CN116128287A

CN116128287A - Grade risk prevention and control method and device, equipment, medium and product thereof

Info

Publication number: CN116128287A
Application number: CN202211678997.7A
Authority: CN
Inventors: 吴冰娜; 吴文龙
Original assignee: Shenzhen Zhiyan Technology Co Ltd; Shenzhen Qianyan Technology Co Ltd
Current assignee: Shenzhen Zhiyan Technology Co Ltd; Shenzhen Qianyan Technology Co Ltd
Priority date: 2022-12-26
Filing date: 2022-12-26
Publication date: 2023-05-16

Abstract

The application relates to a grade risk prevention and control method and a device, equipment, medium and product thereof, wherein the method comprises the following steps: acquiring condition description data obtained by monitoring the operation risk condition of a target commodity item by a plurality of wind control modules corresponding to different risk prevention and control functions; generating a visual risk notification chart of the target commodity item according to the condition description data; determining a comprehensive score of the target commodity item according to the condition description data; and constructing an alarm page containing the comprehensive score and risk notification chart and pushing the alarm page to a target user. According to the method and the system, the management risk of the commodity item is monitored in a grade mode technically, meanwhile, effective information interaction can be achieved, the commodity item management risk in a plurality of independent stations distributed and deployed by an e-commerce merchant can be monitored in a centralized and unified mode, the information processing efficiency of the merchant is improved, and the user experience of a related information management system is optimized.

Description

Grade risk prevention and control method and device, equipment, medium and product thereof

Technical Field

The application relates to the field of electronic commerce information processing, in particular to a grade risk prevention and control method and a device, equipment, medium and product thereof.

Background

Merchants of cross-border electronic commerce generally deploy online stores in a plurality of electronic commerce platforms at the same time, and conduct commodity management through the multiple platforms and the multiple stores, so that very complex cross management is involved, but the merchants themselves need to realize marketing and management condition monitoring of the commodity which is distributed and deployed, and the related technology lacks attention to the field at present.

The common risk prevention and control means in the e-commerce platform mainly pay attention to network security risks at the platform level or at the shop level, namely at the independent station level, or information security risks are dominant, single or single commodity level cannot be generally involved, and the rough technical means are obviously not applicable to the requirements of the scenes.

In practice, risk prevention and control of a commodity item needs to consider multiple aspects, such as the operating condition of a competitor, the performance of the commodity item in user access behaviors, and problems reflected by various changes such as logistics warehouse management, etc., and the like, which are risk information that needs to be focused on by a single commodity item, and it can be seen that, although the risk of the commodity item is focused on a specific grade, the specific scene involved and the complexity of the information involved are self-evident.

In view of this, technologies related to prevention and control of risks of commodity items in a grade level need to be further explored to improve the grade information security function of the e-commerce platform.

Disclosure of Invention

It is an object of the present application to solve the above problems and to provide a method for controlling a risk of a grade, and corresponding apparatus, device, non-volatile readable storage medium, and computer program product.

According to one aspect of the present application, there is provided a method for controlling a risk of a grade, comprising the steps of:

acquiring condition description data obtained by monitoring the operation risk condition of a target commodity item by a plurality of wind control modules corresponding to different risk prevention and control functions;

generating a visual risk notification chart of the target commodity item according to the condition description data;

determining a comprehensive score of the target commodity item according to the condition description data;

and constructing an alarm page containing the comprehensive score and risk notification chart and pushing the alarm page to a target user.

Optionally, acquiring condition description data obtained by monitoring the operation risk condition of the target commodity item by a plurality of wind control modules corresponding to different risk prevention and control functions includes:

for a first risk prevention and control function, periodically acquiring first sales volume data of a commodity item similar to a commodity title of a target commodity item in an external website through a first wind control module, judging whether the sales volume data exceeds second sales volume data of the target commodity item, and generating corresponding first condition description data when the sales volume data exceeds the second sales volume data, wherein the first condition description data comprises a difference value between the first sales volume data and the second sales volume data;

For a second risk prevention and control function, periodically acquiring index change data of a target commodity item through a second wind control module, and generating second condition description data when the index change data exceeds a preset threshold value, wherein the second condition description data comprises the index change data;

and aiming at a third risk prevention and control function, periodically counting the failure times of failure transaction events of the target commodity item through a third wind control module, and generating third condition description data which contains the failure times when the failure times exceed a preset threshold value.

Optionally, determining the comprehensive score of the target commodity item according to the condition description data includes:

constructing any plurality of the condition description data into a condition description vector;

inputting the condition description vector into a preset score prediction model to predict a comprehensive score corresponding to the condition description vector;

the composite score is converted to a format-specific representation.

Optionally, constructing an alert page including the comprehensive score and risk notification chart to be pushed to the target user includes:

judging the alarm grade corresponding to the comprehensive score;

corresponding to the alarm grade, calling a corresponding page style, and constructing the comprehensive score and the risk notification chart into an alarm page of the corresponding page style;

Pushing the alarm page to a target user corresponding to the alarm level.

Optionally, after the alert page including the comprehensive score and risk notification chart is constructed and pushed to the target user, the method includes:

constructing a plurality of target commodity items into an alarm list, wherein the alarm list comprises commodity characteristic identifiers, commodity titles, commodity pictures and the comprehensive scores of the target commodity items;

sequencing all target commodity items in the alarm list according to the comprehensive scores;

and pushing the alarm list to a corresponding target user.

Optionally, before acquiring the condition description data obtained by monitoring the operation risk condition of the target commodity item by the plurality of wind control modules corresponding to different risk prevention and control functions, the method includes:

acquiring wind control configuration information, wherein the wind control configuration information comprises target commodity items, designated wind control module identifiers and mapping relation data formed by corresponding target users;

and sending the wind control configuration information to a wind control module corresponding to the designated wind control module identifier, and registering the corresponding target commodity item into a monitoring list by the corresponding wind control module.

Optionally, before the alert page including the comprehensive score and risk notification chart is constructed and pushed to the target user, the method includes:

Combining and encoding all the condition description data and commodity information of the target commodity item to which the condition description data belongs to obtain combined encoding information;

inputting the joint coding information into a preset risk classification model, and predicting a risk type corresponding to the joint coding information;

and labeling the target commodity item by the risk type so as to be pushed to a corresponding target user along with the alarm page.

According to another aspect of the present application, there is provided a grade risk prevention and control device comprising:

the data acquisition module is used for acquiring situation description data obtained by monitoring the operation risk situation of the target commodity item by a plurality of wind control modules corresponding to different risk prevention and control functions;

a chart generation module configured to generate a risk notification chart for the target commodity item visualization according to each of the condition description data;

the score determining module is used for determining the comprehensive score of the target commodity item according to the condition description data;

and the result pushing module is used for pushing the alarm page containing the comprehensive score and risk notification chart to the target user.

According to another aspect of the present application, there is provided a grade risk prevention and control device comprising a central processor and a memory, the central processor being adapted to invoke the steps of executing a computer program stored in the memory to perform the grade risk prevention and control method described herein.

According to another aspect of the present application, there is provided a non-transitory readable storage medium storing in the form of computer readable instructions a computer program implemented in accordance with the method for controlling a risk of a grade, the computer program, when executed by a computer, performing the steps comprised by the method.

According to another aspect of the present application, there is provided a computer program product comprising computer programs/instructions which when executed by a processor implement the steps of the method as described in any of the embodiments of the present application.

Compared with the prior art, the method and the system have the advantages that risk prevention and control is sunk to specific commodity levels, complicated risk demands are divided into a plurality of special wind control modules to conduct monitoring, the management risk conditions of target commodity items are monitored through the wind control modules respectively, condition description data which are quantized after the wind control modules monitor the management risk conditions are obtained, the concentration of multiple aspects of risk information of the commodity items is achieved, then corresponding visual charts are further generated according to the risk information, corresponding comprehensive scores are determined, the visual charts are pushed to related target users, the management risk of the commodity items is monitored in a technical mode, meanwhile effective information interaction can be achieved, the fact that commodity items in multiple independent stations distributed and deployed by electronic commerce merchants can be monitored in a centralized mode is guaranteed, information processing efficiency of the merchants is improved, and user experience of related information management systems is optimized.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic architecture diagram of an exemplary network environment of the present application;

FIG. 2 is a flow chart of one embodiment of a method of risk prevention and control of the present application;

FIG. 3 is a flow chart of acquiring status description data by various wind control modules according to an embodiment of the present application;

FIG. 4 is a schematic diagram of an exemplary risk notification chart of the present application;

FIG. 5 is a schematic flow chart of determining a composite score using a classification prediction model in an embodiment of the present application;

FIG. 6 is a schematic flow chart of constructing an alarm page of a corresponding sample according to an alarm level to push to a corresponding target user in the embodiment of the present application;

FIG. 7 is a schematic flow chart of constructing and pushing an alarm list in an embodiment of the present application;

FIG. 8 is a schematic flow chart of labeling risk types of target commodity items according to an embodiment of the present application;

FIG. 9 is a schematic block diagram of a grade risk prevention and control device of the present application;

fig. 10 is a schematic structural diagram of a grade risk prevention and control device used in the present application.

Detailed Description

Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for the purpose of illustrating the present application and are not to be construed as limiting the present application.

As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. The term "and/or" as used herein includes all or any element and all combination of one or more of the associated listed items.

It will be understood by those skilled in the art that all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs unless defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

As used herein, "client," "terminal device," and "terminal device" are understood by those skilled in the art to include both devices that include only wireless signal receivers without transmitting capabilities and devices that include receiving and transmitting hardware capable of two-way communication over a two-way communication link. Such a device may include: a cellular or other communication device such as a personal computer, tablet, or the like, having a single-line display or a multi-line display or a cellular or other communication device without a multi-line display; a PCS (Personal Communications Service, personal communication system) that may combine voice, data processing, facsimile and/or data communication capabilities; a PDA (Personal Digital Assistant ) that can include a radio frequency receiver, pager, internet/intranet access, web browser, notepad, calendar and/or GPS (Global Positioning System ) receiver; a conventional laptop and/or palmtop computer or other appliance that has and/or includes a radio frequency receiver. As used herein, "client," "terminal device" may be portable, transportable, installed in a vehicle (aeronautical, maritime, and/or land-based), or adapted and/or configured to operate locally and/or in a distributed fashion, at any other location(s) on earth and/or in space. As used herein, a "client," "terminal device," or "terminal device" may also be a communication terminal, an internet terminal, or a music/video playing terminal, for example, a PDA, a MID (Mobile Internet Device ), and/or a mobile phone with music/video playing function, or may also be a device such as a smart tv, a set top box, or the like.

The hardware referred to by the names "server", "client", "service node" and the like in the present application is essentially an electronic device having the performance of a personal computer, and is a hardware device having necessary components disclosed by von neumann's principle, such as a central processing unit (including an arithmetic unit and a controller), a memory, an input device, and an output device, and a computer program is stored in the memory, and the central processing unit calls the program stored in the external memory to run in the memory, executes instructions in the program, and interacts with the input/output device, thereby completing a specific function.

It should be noted that the concept of "server" as referred to in this application is equally applicable to the case of a server farm. The servers should be logically partitioned, physically separate from each other but interface-callable, or integrated into a physical computer or group of computers, according to network deployment principles understood by those skilled in the art. Those skilled in the art will appreciate this variation and should not be construed as limiting the implementation of the network deployment approach of the present application.

One or several technical features of the present application, unless specified in the plain text, may be deployed either on a server to implement access by remotely invoking an online service interface provided by the acquisition server by a client, or directly deployed and run on the client to implement access.

The neural network model cited or possibly cited in the application can be deployed on a remote server and used for implementing remote call on a client, or can be deployed on a client with sufficient equipment capability for direct call unless specified in a clear text, and in some embodiments, when the neural network model runs on the client, the corresponding intelligence can be obtained through migration learning so as to reduce the requirement on the running resources of the hardware of the client and avoid excessively occupying the running resources of the hardware of the client.

The various data referred to in the present application, unless specified in the plain text, may be stored either remotely in a server or in a local terminal device, as long as it is suitable for being invoked by the technical solution of the present application.

Those skilled in the art will appreciate that: although the various methods of the present application are described based on the same concepts so as to be common to each other, the methods may be performed independently, unless otherwise indicated. Similarly, for each of the embodiments disclosed herein, the concepts presented are based on the same inventive concept, and thus, the concepts presented for the same description, and concepts that are merely convenient and appropriately altered although they are different, should be equally understood.

The various embodiments to be disclosed herein, unless the plain text indicates a mutually exclusive relationship with each other, the technical features related to the various embodiments may be cross-combined to flexibly construct a new embodiment, so long as such combination does not depart from the inventive spirit of the present application and can satisfy the needs in the art or solve the deficiencies in the prior art. This variant will be known to the person skilled in the art.

A method for controlling risk in a level of the present application may be programmed as a computer program product, deployed in a client or server for execution, for example, in an exemplary application scenario of the present application, may be deployed in a server of an e-commerce platform, whereby the method may be performed by accessing an interface of the computer program product that is open after execution, and performing man-machine interaction with a process of the computer program product through a graphical user interface.

Referring to fig. 1, an exemplary network architecture adopted in an application scenario of the present application includes a terminal device 80 and a service server 81, where the service server 81 may be used to deploy an information management system related to enterprise resource planning, and the information management system may be, for example, a dedicated software system used by a cross-border e-commerce enterprise, so as to implement centralized management of operation data of a plurality of online stores distributed and operated in a multi-home-commerce platform, each online store may have a plurality of language versions to serve different language areas, and a cross-border e-commerce business user may implement centralized control management in different aspects such as sales, logistics, warehousing, customer service, advertisement, and so on through the information management system.

The level risk prevention and control method of the present application may be programmed as a computer program product, constructing one of the functional plug-ins of the software system, and deployed together for operation in the service server 81. The terminal device 80 may be provided for use by operating users of different authorities to log into the information management system to receive, process, send various relevant information, such as obtaining an alert page of the present application, etc. Different authority operation users can obtain different levels of information, and the information can be realized through pre-configuration.

Based on the above principle description, please refer to fig. 2, according to one embodiment of the present disclosure, a method for controlling a grade risk includes the following steps:

step S1100, acquiring condition description data obtained by monitoring the operation risk condition of a target commodity item by a plurality of wind control modules corresponding to different risk prevention and control functions;

in the service server deployed with the information management system of the application, a plurality of wind control modules are deployed, and the wind control modules are respectively used for implementing different risk prevention and control functions so as to acquire condition description data corresponding to the management risk condition of the pre-designated commodity item.

The risk prevention and control function may be preset, for example, to prevent the risk of occurrence of a deficit by monitoring the variation of the market price of raw materials required for the target commodity item, to prevent the risk of occurrence of lower user satisfaction by monitoring the variation of user evaluation data of the target commodity item, to prevent the risk of suffering from market competition disadvantages by monitoring the variation of sales data of the bid for the target commodity item, and the like.

The commodity items can be massive, the same commodity item can exist in a plurality of online shops, and the information management system has authority to access partial bottom layer data of each online shop so as to generate condition description data corresponding to one or more management risk conditions according to the bottom layer data. For example, the condition description data corresponding to the drop risk under evaluation is generated according to star grade scoring data in the bottom data of the commodity item corresponding to the user access behavior. Since the online store and the information management system are usually owned by the same home, the authority of the risk module to acquire the underlying data from the online store may be preset in the information management system and/or the corresponding online store.

The information management system can directly reach commodity databases of all online shops through configuration account information, so that a corresponding wind control module can directly scan all commodity items in the commodity databases, take each commodity item as a risk monitoring object, namely a target commodity item, and analyze various bottom data of the target commodity item for a corresponding purpose, thereby generating situation description data representing corresponding management risk situations.

The operational risk status of the target commodity item is generally expressed in multiple aspects, and for different aspects, the underlying data may have multiple sources, and besides the existing in the online store of the merchant, some data of a third party may need to be relied on, for this aspect, the acquisition authority of the data of the third party may be preset, or related public data may also be acquired in other feasible manners, for example, sales data of the bid product of the target commodity item, public price data of raw materials required by the target commodity item, and the like, and may be specifically taken charge of by a corresponding risk module so as to generate corresponding status description data according to the data.

The method for generating the condition description data corresponding to the business risk condition of the target commodity item can be various, for example, some methods are used for directly executing data simple operation to obtain operation data, some methods are used for executing statistical processing to obtain corresponding statistical data, corresponding risk modules can be provided for different generation methods to be implemented in the same way, and when the corresponding condition description data is generated by implementing, each risk module can write related algorithms, formulas and the like into business logic in advance so as to be implemented when the business logic is operated.

As will be understood from the above description, the condition description data is obtained by the wind control module through obtaining various bottom data corresponding to the target commodity item, including, but not limited to, bottom data in an online store owned by a merchant, bottom data from other third parties, and the like, and on the basis of the bottom data, result data obtained by processing according to preset processing logic may be in different forms, such as a numerical value, a boolean value, a characteristic value, and the like, and the condition description data may be configured by the wind control module, so as to be used for describing the operational risk condition of the aspect corresponding to the risk prevention and control function responsible for the wind control module.

Therefore, a plurality of wind control modules can be arranged, each wind control module has a special risk prevention and control function, the management risk conditions of the target commodity items can be monitored cooperatively, and the bottom data corresponding to the corresponding management risk conditions are analyzed through each wind control module to generate condition description data representing the corresponding management risk conditions.

Referring to fig. 3, in an exemplary embodiment, the present step may be implemented according to the following procedure, including:

Step S1110, for a first risk prevention and control function, periodically acquiring first sales volume data of a commodity item similar to a commodity title of a target commodity item in an external website through a first wind control module, judging whether the sales volume data exceeds second sales volume data of the target commodity item, and generating corresponding first condition description data when the sales volume data exceeds the second sales volume data, wherein the first condition description data comprises a difference value between the first sales volume data and the second sales volume data;

the first risk prevention and control function is mainly used for finding potential or real risks of the target commodity item in market competition by monitoring sales situations of the bid of the target commodity item, and for this purpose, a first wind control module is used for correspondingly acquiring sales data of the bid on an external website. The sales data of the commodity is generally public, and thus, the sales data of the corresponding bid product can be obtained directly from the external website, which is referred to herein as first sales data. The bids of the target commodity items can be distributed in a plurality of external websites, and for this purpose, the acquisition addresses of the first sales data of the bids can be preset correspondingly, and the first sales data of the bids can be acquired and used periodically by the first wind control module.

The method for determining whether the commodity item in the external website belongs to the target commodity item can be directly preset by manpower, and the first pneumatic control module can also screen the access address of the commodity detail page of the bid corresponding to the target commodity item. Specifically, the first wind control module can obtain commodity information in commodity detail pages of all commodity items through accessing all commodity items of the external website, wherein the commodity information comprises commodity titles, commodity pictures, commodity prices and the like, and on the basis, whether the commodity information of the commodity items of the external website, in particular the semantic similarity between deep semantic features corresponding to the commodity titles and the deep semantic features of the target commodity items, reach a preset threshold value is judged, and when the preset threshold value is reached, the commodity items of the external website are determined to be the bid items of the target commodity items. Therefore, the first wind control module automatically identifies the bid of the target commodity item, and the bid is more efficient and accurate.

Aiming at the condition that a plurality of competitive products need to be monitored in a target commodity item, corresponding first sales data can be obtained corresponding to each competitive product, then whether the sales data of each competitive product exceeds second sales data of the target commodity item is judged one by one, when the first sales data of each competitive product exceeds the second sales data of the target commodity item or the exceeding amplitude reaches a minimum tolerance ratio, the corresponding competitive product can be confirmed to form market competition risks for the target commodity item, and therefore, a difference value can be obtained between the second sales data of the target commodity item and the first sales data of the corresponding competitive product, and the difference value is used as corresponding first condition description data; when there is a risk of multiple bid items forming, the first condition description data is allowed to contain difference data corresponding to the multiple bid items.

In one embodiment, the first condition description data may further include other detail data, such as a commodity title of the bid product, a commodity price, key commodity information such as first sales data, and key commodity information such as second sales data of the target commodity.

In yet another embodiment, to compare the sales data difference between different bids and target merchandise items on the same dimension, the sales data difference between the bids and target merchandise items may be normalized and adjusted to a numerical interval such as [0,100] for visually exhibiting the difference.

Step S1120, aiming at a second risk prevention and control function, periodically acquiring index variation data of a target commodity item through a second wind control module, and generating second condition description data when the index variation data exceeds a preset threshold value, wherein the second condition description data comprises the index variation data;

the second risk prevention and control function is mainly used for identifying potential or actual risks of sales or other operation conditions of the target commodity item by monitoring index change data corresponding to key indexes of the target commodity item. The key indicators may include, but are not limited to, user evaluation indicators, inventory balance indicators, advertising cost balance indicators, and the like, as indicators of sales potential for the targeted merchandise item.

For example, for the user evaluation index, a general electronic commerce platform can provide a user evaluation link for a commodity item, and a user who purchases the commodity item can represent subjective feeling degrees obtained by purchasing the commodity item by giving user evaluation data such as star grade or score, and the electronic commerce platform can further quantify the subjective feeling degrees to be obtained by purchasing the commodity item into uniformly represented star grade indexes or comprehensive scores according to the total user evaluation data of the commodity item. It will be appreciated that over time, as sales of items increase, the value of the key indicator will also change accordingly. According to the method, the corresponding key indexes can be periodically acquired corresponding to the target commodity item, then the key indexes acquired in the current period are compared with the key indexes acquired in the previous period, the difference between the key indexes is determined, and the index change data corresponding to the key indexes is acquired, wherein the index change data has the effect of measuring the experience risk of the target commodity item in the aspect of user evaluation. The method for acquiring the index variation data of different key indexes such as the inventory balance index, the advertisement expense balance index and the like is the same as the method for acquiring the index variation data of the user evaluation index, and is not repeated.

In order to identify whether the index variation data can trigger corresponding risk identification, a corresponding preset threshold value is utilized to be compared with the index variation data, when the index variation data is higher than the threshold value, the existence of corresponding risk can be confirmed, so that second condition description data is generated, the index variation data is contained in the second condition description data, and other detail data such as key commodity information of commodity titles, commodity prices, sales data and the like of target commodity items can be further contained according to requirements. The second condition description data of the same target commodity item may include index change data corresponding to a plurality of different key indexes as required, for example, on the basis of index change data corresponding to user evaluation, index change data corresponding to inventory balance, index change data corresponding to advertisement fee balance, and the like.

Similarly, the index variation data may be normalized to a specific numerical range, so as to unify different index variation data to the same dimension, so as to effectively compare the merits of the index variation data with each other.

Step S1130, for the third risk prevention and control function, periodically counting the failure times of the failure transaction events of the target commodity item through the third wind control module, and generating third condition description data when the failure times exceed a preset threshold value, wherein the third condition description data comprises the failure times.

The third risk prevention and control function is mainly used for identifying the management risk condition of the corresponding aspect by periodically counting certain events corresponding to the target commodity item. Therefore, the third wind control module provided by the corresponding third risk prevention and control function periodically counts the total number of events corresponding to the events to be counted on the basis of acquiring the user behavior data of the target commodity item, and judges whether the corresponding real or potential risk exists according to the total number of the events.

For example, an event in which a user adds a target commodity item to his shopping cart at a time but deletes the target commodity item from the shopping cart may be regarded as a failed transaction event, and similarly, an event in which the user places an order but does not settle may be regarded as a failed transaction event, and the more times the failed transaction event is, the more the number of times the failed transaction event is for a target commodity item, the higher the replaceability of the target commodity item is, so that it may be determined whether the target commodity item has a corresponding risk through the failed transaction event.

In one embodiment, for the exemplary failed transaction event, the total number of failed transaction events of the target commodity item is counted periodically, and the number of failed times is counted to measure the risk of transaction failure of the target commodity item. When counting the failure times, each user triggers a failure transaction event, and the failure times of one unit can be accumulated, and the like. The user's failed transaction event may be obtained from corresponding user behavior data, which may be pre-collected by embedding a point code or the like.

After the failure times are determined, the failure times are compared with the corresponding preset threshold value, when the failure times of one target commodity item are higher than the threshold value, the corresponding operation risk exists in the corresponding target commodity item, at the moment, corresponding third condition description data can be generated, wherein the failure times are contained, other commodity information related to the target commodity item is contained as required, and the like.

Similarly, the failure times can be normalized to a specific numerical interval so as to unify different index variation data to the same dimension, so as to effectively compare the advantages and disadvantages of the different index variation data.

According to the above exemplary implementation process of this step, it can be seen that, through the multiple wind control modules, the data of multiple data sources such as external websites or the target commodity item itself can be efficiently compatible and organized, and the complex situations of multiple risk types such as competitive risk, user evaluation risk, inventory balance risk, advertising cost risk, failure transaction risk and the like, such as sales volume data, price data, user behavior data and the like, can be effectively organized, so that comprehensive identification of multiple aspects of management risk conditions of the target commodity item can be realized, and management risk monitoring of the commodity item based on grade implementation can be technically and efficiently implemented.

Step 1200, generating a visual risk notification chart of the target commodity item according to each condition description data;

as can be seen from the above process, the condition description data generated by each wind control module for each target commodity item includes various description data of corresponding management risk conditions, including, but not limited to, difference values of the sales volume data, index variation data, failure transaction times, and the like, and of course, commodity information of the corresponding target commodity item is also included if necessary, or at least feature identifiers of the target commodity item are associated, so that the commodity information of the target commodity item is obtained from a commodity database according to the feature identifiers, therefore, for each target commodity item, two types of information, including numerical information, including non-numerical information, can be obtained according to the condition description data generated by a plurality of wind control modules, wherein the numerical information can be used for generating a chart effect, for example, each specific numerical value is displayed in a bar chart according to each specific numerical information, and the difference values of the sales volume data of the target commodity item and the index variation data corresponding to user evaluation are shown in the histogram shown in fig. 4, and the feature identifiers of the target commodity item are also shown for reference.

The risk notification chart can be placed in a corresponding result page in the form of pictures to be pushed to terminal equipment of a user for loading and displaying. Of course, other data exchange format representations may be pushed to the terminal device in the relevant result page, and then the terminal device analyzes the result to generate a corresponding chart effect. It can be seen that the risk notification chart can visually present the risk status of the target commodity item.

In practice, a user typically obtains a corresponding risk notification chart for a plurality of target commodity items, and each target commodity item may contain monitoring of the operational risk status of a plurality of aspects, whereby status description data of a plurality of different contents is obtained, in which case a chart of a corresponding format suitable for representing the numerical information may be generated according to the difference of the various numerical information contained in the status description data, without being limited to the example of fig. 4.

In some embodiments, for the risk notification chart to be generated for the target commodity item, each chart template may be preset, so that each chart template is suitable for displaying numerical information in several condition description data therein, that is, numerical information to be displayed specifically has a corresponding relation with the chart template, and according to the corresponding relation, each specific numerical information contained in the condition description data provided by each air control module is identified and sleeved into the corresponding template, so that the corresponding risk notification template can be generated. And so forth, those skilled in the art will be able to implement the concepts disclosed herein in a flexible manner.

Step S1300, determining the comprehensive score of the target commodity item according to the condition description data;

in order to reflect the risk profile of the target commodity item as a whole, on the basis that each wind control module periodically provides respective corresponding condition description data, numerical information in each condition description data can be utilized to fuse each numerical value therein, for example, the following formula is used to calculate the comprehensive score of each numerical information:

Score＝α ₁ *Value ₁ +α ₂ *Value ₂ +…+α _n *Value _n

wherein:

α ₁ +α ₂ +…+α _n =1, the final obtained composite score can be normalized and projected to a specific numerical interval.

Value _n And n is the total amount of numerical information in all the condition description data.

In other embodiments, other ways of determining the comprehensive evaluation corresponding to the target merchandise item may be employed, which is not listed here.

And S1400, constructing an alarm page containing the comprehensive score and risk notification chart and pushing the alarm page to a target user.

After the comprehensive scores of one or more target commodity items and the risk notification charts thereof are obtained, the comprehensive scores and the risk notification charts are constructed into corresponding alarm pages and pushed to corresponding target users. The target user may be a preset operating user having authority to access the alert page.

The mode of pushing the alarm page to the target user depends on the type of a communication interface preset by communication with the target user, for example, when the communication interface is an instant communication interface, the access address of the alarm page can be sent to the target user only through the instant communication interface; in the case of a mail communication interface, the alert page may be embedded in a mail and sent to the target user's mailbox so that the composite score and risk notification chart can be directly seen when the target user opens the mail.

The alarm page may be used to display the comprehensive scores of multiple target commodity items and the risk notification charts thereof at the same time, in which case, the multiple target commodity items may be represented as a list, and commodity titles, feature identifiers, commodity pictures, comprehensive scores, chart guide identifiers, and the like of the respective target commodity items are given in the list and ranked according to the comprehensive scores. And when the chart guide mark is touched, popping up a corresponding risk warning chart of the target commodity item.

After receiving the alarm page, the terminal equipment of the target user can load and display the alarm page, so that a corresponding comprehensive score and risk notification chart is displayed in a graphical user interface of the terminal equipment, and the management risk condition of the target commodity item is clearly and efficiently mastered.

According to the embodiment, the risk prevention and control is sunk to the specific commodity level, the complicated risk requirement is divided into a plurality of special wind control modules to monitor, the management risk conditions of the target commodity items are monitored through the wind control modules respectively, the condition description data which are quantized after the wind control modules monitor the management risk conditions are obtained, so that the concentration of the multiple aspects of risk information of the commodity items is realized, then, a corresponding visual chart is further generated according to the risk information, a corresponding comprehensive score is determined, and the corresponding comprehensive score is pushed to related target users.

On the basis of any of the above embodiments, referring to fig. 5, determining the composite score of the target commodity item according to the condition description data includes:

step S1310, constructing any plurality of the condition description data into a condition description vector;

In order to efficiently and intelligently determine the comprehensive score of each target commodity item, a classification model which is trained in advance to achieve a convergence state is adopted in the embodiment, and the classification model is used for reasoning the corresponding comprehensive score according to the multi-source condition description data of the target commodity item. For this purpose, according to the input format of the classification model in the pre-training stage, each piece of numerical information, such as the sales volume data difference value, the index change data, the failure number, and the like, may be extracted from each piece of condition description data of the target commodity item, and these pieces of numerical information may be used as feature values to construct a condition description vector.

Step S1320, inputting the condition description vector into a preset score prediction model to predict a comprehensive score corresponding to the condition description vector;

the score prediction model may be a pre-modeled machine learning model that is responsible for fitting its corresponding composite score based on the condition description vector, or the machine learning model may be modeled according to a pre-set composite score formula, such as the formula exemplified above. And then training the training sample by adopting a corresponding training sample until the training sample converges, and putting the training sample into an on-line reasoning stage for use.

When the comprehensive score of the condition description vector needs to be determined, the condition description vector is directly input into the machine learning model, and the machine learning model can utilize the weight parameters obtained by the machine learning model in the training process to carry out weighted summation on each numerical value in each condition description vector, so that the sum value is directly obtained as the comprehensive score corresponding to the target commodity item.

Step S1330, converting the composite score into a specific format representation.

The output of the score prediction model is typically normalized to a floating point value space of a particular value interval, e.g., [0,1], which may be converted to a format representation of a percentage, e.g., to make the composite score more visual.

According to the embodiment, numerical information in the condition description data corresponding to the same target commodity item can be compiled into the condition description vector, and then the corresponding comprehensive score is calculated according to the condition description vector by adopting the pre-modeling score prediction model, so that the comprehensive score has higher efficiency and higher accuracy, and has higher practical index significance in indicating the overall management risk condition of the target commodity item.

On the basis of any of the above embodiments, referring to fig. 6, constructing an alert page including the comprehensive score and risk notification chart to be pushed to a target user includes:

step 1410, judging the alarm level corresponding to the comprehensive score;

when the alarm page is constructed, the alarm pages with different styles can be generated according to the alarm grade of the comprehensive score of the target commodity item, so that the alarm pages with different styles can be sent to corresponding target users corresponding to the alarm grade.

Specifically, the target user may be a target user with manager level authority, or may be a target user with only basic level authority, that is, the role identities of the target users may be different, and the alarm pages may be correspondingly configured corresponding to target users with different role identities, so as to allow the target users with different role identities to correspondingly obtain alarm pages corresponding to different alarm levels, or allow the alarm pages with different styles to report to the target users corresponding to different alarm levels.

In order to determine the alarm level corresponding to the composite score, a frequency band interval may be preset, for example, set to: primary alarm, wherein the frequency band interval is [0%,50% ]; medium-level warning, wherein the frequency band interval is [50%,80% ]; and (3) high-level warning, wherein the frequency band interval is [80%,100% ]. And on the basis of each frequency band interval, judging which interval the obtained comprehensive score falls into, and determining the target interval, namely determining the corresponding alarm level.

Step S1420, corresponding to the alarm level, calling a corresponding page style, and constructing the comprehensive score and the risk notification chart into an alarm page of the corresponding page style;

in the application, corresponding page patterns are uniformly and correspondingly arranged corresponding to different alarm levels, such as the primary alarm, the medium alarm, the high alarm and the like. And after determining the alarm grade corresponding to the comprehensive score, correspondingly calling the page style of the corresponding grade, and then, when constructing an alarm page according to the comprehensive score and the risk notification chart, applying the page style of the corresponding grade to the alarm page so that the alarm page corresponds to the corresponding grade to form the corresponding style.

Step S1430, pushing the alarm page to a target user corresponding to the alarm level.

Finally, according to the corresponding relation between the alarm level and the target user, the constructed alarm page can be pushed to the user corresponding to the alarm level determined according to the comprehensive score, for example, the alarm page with the first style of the red theme is sent to the target user of the general supervision level, the alarm page with the second style of the orange theme is sent to the target user of the manager level, the alarm page with the third style of the yellow theme is sent to the target user of the base level, and the like. Therefore, different alarm levels can be shown through different styles, related target users are highlighted, and the alarm pages processed by each target user can be displayed in a relatively uniform style.

According to the embodiment, the alarm level can be determined according to the comprehensive score of the target commodity item, the corresponding mode is applied to generate the corresponding alarm page according to different alarm levels, and the alarm page is sent to the target user corresponding to the alarm level, so that the alarm information of the target user can be classified on the server side, the alarm pages with unified theme style can be obtained for each target user, the interface is ensured to be friendly, the information is displayed in an orderly manner, and the man-machine interaction efficiency of the user side is improved.

On the basis of any of the above embodiments, referring to fig. 7, after the alert page including the comprehensive score and risk notification chart is constructed and pushed to the target user, the method includes:

step S1500, constructing a plurality of target commodity items into an alarm list, wherein the alarm list comprises commodity characteristic identifiers, commodity titles, commodity pictures and the comprehensive scores of the target commodity items;

in the information management system, corresponding comprehensive scores and corresponding risk notification charts are continuously obtained for all target commodity items, and the information is presented to all target users at the first level to process the specific level, but for merchants, special persons are required to process risk prevention of the individual target commodity items, and overall risk conditions of all target commodity items are required to be grasped in a comprehensive way. In this embodiment, the information of each target commodity item and its comprehensive score may be deeply mined to form an alarm list for presentation.

For constructing the alarm list, for each target commodity item for which the comprehensive score has been obtained, the commodity title, the commodity image and the like of the target commodity item can be obtained from the corresponding commodity database according to the characteristic identifiers of the target commodity items, and any commodity information desired to be displayed can be obtained. Then, the commodity characteristic identification, commodity title, commodity picture and comprehensive score of each target commodity item are constructed as mapping relation data, and stored into an alarm list as one data record, so that the construction of the alarm list is completed by storing the mapping relation data of each target commodity item as the corresponding data record in the alarm list.

Step 1600, sorting all target commodity items in the alarm list according to the comprehensive scores;

in order to highlight the degree of alarm between the target commodity items, in this embodiment, each data record in the alarm list may be ordered according to the comprehensive score, and it is recommended to use a reverse ordering mode to arrange each target commodity item from large to small according to the comprehensive score.

Step S1700, pushing the alert list to the corresponding target user.

After the sorting is completed, the alarm list can be pushed to the corresponding target user. The target users receiving the alert list are typically preset, typically with a higher level of administrative authority, such as the target users corresponding to the overall manager of the merchant's business.

It is easy to understand that after the corresponding target user obtains and displays the alarm list, the risk condition of each target commodity item can be relatively comprehensively and completely grasped, and accordingly, corresponding task scheduling can be performed to perform deeper information processing.

According to the above embodiment, after each target commodity item identifies the corresponding management risk condition and obtains the corresponding comprehensive score, the present application may further construct each target commodity item as an alarm list, and associate the corresponding comprehensive score to form overview information, and provide the overview information for the corresponding target user to perform further comprehensive processing.

On the basis of any of the above embodiments, before acquiring the condition description data obtained by monitoring the operation risk condition of the target commodity item by the plurality of wind control modules corresponding to different risk prevention and control functions, the method includes:

Step S2100, acquiring wind control configuration information, wherein the wind control configuration information comprises target commodity items, designated wind control module identifiers and mapping relation data formed by corresponding target users;

in order to monitor the risk of operation of the target commodity item, a management user of the information management system can be allowed to realize the configuration of the target commodity item by providing wind control configuration information. To this end, the relevant management user may provide the target commodity item, the designated wind control module identity, and the corresponding target user who can obtain the alert page and/or alert list in the wind control configuration information, which is essentially a set of mapping relationship data, wherein the target commodity item may be represented by its commodity feature identity. The management user can enter the wind control configuration information through a special page provided by the information management system.

Step S2200, the wind control configuration information is sent to the wind control module corresponding to the designated wind control module identifier, and the corresponding wind control module registers the corresponding target commodity item into the monitoring list.

In the information management system, each carried wind control module can be distributed and deployed and then called through an interface, or can be operated in the same server as a plug-in unit, and the functions realized by each wind control module are independent in any way. In this case, after the information management system obtains the wind control configuration information, the wind control configuration information is sent to the corresponding wind control modules according to the specified wind control module identifiers, then each wind control module identifies whether the wind control configuration information is specified with the own identifier, after confirming that the own identifier is specified, the commodity characteristic identifiers of the target commodity items specified in the wind control configuration information are registered in the monitoring list, and subsequently, the wind control module carries out risk monitoring on the target commodity items by traversing the monitoring list.

According to the embodiment, the method and the device provide an efficient and convenient operation means for starting risk monitoring of the target commodity item, the management user can customize wind control configuration information and submit the wind control configuration information to each wind control module for corresponding registration, the function of each wind control module is relatively independent, partial decoupling with an information management system can be achieved by using a self-registration mechanism, interaction is achieved only through an interface, and the method and the device have the advantage of modularized deployment.

On the basis of any of the above embodiments, referring to fig. 8, before the alert page including the comprehensive score and risk notification chart is constructed and pushed to the target user, the method includes:

step S3100, the whole situation description data and commodity information of the target commodity item to which the situation description data belongs are combined and encoded to obtain combined encoding information;

in order to enrich the information presentation corresponding to the management risk status of the target commodity items to improve the risk identification, the corresponding risk types may be further marked for each target commodity item, and for this purpose, the risk classification model trained in advance to a convergence state may be used for implementation.

The risk classification model comprises a text feature extraction model and a classifier, wherein the text feature extraction model is used for extracting deep semantic information of input information, the classifier performs classification mapping according to the deep semantic information, the deep semantic information is mapped into a classification space to obtain classification probabilities corresponding to all categories of the classification space, and the categories of the classification space are arranged corresponding to all risk types in a risk classification system, so that the classification probabilities corresponding to all risk types of the input information are determined in practice, and the risk type with the largest classification probability is the risk type corresponding to the input information.

The risk classification system may be classified according to different business risk conditions, for example, advertisement class, inventory class, competition class, logistics class, and the like. The risk classification model performs classification task training by adopting a corresponding training sample and a corresponding risk type label in advance, so that the risk classification model has corresponding capability. Each training sample can be manually collected for preparation, and the corresponding risk type is determined according to the operation risk condition of the training sample, and a corresponding risk type label is provided for labeling.

The input information may be joint code information constructed by performing feature extraction and then coding according to commodity information of the target commodity item and all condition description data obtained by the target commodity item.

In one embodiment, for all condition description data of the target commodity item, all numerical value information is extracted as a part of characteristic value, and other non-numerical value data such as commodity titles and the like contained in the condition description data and other commodity information such as commodity attribute data and the like obtained from a commodity database of the target commodity item can be converted into corresponding characteristic values through characteristic engineering, all characteristic values are coded together, and the combined coded information is constructed. The jointly encoded information may be a vector representation comprising characteristic values embodied by all numerical information in all condition description data, word vectors comprising characteristic values obtained by feature extraction of other data or information, and the like, which may be flexibly implemented by one skilled in the art.

Step S3200, inputting the joint coding information into a preset risk classification model, and predicting a risk type corresponding to the joint coding information;

according to the function of the risk classification model, after the joint coding information is input into the risk classification model, the text feature extraction model performs feature extraction on the joint coding information by the associated context so as to extract corresponding deep semantic information, then the deep semantic information is fully connected by a fully connected layer in a classifier and mapped to an output layer, the classification probability corresponding to each class is calculated in each class of a corresponding classification space in the output layer, and then the risk type with the largest classification probability is determined as the joint coding information, namely the risk type corresponding to the target commodity item.

In some embodiments, a preset threshold may be utilized to select one or more risk types with a classification probability greater than the preset threshold as the plurality of main risk types corresponding to the target commodity item.

And S3300, labeling the target commodity item with the risk type so as to be pushed to a corresponding target user along with the alarm page.

After the corresponding risk type of the target commodity item is determined, the target commodity item is marked by taking the risk type as a label, so that the risk type can be correspondingly prompted in different scenes such as an alarm page, an alarm list and the like, and a target user can conveniently and quickly recognize the risk type.

According to the above embodiment, according to the information provided by the description data of various conditions obtained by the target commodity item, the commodity information of the target commodity item is related to construct joint coding information, the joint coding information is inferred by adopting a pre-trained deep learning model, the main risk type corresponding to the target commodity item can be more intelligently and accurately determined, after the target commodity item is marked, the target user can more conveniently identify the risk type of the target commodity item, and conveniently search, summarize and the like, the information processing is more efficient, and the advantage of the information management system with massive commodity items to be monitored in the information processing efficiency is self-evident.

Referring to fig. 9, a grade risk prevention and control device provided according to an aspect of the present application includes a data acquisition module 1100, a chart generation module 1200, a score determination module 1300, and a result pushing module 1400, where the data acquisition module 1100 is configured to acquire status description data obtained by monitoring operation risk status of a target commodity item by a plurality of wind control modules corresponding to different risk prevention and control functions; the chart generating module 1200 is configured to generate a risk notification chart of the target commodity item visualization according to each of the condition description data; the score determination module 1300 is configured to determine a composite score of the target commodity item according to the condition description data; the result pushing module 1400 is configured to push an alert page including the composite score and risk notification chart to a target user.

On the basis of any of the above embodiments, the data acquisition module 1100 includes: the first acquisition unit is configured to periodically acquire first sales volume data of a commodity item similar to a commodity title of a target commodity item in an external website through a first wind control module aiming at a first risk prevention and control function, judge whether the sales volume data exceeds second sales volume data of the target commodity item, and generate corresponding first condition description data when the sales volume data exceeds the second sales volume data, wherein the first condition description data comprises a difference value between the first sales volume data and the second sales volume data; the second acquisition unit is used for periodically acquiring index change data of the target commodity item through a second wind control module aiming at a second risk prevention and control function, and generating second condition description data when the index change data exceeds a preset threshold value, wherein the second condition description data comprises the index change data; the third acquisition unit is configured to periodically count, by the third wind control module, the number of failures of the failed transaction event of the target commodity item for a third risk prevention and control function, and generate third condition description data when the number of failures exceeds a preset threshold, wherein the number of failures is included.

On the basis of any of the above embodiments, the score determining module 1300 includes: a vector conversion unit configured to construct any plurality of the condition description data as a condition description vector; the classification prediction unit is used for inputting the condition description vector into a preset score prediction model and predicting a comprehensive score corresponding to the condition description vector; and a score conversion unit configured to convert the composite score into a specific format representation.

On the basis of any of the above embodiments, the result pushing module 1400 includes: the grade identification unit is used for judging the alarm grade corresponding to the comprehensive score; the style applying unit is set to correspond to the alarm grade, calls a corresponding page style, and constructs the comprehensive score and the risk notification chart into an alarm page of the corresponding page style; and the corresponding pushing unit is used for pushing the alarm page to a target user corresponding to the alarm grade.

On the basis of any of the above embodiments, the grade risk prevention and control device of the present application includes: the list construction module is used for constructing a plurality of target commodity items into an alarm list, and the alarm list comprises commodity characteristic identifiers, commodity titles, commodity pictures and the comprehensive scores of the target commodity items; the sorting processing module is used for sorting all target commodity items in the alarm list according to the comprehensive scores; and the list pushing module is used for pushing the alarm list to the corresponding target user.

On the basis of any of the above embodiments, the grade risk prevention and control device of the present application includes: the configuration acquisition module is used for acquiring wind control configuration information, wherein the wind control configuration information comprises target commodity items, specified wind control module identifiers and mapping relation data formed by corresponding target users; the configuration application module is used for sending the wind control configuration information to the wind control module corresponding to the designated wind control module identifier, and the corresponding wind control module registers the corresponding target commodity item into the monitoring list.

On the basis of any of the above embodiments, the grade risk prevention and control device of the present application includes: the coding processing module is used for jointly coding all the condition description data and commodity information of the target commodity item to which the condition description data belongs to obtain joint coding information; the risk classification module is used for inputting the joint coding information into a preset risk classification model and predicting a risk type corresponding to the joint coding information; and the risk labeling module is used for labeling the target commodity item with the risk type so as to be pushed to a corresponding target user along with the alarm page.

Another embodiment of the present application also provides a grade risk prevention and control device. As shown in fig. 10, the internal structure of the grade risk prevention and control device is schematically shown. The level risk prevention and control device includes a processor, a computer readable storage medium, a memory, and a network interface connected by a system bus. The non-volatile readable storage medium readable by a computer of the grade risk prevention and control device stores an operating system, a database and computer readable instructions, wherein the database can store an information sequence, and the computer readable instructions can enable the processor to realize a grade risk prevention and control method when the computer readable instructions are executed by the processor.

The processor of the grade risk prevention and control device is used for providing computing and control capabilities and supporting the operation of the whole grade risk prevention and control device. The memory of the grade risk prevention and control device may store computer readable instructions that, when executed by the processor, cause the processor to perform the grade risk prevention and control method of the present application. The network interface of the level risk prevention and control device is used for communicating with the terminal connection.

It will be appreciated by those skilled in the art that the structure shown in fig. 10 is merely a block diagram of a portion of the structure associated with the present application and does not constitute a limitation of the grade risk prevention and control device to which the present application is applied, and that a particular grade risk prevention and control device may include more or fewer components than shown in the figures, or may combine certain components, or have a different arrangement of components.

The processor in this embodiment is configured to perform specific functions of each module, sub-module, and unit in fig. 9, and the memory stores program codes and various types of data required for executing the above modules or sub-modules. The network interface is used for realizing data transmission between the user terminals or the servers. The non-volatile readable storage medium in this embodiment stores program codes and data required for executing all modules in the level risk prevention and control device of the present application, and the server can call the program codes and data of the server to execute the functions of all modules.

The present application also provides a non-transitory readable storage medium storing computer readable instructions which, when executed by one or more processors, cause the one or more processors to perform the steps of the method for controlling risk of a grade according to any of the embodiments of the present application.

The present application also provides a computer program product comprising computer programs/instructions which when executed by one or more processors implement the steps of the method described in any of the embodiments of the present application.

It will be appreciated by those skilled in the art that implementing all or part of the above-described methods according to the embodiments of the present application may be accomplished by way of a computer program stored in a non-transitory readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a computer readable storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).

In summary, the method and the system can realize the technical grade monitoring of the management risk of the commodity items, and simultaneously can realize effective information interaction, ensure that the e-commerce merchants can monitor the management risk of the commodity items in the distributed multiple independent stations in a unified manner, improve the information processing efficiency of the merchants, and optimize the user experience of the related information management system.

Claims

1. A method for controlling grade risk, comprising:

2. The method for controlling the grade risk according to claim 1, wherein obtaining the condition description data obtained by monitoring the operational risk condition of the target commodity item by a plurality of wind control modules corresponding to different risk control functions comprises:

3. The method of claim 1, wherein determining a composite score for the target commodity item from the condition description data comprises:

the composite score is converted to a format-specific representation.

4. A method of controlling a risk in a class according to claim 3, wherein constructing an alert page containing the composite score and risk notification chart to be pushed to a target user comprises:

Judging the alarm grade corresponding to the comprehensive score;

pushing the alarm page to a target user corresponding to the alarm level.

5. The method of claim 4, wherein constructing an alert page containing the composite score and risk notification chart, after pushing to a target user, comprises:

and pushing the alarm list to a corresponding target user.

6. The method for controlling risk of any one of claims 1 to 5, wherein before acquiring the condition description data obtained by monitoring the operational risk condition of the target commodity item by the plurality of wind control modules corresponding to different risk control functions, the method comprises:

7. The method of any one of claims 1 to 5, wherein constructing an alert page containing the composite score and risk notification chart prior to pushing to a target user, comprises:

8. A grade risk prevention and control device, comprising:

9. A grade risk prevention and control device comprising a central processor and a memory, characterized in that the central processor is adapted to invoke execution of a computer program stored in the memory to perform the steps comprised by the method according to any of claims 1-7.

10. A non-transitory readable storage medium, characterized in that it stores a computer program in the form of computer readable instructions, which when invoked by a computer to run, performs the steps comprised by the method according to any one of claims 1 to 7.