Risk information display method, device, equipment, medium and program product

Abstract

Embodiments of the present disclosure disclose a risk information display method, device, equipment, medium and program product. A specific embodiment of the method comprises: in response to adjustment of target configuration content on a configuration page, obtaining target configuration adjustment data; retrieving code information corresponding to the target configuration content; obtaining configuration detail content corresponding to the target configuration content; according to the target configuration adjustment data, the code information and the configuration detail content, generating first configuration adjustment risk information by using a pre-trained large language model; and displaying the first configuration adjustment risk information on the configuration page in a target display mode. This embodiment is related to a large language model, and when configuration adjustment data has not been submitted on the configuration page, the large language model can be used to accurately and efficiently detect configuration adjustment risks and handle potential risks in a timely manner according to relevant code information and configuration detail content.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of large language model technology, specifically to risk information display methods, apparatus, devices, media, and program products. Background Technology

[0002] Currently, in the internet finance and e-commerce sectors, operations teams adjust configuration settings to ensure the effective execution of activities such as campaign implementation and optimization. Risk detection for these configuration adjustments typically relies on pre-configured risk detection middleware to detect and promptly warn of risks after submission. However, by the time the middleware detects a risk, it may have already caused significant losses. Therefore, identifying potential risks before configuration submission to prevent substantial losses has become an urgent need. Summary of the Invention

[0003] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0004] Some embodiments of this disclosure provide risk information display methods, apparatuses, electronic devices, computer-readable media, and program products to address the technical problems mentioned in the background section above.

[0005] In a first aspect, some embodiments of this disclosure provide a method for displaying risk information, including: in response to adjusting target configuration content on a configuration page, obtaining target configuration adjustment data; retrieving code information corresponding to the target configuration content; obtaining configuration details corresponding to the target configuration content; generating first configuration adjustment risk information using a pre-trained large language model based on the target configuration adjustment data, the code information, and the configuration details; and displaying the first configuration adjustment risk information on the configuration page in a target display manner.

[0006] Optionally, the above-mentioned retrieval of code information corresponding to the target configuration content includes: retrieving code information corresponding to the target configuration content using at least one of a link retrieval method for code graphs and a retrieval method for code vectors.

[0007] Optionally, obtaining the configuration details corresponding to the target configuration content includes: using a cross-platform content recognition tool to obtain the cross-platform configuration details corresponding to the target configuration content.

[0008] Optionally, the aforementioned first configuration adjustment risk information includes at least one of the following: value loss risk information, configuration rationality risk information, program compatibility risk information, and user experience risk information; and the generation of the first configuration adjustment risk information using a pre-trained large language model based on the aforementioned target configuration adjustment data, the aforementioned code information, and the aforementioned configuration details includes: generating risk prediction prompts for the aforementioned target configuration adjustment data, the aforementioned code information, and the aforementioned configuration details; and inputting the aforementioned risk prediction prompts into the aforementioned large language model to obtain at least one of the following: value loss risk information, configuration rationality risk information, program compatibility risk information, and user experience risk information.

[0009] Optionally, the above-mentioned method of retrieving code information corresponding to the target configuration content using at least one of the link retrieval method for code graphs and the retrieval method for code vectors includes: retrieving first candidate code information corresponding to the target configuration content using the link retrieval method; retrieving second candidate code information from the code vector library whose similarity information with the key information corresponding to the target configuration content satisfies the target similarity condition; retrieving third candidate code information from the code vector library whose similarity information with the target configuration content satisfies the target similarity condition; and generating the code information based on the first candidate code information, the second candidate code information, and the third candidate code information.

[0010] Optionally, the above method further includes: in response to inputting risk adjustment information and configuration adjustment request information regarding the first configuration adjustment risk information on the configuration page, generating a configuration adjustment result for the target configuration content using the large language model based on the target configuration content, the code information, and the configuration-related information; generating second configuration adjustment risk information for the configuration adjustment result using the large language model based on the configuration adjustment result, the code information, and the configuration-related information; and displaying the configuration adjustment result and the second configuration adjustment risk information on the configuration page in the target display manner.

[0011] Secondly, some embodiments of this disclosure provide a risk information display device, including: a first acquisition unit configured to acquire target configuration adjustment data in response to adjusting target configuration content on a configuration page; a retrieval unit configured to retrieve code information corresponding to the target configuration content; a second acquisition unit configured to acquire configuration details corresponding to the target configuration content; a generation unit configured to generate first configuration adjustment risk information using a pre-trained large language model based on the target configuration adjustment data, the code information, and the configuration details; and a display unit configured to display the first configuration adjustment risk information on the configuration page in a target display manner.

[0012] Optionally, the retrieval unit can be configured to retrieve code information corresponding to the target configuration content using at least one of a link retrieval method for code graphs and a retrieval method for code vectors.

[0013] Optionally, the second acquisition unit can be configured to: use a cross-platform content recognition tool to acquire the cross-platform configuration details corresponding to the target configuration content.

[0014] Optionally, the aforementioned first configuration adjustment risk information includes at least one of the following: value loss risk information, configuration rationality risk information, program compatibility risk information, and user experience risk information; and the generation unit can be configured to: generate risk prediction prompt information for the aforementioned target configuration adjustment data, the aforementioned code information, and the aforementioned configuration details; input the aforementioned risk prediction prompt information into the aforementioned large language model to obtain at least one of the following: value loss risk information, configuration rationality risk information, program compatibility risk information, and user experience risk information.

[0015] Optionally, the retrieval unit can be configured to: retrieve first candidate code information corresponding to the target configuration content using the above-described link retrieval method; retrieve second candidate code information from the code vector library whose similarity information with the key information corresponding to the target configuration content satisfies the target similarity condition; retrieve third candidate code information from the code vector library whose similarity information with the target configuration content satisfies the target similarity condition; and generate the above-described code information based on the first candidate code information, the second candidate code information, and the third candidate code information.

[0016] Optionally, the device further includes: in response to inputting risk adjustment information and configuration adjustment request information regarding the first configuration adjustment risk information on the configuration page, generating a configuration adjustment result for the target configuration content using the large language model based on the target configuration content, the code information, and the configuration-related information; generating second configuration adjustment risk information for the configuration adjustment result using the large language model based on the configuration adjustment result, the code information, and the configuration-related information; and displaying the configuration adjustment result and the second configuration adjustment risk information on the configuration page in the target display manner.

[0017] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, such that when the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any implementation of the first aspect.

[0018] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method as described in any implementation of the first aspect.

[0019] Fifthly, some embodiments of this disclosure provide a computer program product, including a computer program that, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0020] The various embodiments of this disclosure have the following beneficial effects: Through the risk information display method of some embodiments of this disclosure, before the configuration adjustment data is submitted on the configuration page, the risk of configuration adjustment can be accurately and efficiently detected based on the relevant code information and configuration details, and potential risks can be handled in a timely manner using a large language model. Specifically, the reason for the effective and timely handling of related potential risks is that risk detection middleware is used to detect risks in the configuration adjustment content. If a risk is detected, it may have already caused significant losses. Based on this, the risk information display method of some embodiments of this disclosure firstly obtains the target configuration adjustment data in response to the adjustment of the target configuration content on the configuration page. Here, by obtaining the target configuration adjustment data, it is possible to subsequently determine whether there is a risk after the configuration content is adjusted, and to filter out the corresponding code information and configuration-related information. Then, the code information corresponding to the target configuration content is retrieved so that the large language model can subsequently perform risk detection of the configuration adjustment data based on the code logic corresponding to the code information, ensuring the accuracy of risk detection. Next, the configuration details corresponding to the target configuration content are obtained to fully acquire the descriptive information related to the target configuration content, enabling the large language model to learn more configuration information related to the target configuration content. Then, based on the target configuration adjustment data, the code information, and the configuration details, the semantic understanding capability of the pre-trained large language model can accurately generate the first configuration adjustment risk information. Finally, the first configuration adjustment risk information is displayed on the configuration page in a target-oriented manner to inform users of the risk situation corresponding to the target configuration adjustment data, thus achieving effective risk warning before the target configuration adjustment data is submitted. In summary, by utilizing the large language model and determining the code logic corresponding to the target configuration content, potential risks can be accurately predicted and handled in a timely manner before the target configuration adjustment data is submitted. Attached Figure Description

[0021] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0022] Figure 1 This is a schematic diagram illustrating an application scenario of a risk information display method according to some embodiments of this disclosure; Figure 2 These are flowcharts of some embodiments of the risk information display method according to this disclosure; Figure 3 These are flowcharts of other embodiments of the risk information display method according to this disclosure; Figure 4 This is a schematic diagram of a page showing the first configuration adjustment of risk information, as illustrated in some embodiments of the risk information display method according to this disclosure; Figure 5 These are schematic diagrams illustrating the structure of some embodiments of the risk information display device according to this disclosure; Figure 6 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0023] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0024] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0025] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0026] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0027] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0028] Before performing any of the operations involving the collection, storage, or use of personal information (such as target configuration content) disclosed in this disclosure, the relevant organizations or individuals shall fulfill their obligations, including conducting personal information security impact assessments, informing personal information subjects, and obtaining prior authorization and consent from personal information subjects.

[0029] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0030] Figure 1 This is a schematic diagram of an application scenario of a risk information display method according to some embodiments of the present disclosure.

[0031] exist Figure 1 In this application scenario, firstly, electronic device 101 can respond to adjustments made to the target configuration content 103 on the configuration page 102 and obtain target configuration adjustment data 104. In this application scenario, the target configuration content 103 can be "whiteCrowId field: d***e-1***7-4***4-9***5-0***d". The target configuration adjustment data 104 can be "for the whiteCrowId field, change from d***e-1***7-4***4-9***5-0***d to d***d-1***1-4***5-b***0". Then, electronic device 101 can retrieve the code information 105 corresponding to the above target configuration content 103. In this application scenario, the code information 105 can be "code logic information". Next, electronic device 101 can obtain the configuration details content 106 corresponding to the above target configuration content 103. In this application scenario, the configuration details content 106 can be "field description", "field purpose", "field value", and "field usage scenario". Furthermore, the electronic device 101 can generate first configuration adjustment risk information 108 based on the aforementioned target configuration adjustment data 104, the aforementioned code information 105, and the aforementioned configuration details 106, using a pre-trained large language model 107. In this application scenario, the first configuration adjustment risk information 108 could be "whitelist failure leading to rule matching failure, impacting the risk of financial loss." Finally, the electronic device 101 can display the first configuration adjustment risk information 108 on the aforementioned configuration page 102 in a target display manner.

[0032] It should be noted that the aforementioned electronic device 101 can be either hardware or software. When the electronic device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or as a single server or a single terminal device. When the electronic device is software, it can be installed in the hardware devices listed above. It can be implemented as, for example, multiple software programs or software modules used to provide distributed services, or as a single software program or software module. No specific limitations are made here.

[0033] It should be understood that Figure 1 The number of electronic devices shown is merely illustrative. Any number of electronic devices can be used depending on the implementation requirements.

[0034] Continue to refer to Figure 2 The flowchart 200 illustrates some embodiments of a risk information display method according to the present disclosure. The risk information display method includes the following steps: Step 201: In response to adjustments made to the target configuration content on the configuration page, obtain the target configuration adjustment data.

[0035] In some embodiments, in response to adjustments to the target configuration content on the configuration page, the entity executing the aforementioned risk information display method (e.g., Figure 1 The electronic device 101 shown can acquire target configuration adjustment data via wired or wireless connection. The configuration page can be a page for various processing of configuration content. In practice, configuration content can be business rules or business fields configured by relevant operational personnel according to relevant needs. For example, configuration content can be, but is not limited to, one of the following: discount level, activity time, permission scope. Various processing can include, but is not limited to, at least one of the following: add processing, change processing, deletion processing. The target configuration content can be the configuration content currently being adjusted (e.g., the configuration content currently being adjusted for business rules). Here, adjusting the target configuration content on the configuration page can be, but is not limited to, one of the following: changing the target configuration content, adding the target configuration content, or deleting the target configuration content. Target configuration adjustment data can represent the summary data of adjustments made to the target configuration content. In practice, target configuration adjustment data can include: the target configuration content and the configuration content after adjustment. For example, as... Figure 1 As shown, for the target configuration content "whiteCrowId field: d***e-1***7-4***4-9***5-***d", the corresponding adjusted configuration content is "whiteCrowId field: d***d-1***1-4***5-b***0".

[0036] Step 202: Retrieve the code information corresponding to the target configuration content mentioned above.

[0037] In some embodiments, the execution entity may retrieve code information corresponding to the target configuration content. This code information may be source file code. Specifically, the code information corresponding to the target configuration content may be source file code whose code logic is related to the target configuration content. This code information can characterize the underlying execution logic corresponding to the target configuration content.

[0038] As an example, the aforementioned execution entity can query the code information corresponding to the target configuration content based on the maintained mapping table between configuration content and code. The mapping table between configuration content and code represents the logical relationship between the configuration content and the code.

[0039] In some optional implementations of certain embodiments, the aforementioned execution entity can utilize at least one of a link retrieval method for code graphs and a retrieval method for code vectors to retrieve code information corresponding to the target configuration content. The code graph can be a graph representing the relationships between codes, where vertices are code elements and edges represent the relationships between elements. The graph can be stored using a graph database (e.g., Neo4j, JanusGraph) to support efficient traversal (e.g., "finding all business entry points that call calculateDiscount()"). The link retrieval method can be a method of retrieving code from the code graph by identifying the usage path and scope of influence of the configuration content in the code. Code vectors can be information obtained by vectorizing code blocks. For example, vectors obtained by embedding code blocks. Code vectors can represent the characteristic semantic content of the code logic content corresponding to the code block. The retrieval method for code vectors can be a retrieval method that retrieves code information with the most similar semantic content based on the vector similarity (e.g., cosine similarity) between code vectors. In practice, for each piece of code information, the transformed code vectors can be stored in a code vector library, allowing for subsequent retrieval of the most semantically similar code information using vector retrieval. As an example, a code graph can be generated by scanning the code's AST (Abstract Syntax Tree).

[0040] Optionally, the aforementioned execution entity may utilize at least one of the link retrieval method for code graphs and the retrieval method for code vectors to retrieve the code information corresponding to the aforementioned target configuration content, including the following steps: The first step is to use the link retrieval method described above to retrieve the first candidate code information corresponding to the target configuration content. The first candidate code information can be the alternative code information obtained from link retrieval in the code graph.

[0041] The second step involves retrieving candidate code information from the code vector library that shares similarity with the key information corresponding to the target configuration content, satisfying the target similarity condition. The key information can be information located within the target configuration content. In practice, key information can be keywords. For example, key information could be "change" and "configuration field A". Similarity information can be the similarity between the semantic content corresponding to the key information and the semantic content corresponding to the target configuration content. In practice, similarity information can be the cosine similarity between vectors. Similarity information can be a value between 0 and 1; the higher the value, the more similar the corresponding semantic content. The target similarity condition can be the candidate code information with the highest corresponding similarity information.

[0042] The third step involves retrieving third candidate code information from the code vector library that satisfies the similarity criteria to the target configuration content. This similarity information can be the content similarity between the semantic content corresponding to the code vector and the semantic content of the overall data corresponding to the target configuration adjustment data.

[0043] The fourth step is to generate the code information based on the first candidate code information, the second candidate code information, and the third candidate code information.

[0044] As an example, the aforementioned executing entity can determine the candidate code information that appears most frequently among the first, second, and third candidate code information as the code information. If the frequencies are the same, then the first candidate code information is determined as the code information.

[0045] Step 203: Obtain the configuration details corresponding to the target configuration content mentioned above.

[0046] In some embodiments, the aforementioned executing entity may obtain the configuration details corresponding to the target configuration content through limited or wireless means. The configuration details may be the configuration details information corresponding to the target configuration content. That is, the configuration details may include: information describing the configuration fields or rules, configuration application status, and information on any problems encountered during configuration.

[0047] As an example, the aforementioned execution entity can retrieve the configuration details corresponding to the target configuration content from the configuration content database. The configuration content database can be a database that stores the integrated data corresponding to the configuration content.

[0048] In some optional implementations of certain embodiments, the aforementioned execution entity can utilize a cross-platform content recognition tool to obtain the cross-platform configuration details corresponding to the target configuration content. The cross-platform content recognition tool can be a tool that identifies the details corresponding to the target configuration content in a cross-platform manner. Furthermore, the cross-platform content recognition tool can integrate with middleware services to dynamically identify and obtain the cross-platform content corresponding to a modified ID, establishing a cross-system configuration consistency check mechanism to prevent configuration errors.

[0049] Step 204: Based on the target configuration adjustment data, the code information, and the configuration details, generate the first configuration adjustment risk information using a pre-trained large language model.

[0050] In some embodiments, the aforementioned execution entity can generate first configuration adjustment risk information based on the aforementioned target configuration adjustment data, the aforementioned code information, and the aforementioned configuration details, using a pre-trained large language model. The large language model can be a general-trained large language model based on configuration-related knowledge, or a large language model specifically trained on a risk training dataset. The risk training data may include: configuration-related data, code information, configuration details, and actual risk content. In practice, the large language model can be a commercially available, conventional large language model. For example, the large language model can be a large language model based on the Transformer architecture. The first configuration adjustment risk information can be the risk situation existing in adjusting the target configuration content. For example, the first configuration adjustment risk information may include: a risk score and a specific reason for the risk. A higher risk score indicates a higher probability of risk resulting from the application after adjusting the configuration content. The specific reason for the risk can be the cause of the risk arising from adjusting the target configuration content.

[0051] Step 205: Display the first configuration adjustment risk information on the configuration page in a target display manner.

[0052] In some embodiments, the aforementioned implementing entity may display the first configuration adjustment risk information in a targeted display manner on the aforementioned configuration page. The targeted display manner may be a pre-set display method for risk content. In practice, the targeted display method may be, but is not limited to, one of the following: drawer-style display, pop-up display, or floating prompt display.

[0053] In practice, it can be provided as a browser plugin to support non-intrusive access, or it can be integrated into the SDK via hard code to dynamically obtain the content of the form before and after modification and report it to display the risk information of the first configuration adjustment.

[0054] In some optional implementations of certain embodiments, after step 205, the steps further include: The first step involves responding to the input of risk adjustment information and configuration adjustment request information regarding the first configuration adjustment risk information on the configuration page. Based on the target configuration content, the code information, and configuration-related information, the large language model is used to generate a configuration adjustment result for the target configuration content. The risk adjustment information can be information determining how to handle the risk content reflected in the first configuration adjustment risk information. For example, the risk adjustment information could be "eliminate the potential risks present in the first configuration adjustment risk information." The configuration adjustment request information can be a request to adjust the target configuration content. For example, the adjustment request information could be changing the discount of the target item from 70% to 60%. The configuration adjustment result can be the result after adjusting the target configuration content. Here, the configuration adjustment result can be the result output by the large language model after rationally adjusting the target configuration content. For example, for a target configuration content of "whiteCrowId field: d***e-1***7-4***4-9***5-0***d", the corresponding configuration adjustment result is "for the whiteCrowId field, change from d***e-1***7-4***4-9***5-0***d to d***d-1***1-4***5-b***3".

[0055] As an example, firstly, adjustment prompts (i.e., prompt words) are generated to adjust the target configuration content based on the aforementioned target configuration content, the aforementioned code information, and configuration-related information. Then, the aforementioned adjustment prompts are input into the aforementioned large language model to obtain the configuration adjustment result.

[0056] The second step involves generating second configuration adjustment risk information based on the aforementioned configuration adjustment results, code information, and configuration-related information, using the aforementioned large language model. This second configuration adjustment risk information can represent the risks inherent in the configuration adjustment results.

[0057] The third step is to display the configuration adjustment results and the second configuration adjustment risk information on the configuration page in the manner described above.

[0058] The various embodiments of this disclosure have the following beneficial effects: Through the risk information display method of some embodiments of this disclosure, before the configuration adjustment data is submitted on the configuration page, the risk of configuration adjustment can be accurately and efficiently detected based on the relevant code information and configuration details, and potential risks can be handled in a timely manner using a large language model. Specifically, the reason for the effective and timely handling of related potential risks is that risk detection middleware is used to detect risks in the configuration adjustment content. If a risk is detected, it may have already caused significant losses. Based on this, the risk information display method of some embodiments of this disclosure firstly obtains the target configuration adjustment data in response to the adjustment of the target configuration content on the configuration page. Here, by obtaining the target configuration adjustment data, it is possible to subsequently determine whether there is a risk after the configuration content is adjusted, and to filter out the corresponding code information and configuration-related information. Then, the code information corresponding to the target configuration content is retrieved so that the large language model can subsequently perform risk detection of the configuration adjustment data based on the code logic corresponding to the code information, ensuring the accuracy of risk detection. Next, the configuration details corresponding to the target configuration content are obtained to fully acquire the descriptive information related to the target configuration content, enabling the large language model to learn more configuration information related to the target configuration content. Then, based on the target configuration adjustment data, the code information, and the configuration details, the semantic understanding capability of the pre-trained large language model can accurately generate the first configuration adjustment risk information. Finally, the first configuration adjustment risk information is displayed on the configuration page in a target-oriented manner to inform users of the risk situation corresponding to the target configuration adjustment data, thus achieving effective risk warning before the target configuration adjustment data is submitted. In summary, by utilizing the large language model and determining the code logic corresponding to the target configuration content, potential risks can be accurately predicted and handled in a timely manner before the target configuration adjustment data is submitted.

[0059] Further reference Figure 3 The diagram illustrates a flow 300 of another embodiment of the risk information display method according to the present disclosure. This risk information display method includes the following steps: Step 301: In response to the adjustment of the target configuration content on the configuration page, obtain the target configuration adjustment data.

[0060] Step 302: Retrieve the code information corresponding to the target configuration content mentioned above.

[0061] Step 303: Obtain the configuration details corresponding to the target configuration content mentioned above.

[0062] In some embodiments, the specific implementation of steps 301-303 and the resulting technical effects can be found in [reference needed]. Figure 2Steps 201-203 in the corresponding embodiments will not be repeated here.

[0063] Step 304: Generate risk prediction and prompt information for the above target configuration adjustment data, the above code information, and the above configuration details.

[0064] In some embodiments, the execution entity (e.g. Figure 1 The electronic device 101 shown can generate risk prediction and warning information regarding the above-mentioned target configuration adjustment data, code information, and configuration details. The first configuration adjustment risk information includes at least one of the following: value loss risk information, configuration rationality risk information, program compatibility risk information, and user experience risk information. Value loss risk information can be financial loss risk information. For example, value loss risk information could be: after the reward ratio is adjusted from 1:1 to 1:3, the platform needs to significantly increase the reward amount -> if cost control or rule verification is not done well, it may lead to "excessive financial consumption," thus being judged as "extremely high risk." Configuration rationality risk information can be risk information regarding the rationality of business logic. For example, configuration rationality risk information could be: after the ratio adjustment, the new rules (such as "the matching relationship between reward scenarios, triggering conditions, and ratios") conform to the company's established business logic (such as the design intent of activity rules and the rights system) -> there are no problems such as "rule contradictions or logical loopholes," therefore the risk is low. Program compatibility risk information can be risk information regarding whether the system / code can adapt to the changes. For example, program compatibility risk information could be: the "format" of the ratio (such as numerical representation and transmission rules) has not changed, and the existing code can correctly recognize and process the new ratio -> there will be no technical failures such as "program errors" or "data parsing failures", therefore the risk is low. User experience risk information can characterize users' feelings and satisfaction with the product. User experience risk information could be: after the reward ratio is increased, users can get more rewards -> this is a "positive incentive" for users and will not cause negative experiences such as "increased operational complexity" or "decreased perceived benefits", therefore the risk is low. Risk prediction prompts can be prompts using at least one of the following: value loss risk information generated by a large language model, configuration rationality risk information, program compatibility risk information, and user experience risk information.

[0065] As an example, the aforementioned executing entity can add the aforementioned target configuration adjustment data, the aforementioned code information, and the aforementioned configuration details to the risk prediction prompt template to obtain risk prediction prompt information.

[0066] Step 305: Input the above risk prediction prompts into the above large language model to obtain at least one of the following: value loss risk information, configuration rationality risk information, program compatibility risk information, and user experience risk information.

[0067] In some embodiments, the aforementioned executing entity may input the aforementioned risk prediction prompt information into the aforementioned large language model to obtain at least one of the following: value loss risk information, configuration rationality risk information, program compatibility risk information, and user experience risk information.

[0068] like Figure 4 As shown, a schematic diagram of the page displaying the first configuration adjustment risk information is presented.

[0069] The configuration adjustment risk information can be: {Difference item: The field skuConfigDtos.[0].lotteryTicketNum (lottery product) has been modified, with the original value changed from 1 to 5. Overall risk level: medium risk}. For the impact dimension of financial loss risk, the corresponding risk level is "medium risk," and the corresponding reason is described as "the number of lottery tickets issued has increased from 1 to 5, which may increase the risk of financial loss." For the impact dimension of rationality analysis impact, the corresponding risk level is "low risk," and the corresponding reason is described as "the modified value is reasonable, but it needs further confirmation whether it matches the prize issuance limit." For the impact dimension of program compatibility impact, the corresponding risk level is "low risk," and the corresponding reason is described as "the value type has not changed, and there are no compatibility issues." For the impact dimension of user experience impact, the corresponding risk level is "low risk," and the corresponding reason is described as "users receive more lottery tickets, improving the experience."

[0070] from Figure 3 It can be seen from this that, with Figure 2 Compared to the description of some corresponding embodiments, Figure 3 In some corresponding embodiments, the process 300 of the risk information display method utilizes a large language model and corresponding risk prediction prompts to accurately generate diverse risk content for risk prevention from multiple perspectives.

[0071] Further reference Figure 5 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of a risk information display device, which are similar to... Figure 2 Corresponding to the method embodiments shown, this risk information display device can be specifically applied to various electronic devices.

[0072] like Figure 5As shown, a risk information display device 500 includes: a first acquisition unit 501, a retrieval unit 502, a third acquisition unit 503, a generation unit 504, and a display unit 505. The first acquisition unit 501 is configured to acquire target configuration adjustment data in response to adjustments to target configuration content on a configuration page; the retrieval unit 502 is configured to retrieve code information corresponding to the target configuration content; the second acquisition unit 503 is configured to acquire configuration details corresponding to the target configuration content; the generation unit 504 is configured to generate first configuration adjustment risk information using a pre-trained large language model based on the target configuration adjustment data, the code information, and the configuration details; and the display unit 505 is configured to display the first configuration adjustment risk information on the configuration page in a target display manner.

[0073] In some optional implementations of some embodiments, the retrieval unit 502 may be further configured to: retrieve code information corresponding to the target configuration content using at least one of a link retrieval method for code graphs and a retrieval method for code vectors.

[0074] In some optional implementations of some embodiments, the second acquisition unit 503 may be further configured to: use a cross-platform content recognition tool to acquire the cross-platform configuration details corresponding to the target configuration content.

[0075] In some optional implementations of certain embodiments, the first configuration adjustment risk information mentioned above includes at least one of the following: value loss risk information, configuration rationality risk information, program compatibility risk information, and user experience risk information; and the generation unit 504 may be further configured to: generate risk prediction prompt information for the target configuration adjustment data, the code information, and the configuration details; input the risk prediction prompt information into the large language model to obtain at least one of the value loss risk information, configuration rationality risk information, program compatibility risk information, and user experience risk information.

[0076] In some optional implementations of some embodiments, the retrieval unit 502 may be further configured to: retrieve first candidate code information corresponding to the target configuration content using the link retrieval method; retrieve second candidate code information from the code vector library whose similarity information with the key information corresponding to the target configuration content satisfies the target similarity condition; retrieve third candidate code information from the code vector library whose similarity information with the target configuration content satisfies the target similarity condition; and generate the code information based on the first candidate code information, the second candidate code information, and the third candidate code information.

[0077] In some optional implementations of certain embodiments, the apparatus 500 further includes a result generation unit, an information generation unit, and an information display unit (not shown in the figure). The result generation unit can be configured to: in response to inputting risk adjustment information and configuration adjustment request information regarding the first configuration adjustment risk information on the configuration page, generate a configuration adjustment result for the target configuration content using the large language model, based on the target configuration content, the code information, and the configuration-related information. The information generation unit can be configured to: generate second configuration adjustment risk information regarding the configuration adjustment result using the large language model, based on the configuration adjustment result, the code information, and the configuration-related information. The information display unit can be configured to: display the configuration adjustment result and the second configuration adjustment risk information on the configuration page in the target display manner.

[0078] It is understandable that the units recorded in the risk information display device 500 are related to the reference. Figure 2 The steps in the described method correspond accordingly. Therefore, the operations, features, and beneficial effects described above for the method also apply to the risk information display device 500 and the units contained therein, and will not be repeated here.

[0079] The following is for reference. Figure 6 It illustrates electronic devices suitable for implementing some embodiments of this disclosure (e.g., Figure 1 A schematic diagram of the structure of electronic device 101)600 in the middle. Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0080] like Figure 6 As shown, the electronic device 600 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory 602 or a program loaded from a storage device 608 into a random access memory 603. The random access memory 603 also stores various programs and data required for the operation of the electronic device 600. The processing unit 601, the read-only memory 602, and the random access memory 603 are interconnected via a bus 604. An input / output interface 605 is also connected to the bus 604.

[0081] Typically, the following devices can be connected to the input / output interface 605: input devices 606 including, for example, a touchscreen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 607 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 608 including, for example, magnetic tape, hard disk, etc.; and communication devices 609. Communication device 609 allows electronic device 600 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 An electronic device 600 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 6 Each box shown can represent a device or multiple devices as needed.

[0082] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a storage device 608, or installed from a read-only memory 602. When the computer program is executed by the processing device 601, it performs the functions defined above in the methods of some embodiments of this disclosure.

[0083] It should be noted that, in some embodiments of this disclosure, the computer-readable medium described above may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0084] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0085] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to: in response to adjusting the target configuration content on the configuration page, obtain target configuration adjustment data; retrieve code information corresponding to the target configuration content; obtain configuration details corresponding to the target configuration content; generate first configuration adjustment risk information using a pre-trained large language model based on the target configuration adjustment data, the code information, and the configuration details; and display the first configuration adjustment risk information on the configuration page in a target display manner.

[0086] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0087] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0088] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including a first acquisition unit, a retrieval unit, a second acquisition unit, a generation unit, and a display unit. The names of these units do not necessarily limit the specific unit; for example, the first acquisition unit may also be described as "a unit that acquires target configuration adjustment data in response to adjustments to target configuration content on a configuration page."

[0089] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0090] Some embodiments of this disclosure also provide a computer program product, including a computer program that, when executed by a processor, implements any of the risk information display methods described above.

[0091] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for displaying risk information, comprising: In response to adjustments made to the target configuration content on the configuration page, obtain the target configuration adjustment data; Retrieve the code information corresponding to the target configuration content; Obtain the configuration details corresponding to the target configuration content; Based on the target configuration adjustment data, the code information, and the configuration details, a first configuration adjustment risk information is generated using a pre-trained large language model; The configuration page displays the risk information for the first configuration adjustment in a target-oriented manner.

2. The method according to claim 1, wherein, The retrieval of code information corresponding to the target configuration content includes: The code information corresponding to the target configuration content is retrieved using at least one of the link retrieval method for code graphs and the retrieval method for code vectors.

3. The method according to claim 1, wherein, The step of obtaining the configuration details corresponding to the target configuration content includes: Using cross-platform content recognition tools, obtain the cross-platform configuration details corresponding to the target configuration content.

4. The method according to claim 1, wherein, The first configuration adjustment risk information includes at least one of the following: value loss risk information, configuration rationality risk information, program compatibility risk information, and user experience risk information; as well as The step of generating first configuration adjustment risk information based on the target configuration adjustment data, the code information, and the configuration details, using a pre-trained large language model, includes: Generate risk prediction and alert information based on the target configuration adjustment data, the code information, and the configuration details; The risk prediction information is input into the large language model to obtain at least one of the following: value loss risk information, configuration rationality risk information, program compatibility risk information, and user experience risk information.

5. The method according to claim 2, wherein, The step of retrieving code information corresponding to the target configuration content using at least one of a link retrieval method for code graphs and a retrieval method for code vectors includes: Using the link retrieval method, retrieve the first candidate code information corresponding to the target configuration content; Retrieve second candidate code information from the code vector library that is similar to the key information corresponding to the target configuration content and meets the target similarity condition; Retrieve third candidate code information from the code vector library that meets the target similarity criteria and has similarity information to the target configuration content. The code information is generated based on the first candidate code information, the second candidate code information, and the third candidate code information.

6. The method according to claim 1, wherein, The method further includes: In response to the input of risk adjustment information and configuration adjustment request information for the first configuration adjustment risk information on the configuration page, the configuration adjustment result for the target configuration content is generated using the large language model based on the target configuration content, the code information and configuration-related information. Based on the configuration adjustment results, the code information, and the configuration-related information, the large language model is used to generate second configuration adjustment risk information for the configuration adjustment results; The configuration page displays the configuration adjustment results and the second configuration adjustment risk information in the manner described in the target display.

7. A risk information display device, comprising: The first acquisition unit is configured to acquire target configuration adjustment data in response to adjustments made to the target configuration content on the configuration page; The retrieval unit is configured to retrieve code information corresponding to the target configuration content; The second acquisition unit is configured to acquire the configuration details content corresponding to the target configuration content; The generation unit is configured to generate first configuration adjustment risk information based on the target configuration adjustment data, the code information, and the configuration details, using a pre-trained large language model. The display unit is configured to display the first configuration adjustment risk information in a target display manner on the configuration page.

8. An electronic device, comprising: One or more processors; Storage device, on which one or more programs are stored, When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-6.

9. A computer-readable medium having a computer program stored thereon, wherein, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.

10. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-6.

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