object set.
In another possible implementation manner, the processing module is further configured to determine a risk level of the risk analysis result;
The output module is further configured to push the risk analysis result to the risk processing device when the risk level is greater than a preset risk level.
In yet another possible implementation manner, the obtaining module is further configured to obtain a plurality of pieces of historical problem information;
The processing module is further configured to:
processing the plurality of pieces of historical problem information to obtain a training sample set of the risk early warning model, wherein the training sample set comprises: a plurality of pieces of history marking problem information having risk marking objects and/or risk marking contents;
and training a preset network model by using the training sample set to obtain the risk early warning model.
Optionally, the processing module is configured to train a preset network model by using the training sample set to obtain the risk early-warning model, and specifically includes:
The processing module is specifically configured to:
word segmentation processing is respectively carried out on each piece of history marking problem information in the training sample set, so that a word set corresponding to each piece of history marking problem information is obtained;
Sequentially inputting a word set corresponding to each piece of history marking problem information into the preset network model, and adjusting parameters of the preset network model until the preset network model sequentially outputs a risk marking object corresponding to each piece of history marking problem information and risk marking contents of the risk marking object;
and determining the trained preset network model as the risk early warning model.
The present invention also provides an information processing apparatus including: a memory, a processor and an information processing program stored on the memory and executable on the processor, which when executed by the processor, performs the steps of the information processing method as claimed in any one of the preceding claims.
The present invention also provides a computer-readable storage medium having stored thereon an information processing program which, when executed by a processor, implements the steps of the information processing method according to any one of the preceding claims.
The present invention also provides a computer program product comprising: a computer program stored in a readable storage medium, from which it can be read by at least one processor of an information processing apparatus, the at least one processor executing the computer program causing the information processing apparatus to perform the steps of the information processing method of any one of the preceding claims.
According to the invention, a plurality of pieces of problem information of user complaints in a preset time period are input into a pre-trained risk early warning model for cluster analysis, so that a risk analysis result is obtained and output, wherein the risk analysis result comprises the following steps: at least one risk object and risk content of each risk object, wherein each risk object is one object of at least one complaint object in the plurality of problem information, and the risk content is at least one complaint point in the complaint content of the corresponding risk object. According to the technical scheme, the information processing equipment can timely discover risk objects and risk objects in the problem information, the timeliness of risk discovery is improved, and the problem that the property safety of a user is damaged is avoided.
Drawings
Fig. 1 is a schematic diagram of a network architecture according to the present invention;
Fig. 2 is a schematic flow chart of an embodiment of an information processing method according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of a second embodiment of an information processing method according to the embodiment of the present invention;
fig. 4 is a schematic flow chart of a third embodiment of an information processing method according to the embodiment of the present invention;
fig. 5 is a schematic flow chart of a fourth embodiment of an information processing method according to the present invention;
FIG. 6 is a schematic diagram of an embodiment of an information processing apparatus according to an embodiment of the present invention;
Fig. 7 is a schematic structural diagram of an information processing apparatus according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
With the rapid development of network technology, in order to guarantee the benefit of people, an industrial and commercial administrative authority can directly receive a great deal of problem information (worksheets: objects, events, time, related units) every day. The problem information is real-time feedback of the problems existing in the current market by the consumers, can timely and accurately reflect the change characteristics and rules of each industry, and determines the problems existing in each industry.
Optionally, industries related to the embodiment of the present application include different industries such as financial industry, electronic commerce industry, home industry, computer industry, traffic industry, agricultural material, etc., and the embodiment of the present application is not limited to specific industries, and is not described herein. The following explains problems existing in the processing process of the complaint problem information of the existing user by taking the financial industry as an example.
With the continuous expansion of the scale of the internet financial industry, the bandwidth demand is continuously increased, so that the healthy development of the internet financial industry is ensured for preventing the centralized burst of the systematic risk of the industry, and the financial supervision institutions are comprehensively arranged in various places, so that the financial supervision institutions conduct supervision and management on the financial industry according to the authorization of national laws and regulations.
In practical applications, as the user's rights-keeping awareness becomes stronger, when the user finds that some financial institutions may have some illegal operations in the financial industry, for example, a certain platform funds cannot be taken out, a certain company loan is out of specification, and user information is revealed, the user will typically send complaint letters to local letters and financial offices to complain about some illegal operations that exist in some financial institutions. Thus, the relevant financial authorities such as financial offices, interviews and the like receive a great deal of mass complaints and letters every day, reflecting the possible problem of non-compliance of various financial institutions and financial transaction platforms.
Because the current mode of handling the complaint letters of the masses is mostly to handle the received related letters by professionals, the main solution is to answer the related solutions of the complaint problems of the masses or to handle the public security departments for more serious events. However, the financial supervisory institutions only contact the public security department for serious events, and cannot estimate risk of illegal financial events possibly occurring in financial institutions with large potential risks, and sometimes only find the end after serious loss is caused to some users, so as to request the public security bureau to conduct case investigation, which may cause that risk problems of some illegal financial institutions and illegal financial transaction platforms cannot be found in time, and damage to property safety of users is caused.
Aiming at the technical problems, the application concept of the technical scheme of the application is as follows: with the rapid development of artificial intelligence technology, the technology can be deeply fused with financial business, and the financial innovation vitality and application potential are released. Specifically, the artificial intelligence enables models such as a neural network to learn historical problem information, and the trained models such as the neural network have the capability of identifying risk objects and risk contents in the problem information, so that in practical application, information processing equipment bearing the models such as the neural network can perform cluster analysis on a plurality of pieces of problem information in a preset time period, at least one risk object and risk contents of each risk object possibly existing in the plurality of pieces of problem information can be output, and further risk problem points of complaint objects such as certain illegal financial institutions and illegal financial transaction platforms can be found timely, and the problem that user property safety is damaged due to untimely risk finding is solved to a certain extent.
Based on the above inventive concept, in the information processing method provided by the embodiment of the present invention, a plurality of pieces of problem information of user complaints in a preset time period are input into a pre-trained risk early warning model for cluster analysis, so as to obtain and output a risk analysis result, where the risk analysis result includes: at least one risk object and risk content of each risk object, wherein each risk object is one object of at least one complaint object in the plurality of problem information, and the risk content is at least one complaint point in the complaint content of the corresponding risk object. According to the technical scheme, the information processing equipment can timely discover risk objects and risk objects in the problem information, the timeliness of risk discovery is improved, and the problem that the property safety of a user is damaged is avoided.
The information processing method provided by the embodiment of the invention can be applied to information processing equipment such as a server, a computer, an intelligent terminal and the like which can analyze problem information, and the scheme is not limited.
The problem information in the embodiment of the present invention may be problem information in various industries, which is not limited.
Optionally, fig. 1 is a schematic diagram of a network architecture provided in the present invention. As shown in fig. 1, the network architecture may include: the information processing apparatus 11, at least one user terminal (two user terminals are shown in fig. 1, user terminal 121 and user terminal 122, respectively), and at least one server (two servers are shown in fig. 1, server 131 and server 132, respectively).
In the network architecture shown in fig. 1, the main body for executing the information processing method provided by the present invention is an information processing apparatus 11, and the information processing apparatus 11 may acquire a plurality of pieces of problem information in a preset period from a user terminal or other apparatuses for providing problem information through the internet or a wired connection. In an actual scenario, the information processing device 11 may specifically be a server, a computer, an intelligent terminal, or other electronic devices that carry a risk early warning model.
In the financial industry in practical use, as an example, when a user finds that a certain financial institution or financial platform has a problem that funds cannot be taken out and/or loan is not in compliance with regulations, the user may send problem information directly to the information processing device 11 of a financial regulatory institution such as a financial office or a letter office through a user terminal (e.g., the user terminal 121 or the user terminal 122) to complain that the above financial institution or financial platform has a problem that funds cannot be taken out and/or loan is not in compliance with regulations.
It will be appreciated that the information processing apparatus 11 may receive problem information transmitted by a plurality of user terminals, and the present embodiment is not limited to a specific apparatus or the number of apparatuses for transmitting problem information, and may be determined according to actual requirements, and is not determined here.
As another example, the information processing apparatus 11 may further receive issue information sent by a server of a financial supervisory office such as another financial office or a letter office, that is, the information processing apparatus 11 may perform unified processing on issue information received by different servers, so as to determine at least one risk object to be consolidated and risk content of each risk object from a plurality of complaint objects designed by all issue information.
Optionally, the network architecture shown in fig. 1 may also include a display device 14, such as: the personal computer (Personal Computer, PC) shown in fig. 1, the display device 14 may be used to display the risk analysis results of the information processing device, for example, the finalized at least one risk object, the risk content of each risk object, and the like.
Further, the network architecture shown in fig. 1 may further include at least one law enforcement service device 15 (one law enforcement service device is shown in fig. 1), for example: the law enforcement service device 15 may receive at least one risk object and the risk content of each risk object transmitted from the information processing device 14, so that the relevant executive department can perform processing and the like on the risk content of each risk object for each risk object in time, as shown in fig. 1.
The data transmission between the above devices can be performed by wired communication or wireless communication, and the scheme is not limited.
The information processing apparatus is exemplified by several embodiments in the following by taking the scenario of the architecture shown in fig. 1 applied in the financial industry as an example. It is to be understood that the following embodiments may be combined with each other, and that the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 2 is a flowchart of an embodiment of an information processing method according to an embodiment of the present invention. In the present embodiment, the information processing apparatus 11 in fig. 1 will be described as an execution subject of the information processing method. As shown in fig. 2, the information processing method may include the steps of:
s201, acquiring a plurality of pieces of problem information of user complaints in a preset time period, wherein each piece of problem information comprises: at least one complaint object and complaint content for each complaint object.
In this embodiment, the preset time period may be a time period taking the current time as the end time, and the duration of the preset time period may be a time period customized by the user, for example, the duration of the preset time period may be 24 hours (i.e. daily), or may be one week, or other time periods, which is not limited and may be configured according to actual needs.
In practical applications, there are various ways in which the information processing apparatus obtains pieces of problem information of user complaints. For example, the information processing apparatus may receive problem information of user complaints from a plurality of user terminals, may acquire problem information of user complaints from other servers that receive problem information, and may acquire problem information of user complaints in other manners.
Specifically, in the financial industry, the problem information of user complaints mainly refers to the offensiveness information of complaints such as a financial institution, a financial platform, an amount enterprise, etc., for example, the invested funds cannot be taken out, loans are not in accordance with the regulations, complaint contents such as sensitive information of users are revealed, or other complaint contents, and the complaint contents are not specifically limited.
S202, inputting the plurality of pieces of problem information into a pre-trained risk early warning model for cluster analysis to obtain a risk analysis result, wherein the risk analysis result comprises the following steps: at least one risk object and risk content for each risk object.
It is understood that in this embodiment, the at least one risk object is an object of the at least one complaint object, and the risk content is a content of complaint contents corresponding to the risk object.
In practical application, the information processing device carries a pre-trained risk early warning model, which can perform risk analysis on input content. Specifically, when the information processing device obtains a plurality of pieces of problem information of user complaints, the plurality of pieces of problem information can be input into the running risk early warning model, at least one complaint object included in the plurality of pieces of problem information and complaint content of each complaint object are analyzed by using the risk early warning model, a risk object and risk content of the risk object are determined from a plurality of complaint objects reflected by all pieces of problem information, for example, funds of a finance company cannot be taken out, and a problem that a loan does not meet regulations exists on a platform B.
The specific implementation of this step may be referred to as description in the embodiment shown in fig. 3 below, and will not be described here.
S203, outputting a risk analysis result.
In this step, as an example, the information processing apparatus may have a display interface, so that the information processing apparatus may present the obtained risk analysis result on the display interface so that the processing person timely acquires the risk analysis result for the above-described pieces of problem information.
As another example, the information processing apparatus may further have a pronunciation apparatus, so that the information processing apparatus may report the risk analysis result obtained by the pronunciation apparatus, and also enable the processor to timely obtain the risk analysis result for the plurality of pieces of problem information.
According to the information processing method provided by the embodiment of the application, a plurality of pieces of problem information of user complaints in a preset time period are obtained, and each piece of problem information comprises: at least one complaint object and complaint content aiming at each complaint object, inputting the plurality of pieces of problem information into a pre-trained risk early warning model for cluster analysis, and obtaining and outputting a risk analysis result, wherein the risk analysis result comprises: at least one risk object and a risk content of each risk object, wherein the at least one risk object is an object in the at least one complaint object, and the risk content is a content in the complaint content of the corresponding risk object. According to the technical scheme, the pre-trained risk early warning model is utilized to conduct cluster analysis on the plurality of pieces of problem information in the preset time period, so that risk objects and risk objects in the problem information can be found timely, timeliness of risk finding is improved, and the problem that user property safety is damaged is avoided.
In one possible design of the embodiment of the present invention, the information processing method provided by the embodiment of the present invention may further include the following steps:
a1, determining at least one department to be pushed for processing risk analysis results.
A2, respectively processing the risk analysis results by using a risk result processing strategy aiming at each to-be-pushed processing department to obtain risk push contents aiming at each to-be-pushed processing department;
a3, pushing the risk push content aiming at each department to be pushed to risk processing equipment of the corresponding department to be pushed.
Specifically, the information processing device may be preset with a plurality of different risk processing departments and risk result processing policies corresponding to each risk processing department, for example, the risk processing departments may include: the risk law enforcement department, the monitoring management department and the like, the risk result processing strategy corresponding to the risk law enforcement department can be counting the number of questions of the risk object in the risk analysis result, dividing the number into the number of the risk object and the like so as to determine whether to give up punishment, integer and the like to the risk object, and the risk result processing strategy corresponding to the monitoring management department can be carrying out attention grade upgrading to the risk object in the risk analysis result and determining the risk grade of the risk analysis result and the like. The embodiment of the application does not limit the risk processing departments which can be preset in the information processing equipment and the risk result processing strategies corresponding to each risk processing department, and can be set according to actual requirements, and details are not repeated here.
Therefore, in this embodiment, after outputting the risk analysis result, the information processing apparatus may first determine at least one to-be-pushed processing section corresponding to the risk analysis result based on the risk object in the risk analysis result and the risk content of the risk object, then determine the risk result processing policy of each to-be-pushed processing section, and process the risk analysis result by using the risk result processing policy of each to-be-pushed processing section, to obtain the risk push content for each to-be-pushed processing section, and push the risk push content to the risk processing apparatus of the corresponding to-be-pushed processing section.
For example, for a risk law enforcement department, the information processing device determines the intractable degree, the illegal times and the like of the risk object, so that the risk law enforcement department adopts different processing strategies such as punishment, stop operation and regulation and the like based on the intractable degree, the illegal times and the like and sends the different processing strategies to the risk processing device of the risk law enforcement department; for the monitoring management department, the information processing device determines the attention degree and the like of the risk object so as to carry out the attention level upgrading and other strategies on the risk object in the risk analysis result, and sends the attention level upgrading and the like to the risk processing device of the monitoring management department.
In this embodiment, different risk result processing strategies are adopted to process risk analysis results for different departments to be pushed, so that risk pushing contents for each department to be pushed can be obtained and pushed out, and a reference basis is provided for law enforcement of different departments to be pushed.
In another possible design of the embodiment of the present invention, the information processing method provided by the embodiment of the present invention may further include the following steps:
B1, determining at least one industry associated with the risk analysis result according to at least one risk object included in the risk analysis result and the risk content of each risk object;
b2, determining the industry risk level of the risk analysis result in each industry according to the risk analysis result and a preset industry risk classification strategy corresponding to each industry in at least one industry;
And B3, pushing the risk analysis result and the industry risk level in each industry to risk processing equipment of a corresponding industry processing department.
For example, in this embodiment, the information processing device may perform classification processing on problem information of different industries, that is, the information processing device may be preset with preset industry risk classification policies corresponding to different industries, for example, an industry risk classification policy of a financial industry, an industry risk classification policy of an electronic commerce industry, an industry risk classification policy of a transportation industry, and the like, and may divide different industry risk classes for the same risk analysis result based on the industry risk classification policies of different industries.
For example, a certain platform is active in financial industry and is involved in internet industry, so after the information processing device outputs the risk analysis result, the information processing device can firstly determine at least one industry associated with the risk analysis result based on the risk object in the risk analysis result and the risk content of the risk object, then perform industry risk classification on the risk analysis result based on a preset industry risk classification strategy corresponding to each industry, determine the industry risk classification of the risk analysis result in each industry, and push the risk analysis result and the industry risk classification in each industry to the risk processing device of the corresponding industry processing department so as to facilitate the risk processing devices of different industry processing departments to process.
In the embodiment, for different industries associated with risk analysis results, the industry risk grade of the risk analysis results in each industry is determined by utilizing a preset industry risk grading strategy corresponding to each industry, and the risk grade is pushed to risk processing equipment of different industry processing departments, so that implementation conditions are provided for targeted processing of subsequent different industry processing departments.
Based on the foregoing embodiments, fig. 3 is a schematic flow chart of a second embodiment of an information processing method according to the present invention. As shown in fig. 3, S202 may be implemented by:
S301, extracting keywords from the plurality of pieces of problem information by using a risk early warning model, and determining at least two keywords in the plurality of pieces of problem information.
In this step, the risk early-warning model running on the information processing apparatus has a word segmentation function, specifically, the information processing apparatus may input each piece of problem information into the risk early-warning model, the risk early-warning model performs word segmentation processing on each piece of problem information first to obtain a word set corresponding to all pieces of problem information, and then extracts keywords for describing complaint objects, complaint contents, and the like from the word set, thereby obtaining at least two keywords in the plurality of pieces of problem information.
Alternatively, the keyword extraction may be performed by a supervised keyword extraction method, or an unsupervised keyword extraction method, which is not limited in this scheme. It will be appreciated that there are a plurality of supervised and unsupervised extraction algorithms, which may be selected according to the actual situation, and will not be described here.
As an example, the supervised keyword extraction method is to construct a vocabulary according to the marked risk object and the marked risk content in the training stage of the risk early warning model, and then in practical application, the risk early warning model may extract at least two keywords from the word set based on the vocabulary.
As another example, the unsupervised keyword extraction method is to sort the importance of each word in the plurality of question information by using a certain mechanism, so as to determine at least two keywords with relatively high importance.
For example, at least two keywords in the plurality of question information may have xx platforms, xx enterprises, funds, loans, etc., and the scheme is not limited to specific implementation of the keywords.
S302, clustering the at least two keywords based on a preset clustering rule, and determining at least one clustering object set.
In this embodiment, the information processing apparatus may train the risk early warning model using a preset clustering rule in the clustering analysis stage, so that, in an actual analysis scenario, the risk early warning model may cluster at least two keywords corresponding to the plurality of pieces of problem information using the preset clustering rule, and divide the at least two keywords into at least one cluster object set.
Illustratively, this S302 may be implemented in a number of different ways, depending on the preset clustering rules. As an example, the information processing apparatus may cluster the at least two keywords based on the platform type, and determine at least one clustered object set, where the determined clustered object set may include, for example, a P2P platform, a small micro-loan enterprise, and the like. As another example, the information processing device may cluster at least two keywords based on the business name, determine at least one set of clustered objects, and the determined set of clustered objects at this time may include xxxxP P company, xxxx internet financial services limited company, and the like, for example. The scheme does not limit the preset clustering rules, so that the specific implementation of the clustering object set is not limited.
In practical application, the clustering algorithm corresponding to the preset clustering rule mainly used may be Kernel K-means clustering (Kernel K-means Clustering) of the unsupervised algorithm, that is, K clustering object sets are output on the basis of at least two keywords as input by setting the number K of the clusters to be clustered, where K is an integer greater than or equal to 1.
S303, if the at least one clustered object set contains objects with the number of the objects being greater than or equal to a preset risk threshold, determining at least one risk object in the at least one clustered object set.
In this embodiment, the information processing apparatus may determine whether or not a risk object or risk content exists in the at least one clustered object set based on a certain determination rule. For example, the information processing device may determine when analyzing the risk analysis results based on a density-based clustering algorithm (e.g., density-based spatial clustering of applications with noise, DBSCAN) based on a noise application.
Specifically, the information processing device defines clusters as a maximum set of points with connected densities, determines complaint objects with the object densities higher than a preset risk threshold from each cluster object set, determines the complaint objects as risk objects, and determines risk content for each risk object based on the complaint content of the risk object.
In this embodiment, the preset risk threshold is a preset value or a certain order of magnitude, for example, when a certain company name or a certain class of enterprise or a certain event occurs in a certain amount or a certain order of magnitude, the preset risk threshold may be set as a high risk and risk early warning may be performed.
In practical application, this step can be implemented as follows: firstly judging whether the at least one clustered object set contains objects with the number greater than or equal to the preset risk threshold, if not, determining that the at least one complaint object mentioned in the plurality of pieces of problem information does not contain a high risk object, and if not, determining that the complaint content of each complaint object does not contain a high risk content, namely if the at least one clustered object set contains objects with the number greater than or equal to the preset risk threshold, determining at least one risk object in the at least one clustered object set.
S304, screening out at least one piece of problem information aiming at each risk object from the plurality of pieces of problem information.
S305, carrying out content analysis on at least one piece of problem information of each risk object, and determining the risk content of each risk object.
Specifically, if it is determined that the number of objects in a cluster object set of a certain financial institution or a certain financial company, a certain financial event and the like is greater than or equal to a preset risk threshold, it is determined that attention needs to be paid, at least one risk object is determined from at least one complaint object corresponding to a plurality of pieces of problem information, and then at least one piece of problem information for each risk object is determined in a plurality of pieces of problem information, so that specific potential risk points can be analyzed for complaint contents in at least one piece of problem information corresponding to each risk object, for example, a certain platform fund cannot be taken out, and a certain company loan does not accord with regulations and the like.
According to the information processing method provided by the embodiment of the application, firstly, keyword extraction is carried out on a plurality of pieces of problem information by using a risk early warning model, at least two keywords in the plurality of pieces of problem information are determined, secondly, clustering is carried out on the at least two keywords based on a preset clustering rule, at least one clustered object set is determined, at least one risk object in the at least one clustered object set is determined if objects with the number of the objects being greater than or equal to a preset risk threshold value exist in the at least one clustered object set, at least one piece of problem information aiming at each risk object is screened out from the plurality of pieces of problem information, finally, content analysis is carried out on the at least one piece of problem information of each risk object, and the risk content of each risk object is determined. According to the technical scheme, the risk early warning model trained by the artificial intelligence technology is used for extracting keywords, carrying out semantic understanding and carrying out cluster analysis on all problem information of user complaints, so that risk analysis results can be obtained in time, and damage to property safety of users can be effectively avoided.
On the basis of the above embodiment, fig. 4 is a schematic flow chart of a third embodiment of an information processing method according to the embodiment of the present invention. As shown in fig. 4, the information processing method may further include the steps of:
S401, determining the risk level of the risk analysis result.
As an example, the information processing device may further analyze the risk analysis result obtained by the risk early warning model directly to determine a risk level of the risk analysis result. For example, a risk level set is preset in the information processing device, and the information processing device may rank the risk analysis result according to the risk objects involved in the risk analysis result and the risk content of each risk object or the historical problem information of the risk object, so as to determine the risk level of the risk analysis result.
As another example, the information processing apparatus may output a risk analysis result, for example, after the risk analysis result is presented through a human-computer interaction interface of the information processing apparatus, a processor who handles user complaints may check the accuracy of the risk analysis result and analyze the risk level of the risk analysis result.
And S402, pushing the risk analysis result to risk processing equipment when the risk level is greater than a preset risk level.
For example, the information processing device may be preset with a preset risk level for pushing the risk analysis result, that is, when the risk level is determined to be greater than the preset risk level, the risk analysis result is directly pushed to the risk processing device, so that relevant personnel of a risk processing department to which the risk processing device belongs can process in time.
As an example, when the processor determines that a certain risk object exists and the risk content of the risk object belongs to high risk based on the risk analysis result, that is, the risk level of the risk analysis result is greater than a preset risk level, the processor may send a risk push instruction through a man-machine interaction interface of the information processing device, so that the information processing device early warns related departments in advance. In this step, the information processing device may send the risk analysis result to the risk processing device to which the risk processing department belongs after receiving the risk push instruction.
Correspondingly, relevant law enforcement personnel of a risk processing department to which the risk processing equipment belongs can timely acquire a risk object belonging to high risk and risk content existing in the risk object, which are complained by a user, so that serious illegal problems can be timely processed, and serious financial loss or explosion events can be avoided.
According to the information processing method provided by the embodiment of the invention, the risk level of the risk analysis result is determined, and when the risk level is greater than the preset risk level, the risk analysis result is pushed to the risk processing equipment. According to the technical scheme, the information processing equipment can timely push the risk analysis result to the risk processing equipment of the risk processing department, so that related executive personnel can timely process risk objects and risk contents with higher risk, the possibility of user property safety loss is effectively reduced, and the safety of user property is improved.
On the basis of the above-described embodiment, the information processing apparatus is capable of executing the method of the embodiment shown in fig. 2 to 4 described above, on the basis of which the risk early-warning model is run, and thus, the following explanation is given of the training process of the risk early-warning model.
Fig. 5 is a flowchart of a fourth embodiment of an information processing method according to an embodiment of the present invention. As shown in fig. 5, the information processing method may further include the steps of:
s501, acquiring a plurality of pieces of history problem information.
S502, processing the plurality of pieces of history problem information to obtain a training sample set of the risk early warning model, wherein the training sample set comprises: a plurality of pieces of history marking problem information having risk marking objects and/or risk marking contents.
In this step, in the training stage of the risk early warning model, the information processing apparatus needs to acquire a plurality of pieces of history marking problem information of user complaints, so that the information processing apparatus can train the preset network model based on the history marking problem information.
Specifically, the information processing device may first obtain a plurality of pieces of history problem information including at least one complaint object and complaint content for the at least one complaint object, and then mark the plurality of pieces of history problem information in a manual marking manner or a machine marking manner to obtain a plurality of pieces of history marking problem information having risk marking objects and/or risk marking content, thereby obtaining a training sample set of a risk early warning model.
S503, training a preset network model by using the training sample set to obtain a risk early warning model.
Optionally, the information processing device may train the preset network model by using the obtained training sample set, so that the input of the trained preset network model is the history identification problem information, and the input is the risk marking object and/or the risk marking content in the corresponding history marking problem information.
As an example, this S503 may be specifically implemented by the following steps: firstly, word segmentation processing is respectively carried out on each piece of history marking problem information in the training sample set to obtain word sets corresponding to each piece of history marking problem information, then the word sets corresponding to each piece of history marking problem information are sequentially input into a preset network model, parameters of the preset network model are adjusted until the preset network model sequentially outputs risk marking objects corresponding to each piece of history marking problem information and risk marking contents of the risk marking objects, and finally the trained preset network model is determined to be a risk early warning model.
Specifically, in the process of training the risk early warning model by using the training sample set and the preset network model, the information processing device can obtain a word set corresponding to each piece of history marking problem information by performing word segmentation processing on each piece of history marking problem information in the training sample set, and then gradually adjust parameters of the preset network model according to the corresponding relation between input and output of the preset network model, so that the output of the preset network model is marking information (namely a risk marking object and risk marking content of the risk marking object) in the history marking problem information.
In one possible design of the present invention, the information processing device may not be a training device of the risk early-warning model, but may receive a modeling parameter of the risk early-warning model sent by the training device of the risk early-warning model, and may further construct the risk early-warning model based on the modeling parameter.
According to the information processing method provided by the embodiment of the invention, the acquired pieces of historical problem information are processed to obtain the training sample set of the risk early warning model, and the training sample set comprises the following components: and training the preset network model by using the training sample set to obtain a risk early warning model. According to the technical scheme, the risk early warning model is firstly trained by using the artificial intelligence technology, and the risk analysis of the complaint problem information of the user is realized by using the trained risk early warning model, so that the processing efficiency and the processing accuracy are improved.
In summary, when the technical scheme provided by the embodiment of the invention is applied to the complaint and visit platform in the financial industry, besides the complaint problems of the user, all financial problems of the user complaint are clustered and analyzed, and early warning high-risk financial events are pushed to related departments in advance according to the risk analysis result, so that the related departments can take measures in time, thereby avoiding huge losses to masses and society, and improving the functions of the complaint and visit platform.
Fig. 6 is a schematic structural diagram of an embodiment of an information processing apparatus according to an embodiment of the present invention. As shown in fig. 6, the information processing apparatus may include:
The obtaining module 601 is configured to obtain a plurality of pieces of problem information of user complaints in a preset period of time, where each piece of problem information includes: at least one complaint object and complaint content for each complaint object;
The processing module 602 is configured to input the plurality of pieces of problem information into a pre-trained risk early warning model for cluster analysis, and obtain a risk analysis result, where the risk analysis result includes: at least one risk object that is an object of the at least one complaint object and a risk content of each risk object that is a content of the complaint content of the corresponding risk object;
and the output module 603 is configured to output the risk analysis result.
In one possible implementation, the processing module 602 is further configured to:
determining at least one to-be-pushed processing department of the risk analysis result;
Processing the risk analysis results by using a risk result processing strategy aiming at each to-be-pushed processing department to obtain risk push content aiming at each to-be-pushed processing department;
The output module 603 is further configured to push risk push content for each to-be-pushed processing department to a risk processing device of the corresponding to-be-pushed processing department.
In another possible implementation manner, the processing module 602 is further configured to:
Determining at least one industry associated with the risk analysis result according to at least one risk object included in the risk analysis result and the risk content of each risk object;
determining an industry risk level of the risk analysis result in each industry according to the risk analysis result and a preset industry risk level strategy corresponding to each industry in the at least one industry;
The output module 603 is further configured to push the risk analysis result and the industry risk level in each industry to a risk processing device of a corresponding industry processing department.
In yet another possible implementation manner, the processing module 602 is specifically configured to:
Extracting keywords from the plurality of pieces of problem information by using the risk early warning model, and determining at least two keywords in the plurality of pieces of problem information;
clustering the at least two keywords based on a preset clustering rule, and determining at least one clustering object set;
If the at least one clustered object set contains objects with the number of the objects being greater than or equal to the preset risk threshold, determining at least one risk object in the at least one clustered object set;
Screening at least one piece of problem information aiming at each risk object from the plurality of pieces of problem information;
And carrying out content analysis on the at least one piece of problem information of each risk object to determine the risk content of each risk object.
Optionally, the processing module 602 is configured to cluster the at least two keywords based on a preset clustering rule, and determine at least one clustered object set, specifically:
the processing module 602 is specifically configured to cluster the at least two keywords based on a platform type, and determine at least one clustered object set; or alternatively
The processing module 602 is specifically configured to cluster the at least two keywords based on the name of the enterprise, and determine at least one clustered object set.
In another possible implementation manner, the processing module 602 is further configured to determine a risk level of the risk analysis result;
The output module 603 is further configured to push the risk analysis result to a risk processing device when the risk level is greater than a preset risk level.
In yet another possible implementation manner, the obtaining module 601 is further configured to obtain a plurality of pieces of historical problem information;
The processing module 602 is further configured to:
processing the plurality of pieces of historical problem information to obtain a training sample set of the risk early warning model, wherein the training sample set comprises: a plurality of pieces of history marking problem information having risk marking objects and/or risk marking contents;
and training a preset network model by using the training sample set to obtain the risk early warning model.
Optionally, the processing module 602 is configured to train a preset network model by using the training sample set to obtain the risk early-warning model, which specifically is:
The processing module 602 is specifically configured to:
word segmentation processing is respectively carried out on each piece of history marking problem information in the training sample set, so that a word set corresponding to each piece of history marking problem information is obtained;
Sequentially inputting a word set corresponding to each piece of history marking problem information into the preset network model, and adjusting parameters of the preset network model until the preset network model sequentially outputs a risk marking object corresponding to each piece of history marking problem information and risk marking contents of the risk marking object;
and determining the trained preset network model as the risk early warning model.
The information processing apparatus provided in this embodiment may be used to execute the technical solution provided in any of the foregoing method embodiments, and its implementation principle and technical effects are similar, and are not repeated here.
Fig. 7 is a schematic structural diagram of an information processing apparatus according to an embodiment of the present invention. The information processing apparatus may be a computer apparatus or an electronic apparatus, and the present embodiment is not limited thereto. As shown in fig. 7, the information processing apparatus may include: the information processing apparatus includes a memory 701, a processor 702, and an information processing program stored on the memory 701 and executable on the processor 702, which when executed by the processor 702, implements the steps of the information processing method according to any of the foregoing embodiments.
Alternatively, the memory 701 may be separate or integrated with the processor 702.
Further, the information processing apparatus may further include a communication interface 703 and a system bus 704, where the memory 701 and the communication interface 703 are connected to the processor 702 through the system bus 704 and perform communication with each other, and the communication interface 703 is used to communicate with other apparatuses.
Optionally, in an embodiment of the present application, the information processing apparatus may further include a man-machine interaction interface 705, where the man-machine interaction interface 705 may be configured to receive an instruction from a user and display a processing result.
The implementation principle and technical effects of the information processing apparatus provided in this embodiment may be referred to the foregoing embodiments, and will not be described herein again.
Embodiments of the present invention also provide a computer-readable storage medium having stored thereon an information processing program which, when executed by a processor, implements the steps of the information processing method according to any one of the preceding claims.
Optionally, the embodiment of the present invention further provides a chip for executing the instruction, where the chip is configured to execute the technical solution described in the foregoing method embodiment.
The embodiment of the invention also provides a program product, which comprises a computer program, the computer program is stored in a computer readable storage medium, at least one processor of the information processing device can read the computer program from the computer readable storage medium, and the at least one processor can enable the information processing device to realize the technical scheme of the embodiment of the method when executing the computer program.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules may be combined or integrated into another system, or some features may be omitted or not performed.
The integrated modules, which are implemented in the form of software functional modules, may be stored in a computer readable storage medium. The software functional modules described above are stored in a storage medium and include instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or processor to perform some of the steps of the methods described in the various embodiments of the invention.
It should be appreciated that the Processor may be a central processing unit (Central Processing Unit, abbreviated as CPU), or may be other general purpose Processor, digital signal Processor (DIGITAL SIGNAL Processor, abbreviated as DSP), application SPECIFIC INTEGRATED Circuit (ASIC), or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in a processor for execution.
The memory may comprise a high-speed RAM memory, and may further comprise a non-volatile memory NVM, such as at least one magnetic disk memory, and may also be a U-disk, a removable hard disk, a read-only memory, a magnetic disk or optical disk, etc.
The storage medium may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an Application SPECIFIC INTEGRATED Circuits (ASIC). It is also possible that the processor and the storage medium reside as discrete components in an electronic device or a master device.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
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 invention 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 invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.

Claims (10)

1. An information processing method, characterized by comprising:
Acquiring a plurality of pieces of problem information of user complaints in a preset time period, wherein each piece of problem information comprises: at least one complaint object and complaint content for each complaint object;
performing word segmentation processing on the plurality of pieces of problem information by using a risk early warning model to obtain word sets corresponding to all pieces of problem information;
Extracting at least two keywords from the word set based on a word list; or sorting the importance of each word in the word set according to a preset mechanism, and determining at least two keywords with high importance; the vocabulary is constructed according to marked risk objects and marked risk contents in a training stage of a risk early warning model; the keywords are words for describing complaint objects and complaint contents;
clustering the at least two keywords based on a preset clustering rule, and determining at least one clustering object set;
If the at least one clustered object set contains objects with the number of the objects being greater than or equal to the preset risk threshold, determining at least one risk object in the at least one clustered object set; the risk object is an object in a complaint object;
Screening at least one piece of problem information aiming at each risk object from the plurality of pieces of problem information;
Content analysis is carried out on the at least one piece of problem information of each risk object, and risk content of each risk object is determined; the risk content is content in complaint content corresponding to the risk object;
Outputting a risk analysis result; the risk analysis results include at least one risk object and risk content for each risk object.
2. The method according to claim 1, wherein the method further comprises:
determining at least one to-be-pushed processing department of the risk analysis result;
Processing the risk analysis results by using a risk result processing strategy aiming at each to-be-pushed processing department to obtain risk push content aiming at each to-be-pushed processing department;
and pushing the risk push content aiming at each to-be-pushed processing department to the risk processing equipment of the corresponding to-be-pushed processing department.
3. The method according to claim 1, wherein the method further comprises:
Determining at least one industry associated with the risk analysis result according to at least one risk object included in the risk analysis result and the risk content of each risk object;
determining an industry risk level of the risk analysis result in each industry according to the risk analysis result and a preset industry risk level strategy corresponding to each industry in the at least one industry;
pushing the risk analysis result and the industry risk level in each industry to risk processing equipment of a corresponding industry processing department.
4. The method of claim 1, wherein the clustering the at least two keywords based on a preset clustering rule, determining at least one set of clustered objects, comprises:
clustering the at least two keywords based on the platform type, and determining at least one clustering object set; or alternatively
And clustering the at least two keywords based on the enterprise name, and determining at least one clustering object set.
5. The method according to claim 1, wherein the method further comprises:
determining a risk level of the risk analysis result;
And when the risk level is greater than a preset risk level, pushing the risk analysis result to risk processing equipment.
6. The method according to any one of claims 1-5, further comprising:
acquiring a plurality of pieces of historical problem information;
processing the plurality of pieces of historical problem information to obtain a training sample set of the risk early warning model, wherein the training sample set comprises: a plurality of pieces of history marking problem information having risk marking objects and/or risk marking contents;
and training a preset network model by using the training sample set to obtain the risk early warning model.
7. An information processing apparatus, characterized by comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a plurality of pieces of problem information of user complaints in a preset time period, and each piece of problem information comprises: at least one complaint object and complaint content for each complaint object;
The processing module is used for carrying out word segmentation processing on the plurality of pieces of problem information by using the risk early warning model to obtain word sets corresponding to all pieces of problem information; extracting at least two keywords from the word set based on a word list; or sorting the importance of each word in the word set according to a preset mechanism, and determining at least two keywords with high importance; the vocabulary is constructed according to marked risk objects and marked risk contents in a training stage of a risk early warning model; the keywords are words for describing complaint objects and complaint contents; clustering the at least two keywords based on a preset clustering rule, and determining at least one clustering object set; if the at least one clustered object set contains objects with the number of the objects being greater than or equal to the preset risk threshold, determining at least one risk object in the at least one clustered object set; the risk object is an object in a complaint object; screening at least one piece of problem information aiming at each risk object from the plurality of pieces of problem information; content analysis is carried out on the at least one piece of problem information of each risk object, and risk content of each risk object is determined; the risk content is content in complaint content corresponding to the risk object;
the output module is used for outputting risk analysis results; the risk analysis results include at least one risk object and risk content for each risk object.
8. An information processing apparatus, characterized in that the information processing apparatus comprises: memory, a processor and an information processing program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the information processing method according to any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon an information processing program which, when executed by a processor, realizes the steps of the information processing method according to any one of claims 1 to 6.
10. A computer program product comprising: computer program, characterized in that it realizes the steps of the information processing method according to any one of claims 1 to 6 when being executed by a processor.
CN202011639532.1A 2020-12-31 2020-12-31 Information processing method, device, equipment and storage medium Active CN112712270B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011639532.1A CN112712270B (en) 2020-12-31 2020-12-31 Information processing method, device, equipment and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011639532.1A CN112712270B (en) 2020-12-31 2020-12-31 Information processing method, device, equipment and storage medium

Publications (2)

Publication Number Publication Date
CN112712270A CN112712270A (en) 2021-04-27
CN112712270B true CN112712270B (en) 2024-04-26

Family

ID=75547964

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011639532.1A Active CN112712270B (en) 2020-12-31 2020-12-31 Information processing method, device, equipment and storage medium

Country Status (1)

Country Link
CN (1) CN112712270B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113592315A (en) * 2021-08-04 2021-11-02 北京沃东天骏信息技术有限公司 Method and device for processing dispute order

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110008394A (en) * 2019-01-22 2019-07-12 阿里巴巴集团控股有限公司 A kind of recognition methods of public feelings information, device and equipment
WO2020037942A1 (en) * 2018-08-20 2020-02-27 平安科技（深圳）有限公司 Risk prediction processing method and apparatus, computer device and medium
CN111259057A (en) * 2020-01-15 2020-06-09 浙江连信科技有限公司 Data processing method and device for civil appeal analysis
CN111353792A (en) * 2020-05-25 2020-06-30 广东电网有限责任公司惠州供电局 Client portrait system with visual display and data analysis functions

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190244148A1 (en) * 2018-02-08 2019-08-08 GeminiRE Computer architecture for characterizing and managing risk

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020037942A1 (en) * 2018-08-20 2020-02-27 平安科技（深圳）有限公司 Risk prediction processing method and apparatus, computer device and medium
CN110008394A (en) * 2019-01-22 2019-07-12 阿里巴巴集团控股有限公司 A kind of recognition methods of public feelings information, device and equipment
CN111259057A (en) * 2020-01-15 2020-06-09 浙江连信科技有限公司 Data processing method and device for civil appeal analysis
CN111353792A (en) * 2020-05-25 2020-06-30 广东电网有限责任公司惠州供电局 Client portrait system with visual display and data analysis functions

Also Published As

Publication number Publication date
CN112712270A (en) 2021-04-27

Similar Documents

Publication Publication Date Title
CN107945024B (en) Method for identifying internet financial loan enterprise operation abnormity, terminal equipment and storage medium
CN107025509B (en) Decision making system and method based on business model
CN108366045B (en) Method and device for setting wind control scoring card
CN111383101B (en) Post-credit risk monitoring method, post-credit risk monitoring device, post-credit risk monitoring equipment and computer readable storage medium
Vajgel et al. Development of intelligent robotic process automation: A utility case study in Brazil
CN114265967B (en) Sensitive data security level marking method and device
CN112668859A (en) Big data based customer risk rating method, device, equipment and storage medium
CN112598513B (en) Method and device for identifying stockholder risk transaction behaviors
CN110955770A (en) Intelligent dialogue system
CN111861716B (en) Method for generating monitoring early warning level in credit based on software system
CN111199469A (en) User payment model generation method and device and electronic equipment
CN113609193A (en) Method and device for training prediction model for predicting customer transaction behavior
CN112990281A (en) Abnormal bid identification model training method, abnormal bid identification method and abnormal bid identification device
CN112950359B (en) User identification method and device
CN112712270B (en) Information processing method, device, equipment and storage medium
CN110796539A (en) Credit investigation evaluation method and device
CN111353728A (en) Risk analysis method and system
Aly et al. Machine Learning Algorithms and Auditor’s Assessments of the Risks Material Misstatement: Evidence from the Restatement of Listed London Companies
CN114493853A (en) Credit rating evaluation method, credit rating evaluation device, electronic device and storage medium
CN115545088A (en) Model construction method, classification method and device and electronic equipment
CN114092230A (en) Data processing method and device, electronic equipment and computer readable medium
CN114443409A (en) Payment business system monitoring method, device and equipment and computer storage medium
CN112561699A (en) Method, system and storage medium for processing dealer client data
CN112308294A (en) Default probability prediction method and device
CN112598225A (en) Evaluation index determination method and apparatus, storage medium, and electronic apparatus

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant