Bond trading risk warning method, device, equipment and medium based on big data

Abstract

The present application discloses a bond trading risk warning method, device, equipment and medium based on big data. The bond trading risk warning method based on big data includes: in response to a target object's click operation on a bond trading interface, selecting risk warning information that matches the transaction risk category of the clicked bond from a preset risk warning information library, the clicked bond is obtained based on the target object's click operation on the bond displayed in the bond trading interface; performing information extraction processing on the matched risk warning information to determine the key information contained in the matched risk warning information; and highlighting the key information contained in the matched risk warning information. The bond trading risk warning method based on big data disclosed in the present application simplifies complex bond risk warnings by highlighting the key information contained in the risk warning information of the bonds, thereby highlighting the key information and improving the information utilization rate.

Description

Technical Field

[0001] The present application relates to the technical field of transaction risk warning, and in particular, to a bond transaction risk warning method, device, electronic device and computer-readable storage medium based on big data. Background Art

[0002] Bonds are an important type of investment tool. When investing in bonds, customers may gain returns, but may also bear certain transaction risks. The risks of trading bonds include but are not limited to: credit risk, liquidity risk, currency risk, interest rate risk, market risk, etc. If customers choose to invest in high-yield bonds or bonds with certain characteristics and risks, they will bear higher risks. For example, if a bond defaults, customers may even lose their principal. Therefore, it is necessary to let customers understand the characteristics of bond products and the degree of risk they need to bear before trading.

[0003] In the prior art, although there is a function to prompt bond risks on the bond trading platform, the description of bond risks is cumbersome and there are no key points throughout the platform, which is not conducive to customers quickly understanding the characteristics of the corresponding bond products and the risks they need to bear, resulting in low information utilization. Summary of the invention

[0004] In order to solve the above technical problems, the embodiments of the present application provide a bond trading risk warning method, device, electronic device and computer-readable storage medium based on big data.

[0005] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0006] According to one aspect of an embodiment of the present application, a bond trading risk warning method based on big data is provided, including: in response to a target object's click operation on a bond trading interface, selecting risk warning information matching the trading risk category of a clicked bond from a preset risk warning information library, wherein the clicked bond is obtained based on the target object's click operation on a bond displayed in the bond trading interface; performing information extraction processing on the matching risk warning information to determine key information contained in the matching risk warning information; and highlighting the key information contained in the matching risk warning information.

[0007] According to one aspect of an embodiment of the present application, a bond trading risk warning device based on big data is provided, the device comprising: a matching module, configured to respond to a target object's click operation on a bond trading interface, select risk warning information matching the trading risk category of a clicked bond from a preset risk warning information library, the clicked bond being obtained based on the target object's click operation on a bond displayed in the bond trading interface; a determination module, configured to perform information extraction processing on the matched risk warning information, and determine key information contained in the matched risk warning information; and a display module, configured to highlight the key information contained in the matched risk warning information.

[0008] According to one aspect of an embodiment of the present application, an electronic device is provided, including a processor and a memory, wherein computer-readable instructions are stored in the memory, and when the computer-readable instructions are executed by the processor, the above-mentioned big data-based bond trading risk warning method is implemented.

[0009] According to one aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor of a computer, the computer executes the bond trading risk warning method based on big data as provided above.

[0010] According to one aspect of the embodiments of the present application, a computer program product or a computer program is provided, the computer program product or the computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the bond trading risk warning method based on big data provided in the above various optional embodiments.

[0011] In the technical solution provided in the embodiments of the present application, in response to a target object's click operation on a bond trading interface, risk warning information matching the trading risk category of the clicked bond is selected from a preset risk warning information library; information extraction processing is performed on the matching risk warning information to determine key information contained in the matching risk warning information; and key information contained in the matching risk warning information is highlighted. Compared with the cumbersome and unemphasized bond trading risk warnings in the prior art, the present application simplifies the complex bond risk warnings by focusing on the key information contained in the risk warning information of the bonds, thereby highlighting key information, improving information display efficiency and utilization, and at the same time improving transaction security.

[0012] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0014] Figure 1 It is a flowchart of a bond trading risk warning method based on big data shown in an exemplary embodiment of the present application;

[0015] Figure 2 It is a flowchart of a bond trading risk warning method based on big data shown in another exemplary embodiment of the present application;

[0016] Figure 3 It is a flowchart of a bond trading risk warning method based on big data shown in another exemplary embodiment of the present application;

[0017] Figure 4 It is a flowchart of a bond trading risk warning method based on big data shown in another exemplary embodiment of the present application;

[0018] Figure 5 It is a flowchart of a bond trading risk warning method based on big data shown in another exemplary embodiment of the present application;

[0019] Figure 6 It is a schematic diagram showing the effect of risk warning information of Group A bonds shown in an exemplary embodiment of the present application;

[0020] Figure 7 It is a flowchart of a bond trading risk warning method based on big data shown in another exemplary embodiment of the present application;

[0021] Figure 8 It is a block diagram of a bond trading risk warning device based on big data shown in an exemplary embodiment of the present application;

[0022] Fig. 9 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0023] Here, exemplary embodiments will be described in detail, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are only examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims.

[0024] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0025] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0026] It should also be noted that the "multiple" mentioned in this application refers to two or more than two. "And / or" describes the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship.

[0027] First of all, big data refers to a collection of data that cannot be captured, managed, and processed by conventional software tools within a certain time frame. It is a massive, high-growth, and diversified information asset that requires new processing models to have stronger decision-making power, insight discovery, and process optimization capabilities. With the advent of the cloud era, big data has also attracted more and more attention. Big data requires special technologies to effectively process large amounts of data within a tolerable time frame. Technologies suitable for big data include large-scale parallel processing databases, data mining, distributed file systems, distributed databases, cloud computing platforms, the Internet, and scalable storage systems. Specifically, as people's economic level continues to improve, investment and financial management has become the mainstream way of financial management. As one of the investment and financial management methods, bonds have also received considerable attention, especially investment and financial management methods based on big data. However, investment bonds based on big data may gain returns, but may also bear certain trading risks. Therefore, in order to enable customers to understand the characteristics of bond products and the risks they need to bear, it is particularly important to remind customers of bond trading risks based on big data.

[0028] In the prior art, although there are risk warnings for bond transactions, the descriptions of the characteristics of bond products and the degree of risk to be borne are too cumbersome, and the key points are not highlighted throughout the document, making it impossible for customers to quickly understand the relevant circumstances of the corresponding bond products, and reducing the efficiency of bond transactions.

[0029] Based on this, in order to highlight the key information in the bond products, customers can learn about the characteristics of the bond products and the degree of risk they need to bear based on the key information. The embodiments of the present application involve: in response to the target object's click operation on the bond trading interface, selecting risk warning information that matches the transaction risk category of the clicked bond from a preset risk warning information library; performing information extraction processing on the matching risk warning information to determine the key information contained in the matching risk warning information; highlighting the key information contained in the matching risk warning information. Compared with the cumbersome and unfocused prompts in the prior art, the present application simplifies the complex bond risk warnings by focusing on the key information contained in the risk warning information of the bonds, realizes the highlighting of key information, improves the efficiency and utilization of information display, and improves the transaction security.

[0030] See also Figure 1 , Figure 1 It is a flowchart of a bond trading risk warning method based on big data shown in an exemplary embodiment of the present application. The bond trading risk warning method based on big data in this embodiment can be applied to a bond trading risk warning device. The bond trading risk warning device of the present application can be a server, a mobile device, or a system in which a server and a mobile device cooperate with each other. Accordingly, the various parts included in the mobile device, such as various units, sub-units, modules, and sub-modules can all be set in the server, can all be set in the mobile device, or can be set in the server and the mobile device respectively.

[0031] Furthermore, the above-mentioned server can be hardware or software. When the server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server. When the server is software, it can be implemented as multiple software or software modules, such as software or software modules used to provide distributed servers, or it can be implemented as a single software or software module, which is not specifically limited here.

[0032] like Figure 1 As shown, in an exemplary embodiment, the bond trading risk warning method based on big data at least includes steps S110 to S130, which are described in detail as follows:

[0033] Step S110: In response to the target object's click operation on the bond trading interface, risk warning information matching the transaction risk category of the clicked bond is selected from a preset risk warning information library, and the clicked bond is obtained based on the target object's click operation on the bond trading interface.

[0034] The target object can be the bond purchaser, or it can be understood as the user or customer of the bond trading platform.

[0035] The bond trading interface refers to an interactive interface in the bond trading risk warning device for displaying or presenting bonds and bond-related information. For example, it can be a touch screen interface of a mobile terminal.

[0036] The preset risk warning information library is the risk warning information corresponding to various bond trading risk categories pre-stored. Among them, the trading risk categories can include prohibited trading bonds, distressed bonds, complex bonds and ordinary bonds. Prohibited trading bonds usually refer to bonds that have already defaulted. Distressed bonds usually refer to bonds with short-term prices, drastic rating declines, high default risks, and need additional risk warnings. Complex bonds usually refer to bonds that have certain characteristics but do not have high credit risks. It should be noted that according to the order of prohibited trading bonds, distressed bonds, complex bonds and ordinary bonds, the risk level corresponding to each bond risk category is getting lower and lower, that is, prohibited trading bonds have the highest risk level and ordinary bonds have the lowest risk level. Exemplarily, the risk warning information of distressed bonds can specifically include "distressed bonds refer to bonds whose bond issuers have defaulted, gone bankrupt or had financial difficulties in the near future and are developing towards the above situation. The bond price may have a large discount, and the spread may be much larger than the average level of Industrial Bank. There are great risks in investing in distressed bonds, including but not limited to high default risk, liquidity risk, and the risk of being subject to economic cycle changes. Distressed bond holders may suffer significant losses when closing their positions."

[0037] Click bonds refer to the bonds that customers want to know about among the bonds displayed on the bond trading interface, that is, the bonds that users want to click to learn more about displayed on the bond trading interface.

[0038] The customer clicks on the bond he wants to know about on the bond trading interface according to his own needs. The bond trading risk warning device receives the customer's click operation and selects risk warning information that matches the transaction risk category of the clicked bond from the preset risk warning information library. Exemplarily, the bond trading risk warning device can calculate the category feature similarity between the category feature of the transaction risk category corresponding to the preset risk warning information in the preset risk warning information library and the category feature of the transaction risk category of the clicked bond, and then determine the corresponding transaction risk category and the risk warning information corresponding to the transaction risk category when the category feature similarity is greater than the preset category feature similarity threshold.

[0039] Step S120: extracting the matched risk warning information to determine key information contained in the matched risk warning information.

[0040] Key information refers to important information in the matching risk warning information, that is, important information that enables customers to quickly understand the characteristics and risks of the corresponding bonds. For example, if the matching risk warning information is "distressed bonds refer to bonds whose issuers have defaulted, gone bankrupt or are in financial difficulties in the near future and are moving towards the above situation. The bond price may have a large discount, and the spread may be much larger than the average level of Industrial Bank. There are great risks in investing in distressed bonds, including but not limited to high default risk, liquidity risk, and risk of being subject to economic cycle changes. Distressed bond holders may suffer significant losses when closing their positions", then the key information may be "distressed bonds", "there are great risks", "closing positions", "suffering significant losses", etc.

[0041] Since the risk warning information includes information such as the bond characteristics and risks of the bond, in order to determine the key information contained in the matching risk warning information, the bond trading risk warning device extracts and processes the matching risk warning information to obtain the extracted information, and then determines the key information contained in the matching risk warning information based on the extracted information. It should be noted that the bond trading risk warning device can determine the key information contained in the matching risk warning information based on the similarity between the extracted information and the preset information, and can also determine the key information contained in the matching risk warning information based on the criticality of the extracted information in the matching risk warning information. The embodiments of the present application are not limited to this.

[0042] Step S130, highlighting key information contained in the matching risk warning information.

[0043] The highlighting method may be highlighting, bolding, or making the font size of the key information larger than other font sizes, or marking the key information with emphasis, for example, adding an exclamation mark in the upper right corner of the key information. This embodiment of the application is not limited to this.

[0044] The bond trading risk warning device highlights the key information contained in the risk warning information of the clicked bond based on the click operation. Specifically, the bond trading risk warning device can highlight the key information contained in the matching risk warning information on the trading risk interface.

[0045] It can be seen that the big data-based bond trading risk warning method of this embodiment responds to the target object's click operation on the bond trading interface, selects risk warning information that matches the trading risk category of the clicked bond from the preset risk warning information library; performs information extraction processing on the matching risk warning information to determine the key information contained in the matching risk warning information; highlights the key information contained in the matching risk warning information. Compared with the cumbersome and unfocused prompts in the prior art, this application simplifies the complex bond risk warnings by focusing on the key information contained in the risk warning information of the bonds, thereby highlighting the key information, improving the efficiency and utilization of information display, and at the same time improving transaction security.

[0046] Figure 2 FIG. 1 is a flowchart of a risk warning method for bond trading based on big data, which is another exemplary embodiment of the present application. Figure 2 In the embodiment shown, the key information contained in the matching risk warning information is determined by the key probability of the candidate words contained in the matching risk warning information. For other steps of bond trading risk warning, please refer to the contents described in the aforementioned embodiment and will not be repeated here.

[0047] like Figure 2 As shown, in an exemplary embodiment, step S120 performs information extraction processing on the matched risk warning information to determine the key information contained in the matched risk warning information, which may include the following steps:

[0048] Step S121, extracting candidate words contained in the matching risk warning information.

[0049] Candidate words refer to words that may be determined as key information in the matching risk warning information. Of course, the keywords here are not limited to words, but can also be sentences that may be determined as key information in the matching risk warning information.

[0050] The bond trading risk warning device extracts and processes the information in the matching risk warning information to obtain candidate words therein. Exemplarily, the bond trading risk warning device can extract information from the matching risk warning information and use the repetition rate or location of the extracted information in the risk warning information to determine whether the extracted information is a candidate word in the matching risk warning information.

[0051] Step S122, calculating the key probability of the candidate word in the matching risk warning information.

[0052] The key probability can be understood as the importance of the extracted candidate words in the matching risk warning information.

[0053] The bond trading risk warning device determines the key probability of the candidate word in the matching risk warning information, specifically by searching for the probability value of each candidate word in a preset dictionary, that is, presetting the possible important words in each risk warning information, and setting the probability value of each important word, and using the searched probability value as the key probability of the candidate word in the matching risk warning information. Alternatively, the bond trading risk warning device can determine the key probability according to the number of times the candidate word appears in the matching risk warning information, that is, the more times it appears, the higher the key probability.

[0054] Step S123: If the key probability is greater than a preset key probability threshold, the corresponding candidate word is used as the key information included in the matching risk warning information.

[0055] The preset key probability threshold is a threshold used to determine whether a candidate word can be used as key information, which can be set manually.

[0056] The bond trading risk warning device determines whether the key probability of a candidate word in the matching risk warning information is greater than a preset key probability threshold. If so, the corresponding candidate word is used as the key information included in the matching risk warning information. If not, the corresponding candidate word is not used as the key information included in the matching risk warning information.

[0057] It can be seen that the big data-based bond trading risk warning method of this embodiment determines whether the candidate words contained in the matching risk warning information can be used as the key information contained in the matching risk warning information by determining the key probability of the candidate words contained in the matching risk warning information, and then highlights the key information in the bond trading risk based on the key information. It realizes the simplification of complex bond risk warnings through the display of key information contained in the risk warning information, so that customers can quickly understand the relevant circumstances of the corresponding bonds, thereby improving the efficiency of bond trading.

[0058] Figure 3 FIG. 1 is a flowchart of a risk warning method for bond trading based on big data, which is another exemplary embodiment of the present application. Figure 3 In the illustrated embodiment, the key probability of the candidate word in the matching risk warning information is determined by the feature vector of the candidate word. For other steps of providing risk warning for bond trading, please refer to the contents described in the aforementioned embodiment and will not be repeated here.

[0059] like Figure 3 As shown, in an exemplary embodiment, the process of calculating the key probability of the candidate word in the matching risk warning information in step S122 may include the following steps:

[0060] Step S1221: extracting feature vectors of candidate words.

[0061] The bond trading risk warning device extracts the feature vector of the candidate word. Specifically, the bond trading risk warning device can extract the features of the candidate word, and then convert the features of the candidate word into the feature vector of the candidate word. Exemplarily, the bond trading risk warning device can input the features of the candidate word into a text vectorization model for processing to obtain the feature vector of the candidate word. It should be noted that the text vectorization model is a pre-trained deep neural network model.

[0062] Step S1222: Calculate the feature vector similarity between the feature vector of the candidate word and the feature vector of the keyword contained in the preset key information storage library.

[0063] The preset stored key information refers to pre-stored words and sentences that may be regarded as key information.

[0064] The feature vector similarity refers to the degree of similarity between the feature vector of the candidate word and the feature vector of the keyword contained in the preset storage key information library. The feature vector of the keyword contained in the preset storage key information library can be obtained by extracting the features of the keyword contained in the preset storage key information library and inputting the features of the keyword contained in the preset storage key information library into the text vectorization model for processing.

[0065] The bond trading risk warning device obtains the feature vectors of the keywords contained in the preset storage key information library, and calculates the feature vector similarity between the feature vector of the candidate word and the feature vector of the keyword contained in the preset storage key information library. Specifically, the bond trading risk warning device extracts features of the candidate word and the keywords contained in the preset storage key information library, obtains the candidate word features and the features of the keywords contained in the preset storage key information library, and inputs the candidate word features and the features of the keywords contained in the preset storage key information library into the text vectorization model for processing, obtains the feature vectors of the keywords contained in the preset storage key information library and the feature vectors of the candidate word, and then calculates the feature vector similarity Sim(X,Y) between the first feature vector X of the keywords contained in the preset storage key information library and the second feature vector Y of the candidate word as follows:

[0066]

[0067] Here, ||·|| represents the modulus of the vector, and α and β represent the first similarity factor and the second similarity factor.

[0068] Step S1223: taking the ratio between the feature vector similarity and the preset feature vector value as the key probability of the candidate word in the matching risk warning information.

[0069] The preset feature vector value may be a feature vector value set manually.

[0070] The bond trading risk warning device uses the ratio between the feature vector similarity and the preset feature vector value as the key probability of the candidate word in the matching risk warning information.

[0071] It can be seen that the big data-based bond trading risk warning method of this embodiment determines the key probability contained in the matching risk warning information through the feature vector similarity between the feature vector of the candidate word and the feature vector of the keyword contained in the preset key information library, and then can simplify the complex bond risk warnings by displaying the key information contained in the risk warning information determined based on the key probability, and realize the prominent prompt of key information, so that customers can quickly understand the relevant situation of the corresponding bonds, thereby improving the efficiency of bond trading.

[0072] Figure 4 FIG. 1 is a flowchart of a risk warning method for bond trading based on big data, which is another exemplary embodiment of the present application. Figure 4 As shown, in an exemplary embodiment, before the process of selecting risk warning information matching the transaction risk category of the clicked bond from the preset risk warning information library in response to the target object's click operation on the bond trading interface in step S110, the following steps may be included:

[0073] Step S100: Based on the risk levels corresponding to the transaction risk categories of the bonds displayed in the bond trading interface, the bonds displayed in the bond trading interface are displayed differently according to their risk levels.

[0074] Because the prior art does not differentiate between the bonds when displaying the bonds for customers to choose on the bond trading interface, customers need to click on the corresponding bonds one by one to view the product features and risks they need to bear, resulting in low efficiency in bond trading for customers. Based on this, the embodiment of the present application displays differentiated prompts for each bond on the bond trading interface. Specifically, the bond trading risk prompt device differentiates the bonds displayed on the bond trading interface according to the risk level corresponding to the transaction risk category of the bonds displayed on the bond trading interface, which can improve customers' understanding of the risks of the bonds displayed on the bond trading platform, thereby improving bond trading efficiency.

[0075] Exemplarily, if the trading risk type is a prohibited bond, when the prohibited bond is displayed separately, the risk level corresponding to the prohibited bond can be determined based on the category similarity between the prohibited bond and the preset trading risk type, that is, the risk level of the corresponding data range is determined based on the data range to which the category similarity belongs. When it is determined that the risk level of the prohibited bond is the highest risk level, when the corresponding bond is displayed on the bond trading interface, an exclamation mark is added to the preset position of the prohibited bond. For example, an exclamation mark can be added to the upper right corner of the prohibited bond to distinguish the trading risk level between the prohibited bond and other bonds.

[0076] It can be seen that the big data-based bond trading risk warning method of this embodiment can improve customers' understanding of the risks of bonds displayed on the bond trading platform by displaying differentiated warnings for each bond according to the risk level corresponding to the bond's trading risk category on the bond trading interface, thereby improving bond trading efficiency.

[0077] Figure 5 FIG. 1 is a flowchart of a risk warning method for bond trading based on big data, which is another exemplary embodiment of the present application. Figure 5 As shown, in an exemplary embodiment, after the process of highlighting the key information included in the matched risk warning information in step S130, the following steps may also be included:

[0078] Step S140, in response to the target object's selection operation on at least two highlighted transaction risk categories, risk warning information corresponding to the transaction risk category is displayed, and key words and phrases in the risk warning information are highlighted.

[0079] Considering that bond products may belong to at least two transaction risk categories, in order to enable customers to fully understand the product characteristics of each transaction risk category and the responsibilities they need to bear, at least two transaction risk categories to which the bond product belongs are highlighted at the same time during the transaction prompt, and based on the customer's selection operation of at least two transaction risk categories, the risk warning information corresponding to the transaction risk category is displayed, and the key words and phrases therein are highlighted. Among them, the bond transaction risk warning device can determine the risk warning information that matches the transaction risk category of the selected bond from the preset risk warning information library, and perform information extraction processing on the matched risk warning information, determine the key information contained in the matched risk warning information, and then highlight the key information contained in the matched risk warning information.

[0080] To explain the embodiments of the present application in detail, Figure 6 The embodiment is described as follows: Figure 6As shown in the figure, the bond trading interface shows that Group A's bond products are both complex products and distressed products. After the user selects a complex product or a distressed product, the corresponding risk warning information will be displayed, and the key information will be highlighted (for details, please refer to Figure 6 The key information highlighted by underline in the table is “complex products” and “distressed bonds”. If the target audience selects Figure 6 For complex products, the risk warning information of the complex products will be displayed accordingly, and the key information in the risk warning information of the complex products will be highlighted.

[0081] It should also be noted that before the bonds of Group A are displayed in the bond trading interface, they can be configured in the background by the staff. For example, the staff can click on the risk control configuration in the bond trading interface. The bond trading risk warning device obtains the click information based on the staff's click operation, and displays the risk assessment parameters and trading switches and warnings under the risk control configuration in the bond trading interface based on the click information. The staff can set the relevant parameters under the risk control configuration. For example, the staff clicks on the trading switch and warning options in the bond trading interface. The bond trading risk warning device displays the product name parameters, buy switch parameters, sell switch parameters and high-risk warning parameters under the trading switch and warning options in the bond trading interface based on the staff's click operation. The staff can click on the relevant parameters in the bond trading interface to modify the parameters. Of course, the above-mentioned background configuration method can also be used to modify the parameters of other types of bonds.

[0082] It can be seen that the big data-based bond trading risk warning method of this embodiment simplifies complex bond risk warnings by highlighting trading risk categories and key words and phrases in the risk warning information corresponding to each trading risk category, and highlights key information, so that customers can quickly understand the relevant circumstances of the corresponding bonds, thereby improving bond trading efficiency.

[0083] Figure 7 FIG. 1 is a flowchart of a risk warning method for bond trading based on big data, which is another exemplary embodiment of the present application. Figure 7 As shown, in an exemplary embodiment, before the process of selecting risk warning information matching the acquired transaction risk category of the bond to be evaluated from the preset risk warning information library in step S110, the following steps may also be included:

[0084] Step S111, obtaining text information contained in the bonds displayed in the bond trading interface.

[0085] The text information contained in the displayed bond may refer to the remaining years of the bond, whether it is redeemable, historical trends, dividend frequency, etc.

[0086] The bond trading risk warning device obtains text information contained in the bond, wherein the text information includes text terms such as the remaining term of the bond, whether it is redeemable, historical trends, and dividend frequency.

[0087] Step S112: performing recognition processing on the text information to obtain key text information contained in the text information.

[0088] The key information included in the text information may refer to the value of each text, for example, two years in the remaining term of the bond, "no" in "redeemable or not", and other key text information.

[0089] The bond trading risk warning device identifies the text information and determines the key text information in the text information. Exemplarily, the bond trading risk warning device identifies the text information and determines the text information value in the text information.

[0090] Step S113, performing a search process using the key text information, and determining a corresponding transaction risk category based on the search results obtained.

[0091] Retrieval processing refers to the process of matching key text information with various text information contained in a preset text database.

[0092] The search result refers to the degree of match between the key text information and each text information contained in the preset text database. If the degree of match is higher than the preset match threshold, it can be determined that the matched text information in the preset text database belongs to the same category as the key text information. If the degree of match is lower than the preset match threshold, it can be determined that the corresponding text information in the preset text database belongs to a different category from the key text information. It should be noted that in the process of using the key text information for search processing, the bond trading risk warning device can perform search processing in the order of arrangement of the text information contained in the preset text database, so as to stop the subsequent search processing when the text information matching the key text information is determined in the preset text database, thereby improving the search efficiency and avoiding the problem of computational redundancy.

[0093] The bond trading risk warning device searches for text information matching the key text information in a preset text database based on the key text information, and when it is determined that there is text information matching the key text information, the trading risk category to which the matching text information belongs is determined as the trading risk category to which the key text information belongs. Exemplarily, the bond trading risk warning device searches sequentially based on the arrangement order of the text information contained in the preset text database, and when it is determined that there is key text information and the degree of matching between the text information in the preset text database is higher than a preset matching threshold, the trading risk category to which the matching text information belongs is determined as the trading risk category to which the key text information belongs.

[0094] It can be seen that the big data-based bond trading risk warning method of this embodiment performs retrieval and processing through key information in text information. Compared with matching retrieval using the entire text information, key information retrieval is conducive to improving recognition efficiency.

[0095] Figure 8 FIG. 1 is a block diagram of a bond trading risk warning device based on big data, shown in an exemplary embodiment of the present application. Figure 8 As shown, the exemplary big data-based bond trading risk warning device 800 includes a matching module 810, a determination module 820 and a display module 830, specifically:

[0096] The matching module 810 is configured to respond to the target object's click operation on the bond trading interface and select risk warning information that matches the trading risk category of the clicked bond from a preset risk warning information library. The clicked bond is obtained based on the target object's click operation on the bond displayed in the bond trading interface.

[0097] The determination module 820 is configured to perform information extraction processing on the matched risk warning information to determine key information included in the matched risk warning information.

[0098] The display module 830 is configured to highlight key information included in the matching risk warning information.

[0099] In another exemplary embodiment, the determination module 820 includes a candidate word extraction module, a key probability calculation module and a key information matching module, specifically:

[0100] The candidate word extraction module is configured to extract candidate words contained in the matching risk warning information.

[0101] The key probability calculation module is configured to calculate the key probability of the candidate word in the matching risk warning information.

[0102] The key information matching module is configured to use the corresponding candidate word as the key information contained in the matched risk warning information if the key probability is greater than a preset key probability threshold.

[0103] In another exemplary embodiment, the key probability calculation module includes a feature vector extraction module, a feature vector similarity calculation module and a module, specifically:

[0104] The feature vector extraction module is configured to extract feature vectors of candidate words.

[0105] The feature vector similarity calculation module is configured to calculate the feature vector similarity between the feature vector of the candidate word and the feature vector of the keyword contained in the preset storage key information library.

[0106] As a module, it is configured to use the ratio between the feature vector similarity and the preset feature vector value as the key probability of the candidate word in the matching risk warning information.

[0107] In another exemplary embodiment, before the matching module 810, the method further includes distinguishing the display modules, specifically:

[0108] The differentiated display module is configured to differentiate the bonds displayed in the bond trading interface according to different risk levels based on the risk levels corresponding to the transaction risk categories of the bonds displayed in the bond trading interface.

[0109] In another exemplary embodiment, the transaction risk category includes: prohibition of trading bonds, and the distinguishing display module includes a risk level determination module and an exclamation mark adding identification module, specifically:

[0110] The risk level determination module is configured to determine the risk level corresponding to the prohibited trading bonds according to the category similarity between the prohibited trading bonds and the preset trading risk category.

[0111] An exclamation mark identification module is added, and it is configured that if the risk level of the prohibited trading bond is determined to be the highest risk level, an exclamation mark logo is added at the preset position of the prohibited trading bond when the corresponding bond is displayed on the bond trading interface.

[0112] In another exemplary embodiment, the key information includes at least two transaction risk categories. After the display module 1030, the method further includes a highlighting module for key words, specifically:

[0113] The keyword highlighting module is configured to display risk warning information corresponding to the transaction risk category in response to the target object's selection operation on at least two highlighted transaction risk categories, and highlight the keyword in the risk warning information.

[0114] In another exemplary embodiment, before the matching module 810, the method further includes a text information acquisition module, a key text information determination module and a retrieval module, specifically:

[0115] The text information acquisition module is configured to acquire the text information contained in the bonds displayed in the bond trading interface.

[0116] The key text information determination module is configured to perform recognition processing on the text information to obtain the key text information contained in the text information.

[0117] The retrieval module is configured to perform retrieval processing using key text information and determine the corresponding transaction risk category based on the obtained retrieval results.

[0118] It should be noted that the device provided in the above embodiment and the method provided in the above embodiment belong to the same concept, wherein the specific manner in which each module and unit performs the operation has been described in detail in the method embodiment and will not be repeated here.

[0119] In another exemplary embodiment, the present application provides an electronic device including a processor and a memory, wherein the memory stores computer-readable instructions, which, when executed by the processor, implement the above-mentioned big data-based bond trading risk warning method.

[0120] Fig. 9 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0121] It should be noted that Fig. 9 The computer system 900 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0122] like Fig. 9 As shown, the computer system 900 includes a central processing unit (CPU) 901, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 902 or the program loaded from the storage part 908 to the random access memory (RAM) 903, such as executing the information recommendation method in the above embodiment. In RAM 903, various programs and data required for system operation are also stored. CPU 901, ROM 902 and RAM 903 are connected to each other through bus 904. Input / output (I / O) interface 905 is also connected to bus 904.

[0123] The following components are connected to the I / O interface 905: an input section 906 including a keyboard, a mouse, etc.; an output section 907 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the I / O interface 905 as needed. A removable medium 911, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 910 as needed so that a computer program read therefrom is installed into the storage section 908 as needed.

[0124] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 909, and / or installed from a removable medium 911. When the computer program is executed by a central processing unit (CPU) 901, various functions defined in the system of the present application are executed.

[0125] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by an instruction execution system, device or device or used in combination with it. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0126] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0127] Another aspect of the present application provides a computer-readable storage medium having computer-readable instructions stored thereon, which, when executed by a processor, implements a bond trading risk warning method based on big data as in any of the previous embodiments.

[0128] Another aspect of the present application also provides a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the bond trading risk warning method based on big data provided in each of the above embodiments.

[0129] The above content is only a preferred exemplary embodiment of the present application and is not intended to limit the implementation scheme of the present application. A person skilled in the art can easily make corresponding changes or modifications based on the main concept and spirit of the present application. Therefore, the scope of protection of the present application shall be based on the scope of protection required by the claims.

Claims

1. A bond trading risk warning method based on big data, characterized in that: The method comprises: In response to a target object's click operation on a bond trading interface, selecting risk warning information matching a transaction risk category of a clicked bond from a preset risk warning information library, wherein the clicked bond is obtained based on the target object's click operation on a bond displayed in the bond trading interface; Performing information extraction processing on the matched risk warning information to determine key information contained in the matched risk warning information; Highlight the key information contained in the matching risk warning information; The step of performing information extraction processing on the matched risk warning information to determine key information contained in the matched risk warning information includes: Extracting candidate words contained in the matching risk warning information; Calculating the key probability of the candidate word in the matching risk warning information; If the key probability is greater than a preset key probability threshold, the corresponding candidate word is used as the key information included in the matching risk warning information; The step of calculating the key probability of the candidate word in the matching risk warning information includes: Extracting a feature vector of the candidate word; Calculating the feature vector similarity between the feature vector of the candidate word and the feature vector of the keyword contained in the preset storage key information library; The ratio between the feature vector similarity and the preset feature vector value is used as the key probability of the candidate word in the matching risk warning information.

2. The method according to claim 1, characterized in that Before the step of selecting risk warning information matching the transaction risk category of the clicked bond from a preset risk warning information library in response to the target object's click operation on the bond trading interface, the method further includes: Based on the risk levels corresponding to the trading risk categories of the bonds displayed in the bond trading interface, the bonds displayed in the bond trading interface are displayed differently according to different risk levels.

3. The method according to claim 2, characterized in that The transaction risk category includes: prohibiting the trading of bonds. The step of displaying the bonds displayed in the bond trading interface according to different risk levels based on the risk levels corresponding to the transaction risk categories of the bonds displayed in the bond trading interface includes: Determining the risk level corresponding to the prohibited trading bond according to the category similarity between the prohibited trading bond and the preset trading risk category; If it is determined that the risk level of the prohibited-trading bond is the highest risk level, an exclamation mark is added at a preset position of the prohibited-trading bond when the corresponding bond is displayed on the bond trading interface.

4. The method according to claim 1, characterized in that: The key information includes at least two transaction risk categories. After the step of highlighting the key information included in the matched risk warning information, the method further includes: In response to the target object's selection operation on at least two highlighted transaction risk categories, risk warning information corresponding to the transaction risk categories is displayed, and key words and phrases in the risk warning information are highlighted.

5. The method according to claim 1, characterized in that Before the step of selecting risk warning information matching the transaction risk category of the clicked bond from a preset risk warning information library in response to the target object's click operation on the bond trading interface, the method further includes: Obtaining text information contained in the bonds displayed in the bond trading interface; Performing recognition processing on the text information to obtain key text information contained in the text information; The key text information is used to perform a search process, and the transaction risk category of the corresponding bond is determined based on the search results obtained.

6. A bond trading risk warning device based on big data, characterized in that: The device comprises: a matching module, configured to select, in response to a target object's click operation on a bond trading interface, risk warning information matching a transaction risk category of a clicked bond from a preset risk warning information library, wherein the clicked bond is obtained based on the target object's click operation on a bond displayed in the bond trading interface; A determination module configured to perform information extraction processing on the matched risk warning information to determine key information contained in the matched risk warning information; A display module configured to highlight key information contained in the matched risk warning information; The determination module includes a candidate word extraction module, a key probability calculation module and a key information matching module; The candidate word extraction module is configured to extract candidate words contained in the matching risk warning information; The key probability calculation module is configured to calculate the key probability of the candidate word in the matching risk warning information; The key information matching module is configured to use the corresponding candidate word as the key information included in the matched risk warning information if the key probability is greater than a preset key probability threshold; The key probability calculation module includes a feature vector extraction module, a feature vector similarity calculation module and a serving module; the feature vector extraction module is configured to extract the feature vector of the candidate word; the feature vector similarity calculation module is configured to calculate the feature vector similarity between the feature vector of the candidate word and the feature vector of the keyword contained in the preset key information library; the serving module is configured to use the ratio between the feature vector similarity and the preset feature vector value as the key probability of the candidate word in the matching risk warning information.

7. An electronic device, characterized in that: include: a memory storing computer-readable instructions; A processor reads the computer-readable instructions stored in the memory to execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: Computer-readable instructions are stored thereon, and when the computer-readable instructions are executed by a processor of a computer, the computer is caused to execute the method according to any one of claims 1 to 5.

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