Asset data analysis method and device, equipment, medium and program product

Abstract

The invention provides an asset data analysis method and device, equipment, a medium and a program product, and relates to the technical field of data processing. The method comprises the steps of obtaining target asset data of a target user in response to a received asset analysis request sent by client equipment; constructing a target asset data analysis table based on the target asset data; the target asset data analysis table comprises classified summary amounts of positive assets and negative assets, an asset-liability structure proportion and a target index; the target index is used for identifying the asset state of the target user; determining the user type of the target user; determining a target asset health analysis result based on the user type and the target asset data by adopting a preset asset health analysis model; determining a target asset optimization suggestion by adopting a preset asset optimization model and based on the target index; and sending the target asset data analysis table, the target asset health analysis result and the target asset optimization suggestion to the client device, and performing display.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, equipment, medium, and program product for analyzing asset data. Background Technology

[0002] With the continuous growth of corporate and personal wealth and the increasing complexity of financial markets, asset health management has become a core aspect of financial decision-making. Businesses need to optimize capital allocation by analyzing their asset and liability structures, while individual users need to balance liquidity, profitability, and risk to preserve and grow their wealth.

[0003] Currently, for asset analysis, users typically obtain asset and liability information separately and then manually integrate and analyze them, resulting in low analysis efficiency. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, medium, and program product for analyzing asset data, in order to solve the problem that in the prior art, users usually obtain asset information and liability information separately and then manually integrate and analyze them, resulting in low analysis efficiency.

[0005] Firstly, this application provides a method for analyzing asset data, including:

[0006] In response to receiving an asset analysis request from a client device, the system obtains the target user's target asset data; the target asset data includes positive asset data and negative asset data; the positive asset data is the asset data held by the target user; the negative asset data is the debt data of the target user.

[0007] A target asset data analysis table is constructed based on the target asset data; the target asset data analysis table includes the sum of positive and negative assets, the asset-liability structure ratio, and target indicators; the target indicators are used to identify the asset status of the target user;

[0008] Determine the user type of the target user;

[0009] A preset asset health analysis model is used to determine the target asset health analysis result based on the user type and the target asset data; the target asset health analysis result is used to identify the asset health status of the target user;

[0010] An optimization recommendation for the target asset is determined using a pre-defined asset optimization model and based on the target indicators.

[0011] The target asset data analysis table, the target asset health analysis results, and the target asset optimization suggestions are sent to the client device and displayed.

[0012] In one possible design,

[0013] The target asset health analysis results include a target asset health score and at least one abnormal indicator;

[0014] The step of using a preset asset health analysis model and determining the target asset health analysis result based on the user type and the target asset data includes:

[0015] The user type and the target asset data are input into the preset asset health analysis model, and the asset data of the target user is analyzed using the preset asset health analysis model, and the target asset health analysis results are output.

[0016] In one possible design, the step of employing a preset asset optimization model and determining target asset optimization suggestions based on the target indicators includes:

[0017] Determine whether to enable the intelligent analysis function;

[0018] If it is determined that the target user has enabled the intelligent analysis function, then input data is generated based on each of the target indicators and prompt word templates;

[0019] The input data is fed into a preset asset optimization model, which is then used to analyze the target user's asset situation and output target asset optimization suggestions.

[0020] In one possible design, the response prior to receiving the asset analysis request from the client device includes:

[0021] In response to automatically triggering an asset analysis request according to a preset period, the target asset data of the target user is obtained;

[0022] At least two target indicators are determined based on the target asset data;

[0023] The values ​​of each target indicator are compared with the corresponding preset thresholds;

[0024] If the value of at least one of the target indicators is greater than the preset threshold, an early warning message is generated and sent to the client device.

[0025] In one possible design, the method further includes:

[0026] In response to receiving an income and expenditure analysis request triggered by a target user; the income and expenditure analysis request includes a target time period and analysis dimensions; the analysis dimensions are transaction type and transaction user;

[0027] Obtain the income and expenditure data of the target user within the target time period; the income data includes income details and amounts corresponding to the transaction type, and the expenditure data includes expenditure details and amounts corresponding to the transaction user;

[0028] An anomaly detection strategy is adopted, and abnormal transaction data is determined based on the income data and the expenditure data;

[0029] The income and expenditure data are categorized and summarized according to the analysis dimensions, and an income and expenditure analysis report is generated based on the categorization and summarization results and the abnormal transaction data.

[0030] The income and expenditure analysis report is sent to the client device and displayed.

[0031] In one possible design, the method further includes:

[0032] In response to receiving an asset structure optimization request triggered by a target user; the asset structure optimization request includes a preset asset structure;

[0033] Obtain the current asset structure and target asset data of the target user;

[0034] A target deviation value is determined based on the target asset data, the current asset structure, and the preset asset structure; the target deviation value is the deviation value between the current asset structure and the preset asset structure.

[0035] Based on the target deviation value and the target asset data, generate asset structure adjustment suggestions;

[0036] The proposed asset structure adjustment and the current structural deviation analysis are sent to the client device and displayed.

[0037] Secondly, this application provides an asset data analysis apparatus, comprising:

[0038] The acquisition module is used to acquire the target asset data of the target user in response to receiving an asset analysis request sent by the client device; the target asset data includes positive asset data and negative asset data; the positive asset data is the asset data held by the target user; the negative asset data is the debt data of the target user.

[0039] The module is used to construct a target asset data analysis table based on the target asset data; the target asset data analysis table includes the sum of positive and negative assets, the asset-liability structure ratio, and target indicators; the target indicators are used to identify the asset status of the target user.

[0040] The determination module is used to determine the user type of the target user;

[0041] The determination module is further configured to use a preset asset health analysis model and determine the target asset health analysis result based on the user type and the target asset data; the target asset health analysis result is used to identify the asset health status of the target user;

[0042] The determination module is also used to determine the target asset optimization suggestions based on the preset asset optimization model and the target indicators;

[0043] The sending module is used to send the target asset data analysis table, the target asset health analysis results, and the target asset optimization suggestions to the client device and display them.

[0044] Thirdly, embodiments of this application provide an electronic device, including: at least one processor and a memory; the memory stores computer-executable instructions; the at least one processor executes the computer-executable instructions stored in the memory, causing the at least one processor to perform the asset data analysis method as described in the first aspect and various possible designs of the first aspect.

[0045] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the asset data analysis method described in the first aspect and various possible designs of the first aspect.

[0046] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the asset data analysis method described in the first aspect and various possible designs of the first aspect.

[0047] The asset data analysis method, apparatus, equipment, medium, and program product provided in this application, in response to receiving an asset analysis request sent by a client device, acquires the target asset data of the target user; the target asset data includes positive asset data and negative asset data; the positive asset data is the asset data held by the target user; the negative asset data is the liability data of the target user; a target asset data analysis table is constructed based on the target asset data; the target asset data analysis table includes the categorized summary amount of positive and negative assets, the asset-liability structure ratio, and target indicators; the target indicators are used to identify the asset status of the target user; the user type of the target user is determined; a preset asset health analysis model is used and the target asset data is used to determine the target asset health analysis result; the target asset health analysis result is used to identify the asset health status of the target user; a preset asset optimization model is used and the target indicators are used to determine the target asset optimization suggestions; the target asset data analysis table, the target asset health analysis result, and the target asset optimization suggestions are sent to the client device and displayed. In response to an asset analysis request received from a client device, the system acquires the target user's target asset data. This data is then aggregated and analyzed to determine the target user's user type. A pre-defined asset health analysis model is used, and based on the user type and target asset data, an asset health analysis is performed on the target user, yielding the target asset health analysis results. Furthermore, a pre-defined asset optimization model is employed, and optimization suggestions are determined based on target indicators. The target asset data analysis table, the target asset health analysis results, and the optimization suggestions are then sent to the client device for display. By integrating scattered data, the system centrally consolidates the target user's positive and negative asset data, avoiding the need for users to switch between multiple functional modules. The analysis table visually displays the classification, structure, and key indicators of assets and liabilities, helping users quickly grasp the overall asset status. The target asset health analysis results transform the asset status into understandable quantitative results, improving the user's perception of financial health and providing corresponding target asset optimization suggestions, thereby increasing asset analysis efficiency and enhancing the user experience. Attached Figure Description

[0048] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0049] Figure 1 This is a diagram illustrating an application scenario for the asset data analysis method applicable to the embodiments of this application.

[0050] Figure 2 A flowchart illustrating an asset data analysis method provided in an embodiment of this application;

[0051] Figure 3 A schematic diagram of the structure of an asset data analysis device provided in an embodiment of this application;

[0052] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0053] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0054] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0055] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0056] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0057] To clearly understand the technical solution of this application, the solutions of the prior art will be described in detail first.

[0058] Currently, when users want to conduct asset analysis, they typically view their asset and liability information separately and then manually integrate and analyze it, resulting in low analysis efficiency and affecting user experience.

[0059] To address the aforementioned technical issues, this application proposes the following technical concept: users no longer need to individually view and obtain their asset status and then perform analysis. Instead, when a user wants to perform asset analysis, they can trigger an asset analysis request with a single click, and the server-side device will automatically analyze the user's asset information. Specifically, in response to receiving an asset analysis request triggered by a target user through a client device, the system acquires the target user's target asset data. This target asset data includes positive and negative asset data. Positive asset data represents the assets held by the target user, while negative asset data represents the liabilities incurred by the target user. Then, a target asset data analysis table is constructed by reading the user's target asset data. This table includes the categorized summary amounts of positive and negative assets, the asset-liability structure ratio, and target indicators. These target indicators are used to identify the target user's asset status. Furthermore, the system determines the target user's user type, uses a pre-defined asset health analysis model based on the user type and target asset data to determine the target asset health analysis results, and uses a pre-defined asset optimization model based on the target indicators to determine target asset optimization suggestions. Finally, the target asset data analysis table, target asset health analysis results, and target asset optimization suggestions are sent to the client device for display. This achieves a complete set of asset analysis functions, allowing users to quickly and directly understand their current asset status, the health of their asset distribution, and how to better optimize their target assets, thereby improving analysis efficiency and user experience.

[0060] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0061] Figure 1 This is an application scenario diagram of the asset data analysis method applicable to the embodiments of this application, such as... Figure 1As shown. The system corresponding to the asset data analysis method in this embodiment may include: a client device 11 and a server device 12. The user triggers an asset analysis request through the client device 11, and the client device 11 sends the asset analysis request to the server device 12. In response to receiving the asset analysis request, the server device 12 obtains the target user's target asset data. The target asset data includes positive asset data and negative asset data; positive asset data refers to the assets held by the target user; negative asset data refers to the liabilities of the target user. Further, a target asset data analysis table is constructed based on the target asset data. The server device 12 determines the user type of the target user, uses a preset weighted algorithm, and determines the target asset health score based on the user type and target asset data, and determines target asset optimization suggestions based on target indicators. The target asset data analysis table includes the categorized summary amount of positive and negative assets, the asset-liability structure ratio, and target indicators; the target indicators are used to identify the target user's asset status. The server device 12 sends the target asset data analysis table, the target asset health score, and the target asset optimization suggestions to the client device 11 and displays them.

[0062] Figure 2 A flowchart of an asset data analysis method provided in an embodiment of this application is shown below. Figure 2 As shown, the execution entity in this embodiment is an asset data analysis device. This asset data analysis device can be implemented through a computer program, or through a medium storing the relevant computer program, such as a USB flash drive and / or optical disc; alternatively, it can be implemented through a physical device that integrates or installs the relevant computer program, such as a chip or electronic device. The asset data analysis method provided in this embodiment includes the following steps:

[0063] S201, in response to receiving an asset analysis request from a client device, obtain the target user's target asset data.

[0064] The target asset data includes positive asset data and negative asset data; positive asset data refers to the assets held by the target user, while negative asset data refers to the liabilities of the target user.

[0065] The target users are those whose assets are to be analyzed.

[0066] The positive asset data may include the target user's current deposits, time deposits, wealth management products, and held bills. Current deposits: Includes current deposits and negotiated deposits, representing the account balance of the target user's current accounts. Time deposits: Includes time deposits, call deposits, negotiated deposits, and large-denomination certificates of deposit, representing the account balance of the target user's time deposit accounts. Wealth management products: These are bank wealth management products purchased by the target user and issued by or distributed by the bank, with the balance of these products representing the target user's outstanding balance. Held bills: These are commercial drafts held by the target user and collected by the bank, representing the amount of the commercial draft.

[0067] The target users include both private users and corporate users.

[0068] It is understandable that different user types may have different positive and negative asset data for their target users.

[0069] The negative asset data may include loans, bank acceptance bills, and trade finance. Loans: The remaining outstanding loan balance of the target user. Bank acceptance bills: Bank acceptance bills issued by the target user and accepted by a bank but not yet paid; the face value of the bill is recorded. Trade finance: Irrevocable letters of credit issued by a bank and not yet fully fulfilled; the unused amount is recorded.

[0070] It is understandable that the positive asset data and negative asset data obtained will differ depending on whether the target user is a private account or a corporate account.

[0071] The target asset data refers to all the assets that the current user owns in the current account.

[0072] Specifically, in this embodiment, the target user triggers an asset analysis request for the corresponding account through a client device on preset software. The client device sends the asset analysis request to the server device. Upon receiving the asset analysis request from the client device, the server device retrieves the target user's target asset data from a preset database.

[0073] Optionally, the preset software may be a banking app, etc., but this embodiment does not limit it.

[0074] S202, Construct a target asset data analysis table based on the target asset data.

[0075] The target asset data analysis table includes the sum of positive and negative assets, the asset-liability structure ratio, and target indicators. Target indicators may include the debt-to-equity ratio, current ratio, and the proportion of highly liquid assets.

[0076] Specifically, in this embodiment, the server-side device groups positive asset data by type (e.g., demand deposits, time deposits, wealth management products) and calculates the total amount of each asset type. Similarly, it groups negative asset data by type (e.g., corporate loans, bank acceptance bills) and calculates the total amount of each liability type. Then, it calculates the proportion of each type of positive asset to the total positive assets and the proportion of each type of negative asset to the total negative assets. Further, it calculates the debt-to-equity ratio, current ratio, and the proportion of highly liquid assets. The above-mentioned categorized summaries of amounts, structural proportions, and target indicators are integrated into a structured table to obtain the target asset data analysis table.

[0077] The debt-to-equity ratio is the percentage of total negative assets to total positive assets. The current ratio is the percentage of liquid assets to short-term liabilities. Liquid assets include demand deposits and short-term wealth management products. Short-term liabilities include loans maturing within one year. The percentage of highly liquid assets is the percentage of highly liquid assets to total positive assets. Highly liquid assets include demand deposits, etc.

[0078] Optionally, the target indicators may vary depending on the type of asset analysis performed, and can be set according to requirements. This embodiment does not impose any limitations on these indicators.

[0079] S203, Determine the user type of the target user.

[0080] Specifically, in this embodiment, the target user's account type can be obtained from a preset database, and the target user's user type can be determined based on the target user's account type.

[0081] Optionally, in this embodiment, the user type of the target user can be determined by a preset tag of the target user.

[0082] The preset tag is used to mark the target user as a private user or a corporate user.

[0083] Optionally, preset markers can be set independently according to needs; this embodiment does not impose any limitations.

[0084] S204 uses a preset asset health analysis model and determines the target asset health analysis results based on user type and target asset data.

[0085] Optionally, the preset asset health analysis model can be a machine learning model or other models, and this embodiment does not limit it.

[0086] As an optional implementation, the following steps can be used to employ a preset asset health analysis model and determine the target asset health analysis result based on user type and target asset data: input user type and target asset data into the preset asset health analysis model, analyze the target user's asset data using the preset asset health analysis model, and output the target asset health analysis result.

[0087] The target asset health analysis results include a target asset health score and at least one abnormal indicator.

[0088] The target asset health score quantifies the overall health status of the target user's assets. Abnormal indicators are those that cause a decline in the target asset health score.

[0089] For example, if the user type is a private user, the target metrics may include credit card usage rate, current account coverage ratio, and short-term debt service ratio.

[0090] Credit card utilization rate = (outstanding credit card balance + outstanding installment principal) ÷ ​​total credit card limit. Current account asset coverage ratio = current account balance ÷ average daily spending over the past 3 months. Short-term debt service ratio = assets liquidated within 7 days (current accounts + money market funds) ÷ liabilities due within 30 days (minimum credit card payment + principal of short-term loans).

[0091] For example, if the user type is a private user, the target metrics may include the current account debt service ratio, the revenue debt service ratio, and the proportion of interest-bearing liabilities.

[0092] The following metrics are used to determine the debt service ratio: Demand deposit balance = Corporate demand deposit balance ÷ Debt due within 90 days (short-term loans + bank draft exposure). Revenue service ratio = Average monthly settlement revenue over the past 3 months ÷ Average monthly debt expenditure (loan interest + principal repayment). Interest-bearing debt ratio = Total interest-bearing debt (loans) ÷ Total liabilities.

[0093] The target indicators include the debt-to-asset ratio, current ratio, proportion of highly liquid assets, proportion of short-term liabilities, and asset-to-finance ratio.

[0094] The target weight refers to the weight used in calculating the target asset health score, and it varies depending on the user type. The target weight includes the weight value corresponding to each indicator.

[0095] Specifically, in this embodiment, the server device inputs user type and target asset data into a preset asset health analysis model. The preset asset health analysis model reads positive and negative asset data from the target asset data, extracts the core fields required for calculation, calculates at least two target indicators according to settings, and scores each calculated target indicator according to a preset scoring strategy to obtain the target score corresponding to each target indicator. Then, it obtains the corresponding target weight according to the user type, multiplies the target score corresponding to each target indicator by the corresponding weight value, and adds the results of the multiplication to obtain the target asset health score. A feature importance algorithm is then used to filter out at least one abnormal indicator, and the target asset health score and at least one abnormal indicator are output to obtain the target asset health analysis result.

[0096] Understandably, the units for the target asset data are uniform. Asset data can be categorized as long-term or short-term based on a time dimension. For example, liabilities due within one year can be marked as short-term liabilities, while those due after one year can be marked as long-term liabilities. The total asset amount and total liability amount can then be calculated.

[0097] Specifically, the current ratio reflects the ability of readily realizable assets to cover short-term liabilities. The proportion of highly liquid assets reflects the percentage of assets that can be immediately converted into cash, measuring the assets' emergency liquidity. The proportion of short-term liabilities reflects short-term debt repayment pressure; short-term liabilities refer to liabilities due within one year. The asset-to-asset ratio reflects the proportion of assets used for capital appreciation, measuring the profitability of the assets.

[0098] Among them, the proportion of short-term liabilities = (amount of short-term liabilities ÷ amount of total liabilities) × 100%.

[0099] The asset-to-finance ratio is calculated as follows: (Wealth management product balance ÷ Total assets) × 100%.

[0100] Optionally, the preset scoring strategy can be set independently according to needs, and this embodiment does not impose any limitations.

[0101] The preset scoring strategy includes the mapping relationship between the indicator value range and the score.

[0102] Specifically, asset health is assessed from different dimensions using at least two indicators, thus avoiding the limitations of a single indicator. Furthermore, a pre-defined strategy is employed to convert each target indicator into a score, achieving standardized quantification of health status.

[0103] As an optional implementation, the following steps can be used to employ a preset asset optimization model and determine target asset optimization suggestions based on target indicators: determine whether to enable the intelligent analysis function; if it is determined that the target user enables the intelligent analysis function, then generate input data based on each target indicator and prompt word template; input the input data into the preset asset optimization model, use the preset asset optimization model to analyze the target user's asset situation, and output target asset optimization suggestions.

[0104] Optionally, the target indicators can be set independently according to needs, and this embodiment does not impose any restrictions.

[0105] Optionally, the preset asset optimization model can be a large model, etc., which is not limited in this embodiment.

[0106] Optionally, the prompt word template can be set independently according to needs, and this embodiment does not impose any limitations.

[0107] For example, the prompt template reads, "Please analyze the user's asset health status based on the following indicators and provide optimization suggestions: Debt-to-asset ratio {X}%, Current ratio {Y}%, Proportion of highly liquid assets {Z}%, Proportion of short-term liabilities {A}%, Asset monetization rate {B}%. Suggestions should include actionable measures for abnormal indicators." Here, X, Y, Z, A, and B can be replaced with the corresponding values ​​for each target indicator.

[0108] Specifically, in this embodiment, the server device reads the target user's function configuration information to determine the activation status of the "intelligent analysis function". Based on the status settings configured by the target user through the client interface stored in the configuration information, the system directly reads this status value for judgment. If it is determined that the target user has enabled the intelligent analysis function, the corresponding values ​​of a preset number of target indicators are extracted, a preset prompt word template is obtained, the corresponding values ​​of the preset number of target indicators are filled into the corresponding positions in the prompt word template, the input data is input into a preset asset optimization model, and target asset optimization suggestions are output. The server device determines the target asset health score and target asset optimization suggestions as target asset health analysis data and sends it to the client device for interface display.

[0109] For example, a target asset optimization suggestion could be: "The short-term debt ratio of 65% is too high. It is recommended to replace the RMB 1 million bank acceptance bill with a 6-month commercial acceptance bill to reduce short-term debt repayment pressure."

[0110] The prompt word template is used to standardize the format of input data, ensuring that the preset asset optimization model can accurately understand the analysis requirements. The preset asset optimization model can be a trained natural language processing model, capable of parsing indicator data, identifying anomalies, and generating targeted suggestions; the model parameters and training logic are pre-configured. Target asset optimization suggestions are specific improvement measures for abnormal indicators output by the preset asset optimization model. Target asset health analysis data can be used to comprehensively reflect the asset health status of the target user.

[0111] The target asset optimization suggestions include indicator names, anomaly descriptions, and specific measures.

[0112] Specifically, it allows users to choose whether to enable intelligent analysis, increasing user autonomy. By combining user target metrics and automatically generating suggestions through preset asset optimization models, it improves asset optimization efficiency.

[0113] S205 sends the target asset data analysis table, target asset health analysis results, and target asset optimization suggestions to the client device and displays them.

[0114] Specifically, in this embodiment, the server device sends the generated target asset data analysis table, target asset health analysis results, and target asset optimization suggestions to the client device for display on the page.

[0115] The asset data analysis method provided in this application includes: responding to receiving an asset analysis request sent by a client device, obtaining target asset data of the target user; the target asset data includes positive asset data and negative asset data; positive asset data refers to the assets held by the target user; negative asset data refers to the liabilities of the target user; constructing a target asset data analysis table based on the target asset data; the target asset data analysis table includes the categorized summary amount of positive and negative assets, the asset-liability structure ratio, and target indicators; the target indicators are used to identify the asset status of the target user; determining the user type of the target user; using a preset asset health analysis model and based on the user type and target asset data to determine the target asset health analysis result; the target asset health analysis result is used to identify the asset health status of the target user; using a preset asset optimization model and based on the target indicators to determine target asset optimization suggestions; sending the target asset data analysis table, the target asset health analysis result, and the target asset optimization suggestions to the client device and displaying them. In response to an asset analysis request received from a client device, the system acquires the target user's target asset data. This data is then aggregated and analyzed to determine the target user's user type. A pre-defined asset health analysis model is used, and based on the user type and target asset data, an asset health analysis is performed on the target user, yielding the target asset health analysis results. Furthermore, a pre-defined asset optimization model is employed, and optimization suggestions are determined based on target indicators. The target asset data analysis table, the target asset health analysis results, and the optimization suggestions are then sent to the client device for display. By integrating scattered data, the system centrally consolidates the target user's positive and negative asset data, avoiding the need for users to switch between multiple functional modules. The analysis table visually displays the classification, structure, and key indicators of assets and liabilities, helping users quickly grasp the overall asset status. The target asset health analysis results transform the asset status into understandable quantitative results, improving the user's perception of financial health and providing corresponding target asset optimization suggestions, thereby increasing asset analysis efficiency and enhancing the user experience.

[0116] As an optional implementation, based on the above embodiments, in response to receiving an asset analysis request sent by the client device, the following steps are included:

[0117] In response to automatically triggering asset analysis requests according to a preset period, the target asset data of the target user is obtained;

[0118] At least two target metrics are determined based on the target asset data;

[0119] Compare the values ​​of each target indicator with the corresponding preset thresholds;

[0120] If the value of at least one target indicator is greater than a preset threshold, an early warning message is generated and sent to the client device.

[0121] Optionally, the preset period can be every 10 hours, or at 22:00 every day, or at any time. It can be set independently according to needs, and this embodiment does not impose any limitations.

[0122] The preset cycle can be set by the target user or other users themselves.

[0123] Optionally, the preset threshold can vary depending on the target indicator and can be set independently according to the needs. This embodiment does not impose any limitations.

[0124] Specifically, in this embodiment, the server device sets a timer task to automatically trigger an asset analysis request when the preset period's trigger time is reached, thereby obtaining the target user's target asset data. The server device extracts the latest target asset data of the target user from a preset database, including positive asset data and negative asset data, and uses the target asset data to calculate at least two target indicators. It also reads the preset threshold corresponding to each target indicator and compares it one by one. If the value of at least one target indicator is greater than the preset threshold, an early warning message is generated and sent to the client device.

[0125] For example, the warning information may include the name of the indicator that exceeds the standard, the current value of the indicator, and a risk warning.

[0126] The asset data analysis method provided in this application can achieve continuous monitoring of asset status by setting a preset period, and immediately notify the target user when the indicators exceed the standard, so as to help the user take measures before the risk expands, improve the timeliness of risk prevention and control, and thus reduce the risk of financial loss.

[0127] As an optional implementation, based on the above embodiments, the method further includes:

[0128] The system responds to a revenue and expense analysis request triggered by a target user. The revenue and expense analysis request includes the target time period and analysis dimensions. The analysis dimensions are transaction type and transaction user.

[0129] Obtain the target user's income and expenditure data within the target time period; income data includes income details and amounts corresponding to transaction types, and expenditure data includes expenditure details and amounts corresponding to transaction users;

[0130] An anomaly detection strategy is adopted, and abnormal transaction data is identified based on revenue and expenditure data;

[0131] Income and expenditure data are categorized and summarized according to analytical dimensions, and an income and expenditure analysis report is generated based on the categorization and summary results and abnormal transaction data;

[0132] The income and expenditure analysis report is sent to the client device and displayed.

[0133] The target time period is the user-specified analysis time range used to limit the scope of income and expense data extraction. The analysis dimension is the user-selected perspective for income and expense analysis. Transaction type refers to the category categorized according to the nature or purpose of the transaction. Transaction user refers to the counterparty to the target user's income and expense transactions. Income data refers to the target user's cash inflow records within the target time period, including transaction type, details, and amount. Expense data refers to the target user's cash outflow records within the target time period, including transaction user, details, and amount.

[0134] Optionally, the transaction type can be salary income, payment for goods, daily consumption expenditure, etc., which is not limited in this embodiment.

[0135] Here, "transaction user" refers to any user with whom the target user engages in a transaction. If not specified, it refers to all users within the target time period.

[0136] The target time period is the time period selected by the target user.

[0137] Optionally, the preset anomaly detection strategy can be set independently according to needs, and this embodiment does not impose any limitations.

[0138] Specifically, in this embodiment, the target user triggers an income and expenditure analysis request through a client device. This request includes a target time period, transaction type, and transaction user. The server device filters transaction records related to the target user's transaction type and user within the target time period from a preset database; these records are income and expenditure data. A preset anomaly detection strategy is then used to identify abnormal transaction data based on the income and expenditure data. The income and expenditure data are then categorized and summarized according to analytical dimensions. Based on the categorization and summarization results and the abnormal transaction data, an income and expenditure analysis report is generated and sent to the client device for display.

[0139] For example, the preset anomaly identification strategy may include: abnormal amount, i.e., the amount of a single transaction exceeds three times or more of the user's historical average for similar transactions; abnormal frequency, i.e., the same user makes three or more transfers within one hour; abnormal time, i.e., the transaction occurs during an unusual time period; abnormal type, i.e., the transaction type does not match the user's identity.

[0140] Specifically, the summaries by analysis dimension include: by "transaction type": revenue side: summing up the revenue amount for the same transaction type; expenditure side: summing up the expenditure amount for the same transaction type; by "transaction user": revenue side: summing up the revenue amount for each transaction user; expenditure side: summing up the expenditure amount for each payer.

[0141] The report structure includes: target users, target time period, and analysis dimensions. Revenue and expenditure amounts and percentages for "transaction type" and "transaction user" dimensions can be displayed in tables or charts. For abnormal transaction data, details of the abnormal transactions and their causes can be listed.

[0142] The asset data analysis method provided in this application helps users clarify the source and destination of funds by supporting the analysis of income and expenditure data by target time period and multiple dimensions. It also adopts a preset anomaly identification strategy to automatically identify abnormal transactions, promptly detect potential fund security risks, and improve the security of fund management.

[0143] As an optional implementation, based on the above embodiments, the method further includes:

[0144] In response to receiving an asset structure optimization request triggered by a target user; the asset structure optimization request includes a preset asset structure;

[0145] Obtain the target user's current asset structure and target asset data;

[0146] The target deviation value is determined based on the target asset data, the current asset structure, and the preset asset structure; the target deviation value is the deviation between the current asset structure and the preset asset structure.

[0147] Based on the target deviation value and target asset data, generate asset structure adjustment suggestions;

[0148] The asset structure adjustment suggestions and current structural deviation analysis are sent to the client device and displayed.

[0149] The asset structure optimization request refers to an instruction initiated by the target user to optimize their current asset allocation based on a "preset asset structure." The preset asset structure refers to the user's desired asset allocation ratio, composed of the proportions of one or more asset classes (e.g., "20% current accounts, 30% fixed deposits, 50% wealth management products"), reflecting the user's risk preference or financial goals. The current asset structure refers to the proportion of each type of asset currently held by the target user in their total assets (e.g., "25% current accounts, 35% fixed deposits, 40% wealth management products"), reflecting the actual asset allocation status. Target asset data refers to the specific amounts of each type of asset currently held by the user and the total asset amount, serving as the basis for calculating the adjustment amount. The target deviation value refers to the difference between the current asset structure and the preset asset structure, including individual deviation values ​​(the deviation of a certain asset class) and the overall deviation degree (overall deviation), used to identify the asset types that need optimization. The asset structure adjustment suggestion refers to specific optimization measures generated based on the target deviation value, including the adjustment direction, involved asset types, adjustment amount, and operational guidelines, aiming to narrow the gap between the current structure and the preset structure.

[0150] Specifically, in this embodiment, the target user triggers an asset structure optimization request through a client device. This request extracts details of all types of assets currently held by the target user from a preset database, along with target asset data, and calculates the proportion of each asset type in the total assets, thus obtaining the current asset structure. The current asset structure is then aligned with the asset types of a preset asset structure, and the deviation values ​​between the indicators included in the current asset structure and their corresponding indicators in the preset asset structure are calculated. Adjustment needs are determined based on the sign of the individual deviation values. If the deviation value is positive (current proportion > preset proportion), the proportion of that asset type needs to be reduced. If the deviation value is negative (current proportion < preset proportion), the proportion of that asset type needs to be increased. The server device combines the target asset data and the deviation values ​​to calculate the specific adjustment amount and provide asset structure adjustment suggestions. The asset structure adjustment suggestions and the current structure deviation analysis are then sent to the client device for display.

[0151] For example, if current account deposits currently account for 25% and the preset percentage is 20%, then the single deviation value is 5%. If wealth management products currently account for 40% and the preset percentage is 50%, then the single deviation value is -10%. The adjustment amount is then calculated based on the total funds and the deviation value. If the total funds are 2 million yuan, and the current account deviation is +5%, then 100,000 yuan needs to be transferred out. For each type of asset with deviation, and considering the direction and amount of adjustment, actionable suggestions are provided, such as transferring 100,000 yuan from current accounts to wealth management products to compensate for the insufficient proportion of wealth management products. If multiple deviations exist, suggestions are made to sort them by absolute deviation value from largest to smallest, prioritizing the adjustment of the asset with the highest deviation.

[0152] The asset data analysis method provided in this application clarifies the direction of asset adjustments by comparing a preset asset structure with the current asset structure, and provides actionable specific amounts and methods based on the target deviation value, thereby improving the feasibility of the recommendations. It allows users to set preset structures according to their own needs, ensuring that the optimization plan aligns with their risk preferences and financial goals.

[0153] Figure 3 A schematic diagram of the structure of an asset data analysis device provided in an embodiment of this application is shown below. Figure 3 As shown, the asset data analysis device provided in this embodiment is located in an electronic device. The asset data analysis device 30 provided in this embodiment includes: an acquisition module 31, a construction module 32, a determination module 33, and a sending module 34.

[0154] The acquisition module 31 is used to receive an asset analysis request sent by the client device and acquire the target asset data of the target user. The target asset data includes positive asset data and negative asset data. Positive asset data refers to the assets held by the target user, and negative asset data refers to the liabilities of the target user. The construction module 32 is used to construct a target asset data analysis table based on the target asset data. The target asset data analysis table includes the sum of positive and negative assets, the asset-liability structure ratio, and target indicators. The target indicators are used to identify the asset status of the target user. The determination module 33 is used to determine the user type of the target user. The determination module 33 is also used to determine the target asset health analysis result based on the user type and target asset data using a preset asset health analysis model. The target asset health analysis result is used to identify the asset health status of the target user. The determination module 33 is also used to determine the target asset optimization suggestion based on the target indicators using a preset asset optimization model. The sending module 34 is used to send the target asset data analysis table, the target asset health analysis result, and the target asset optimization suggestion to the client device and display them.

[0155] Optionally, the target asset health analysis results may include a target asset health score and at least one abnormal indicator.

[0156] Accordingly, the determination module 33, when using a preset asset health analysis model and determining the target asset health analysis result based on user type and target asset data, is specifically used to: input user type and target asset data into the preset asset health analysis model, analyze the target user's asset data using the preset asset health analysis model, and output the target asset health analysis result.

[0157] Optionally, the determination module 33, when using a preset asset optimization model and determining target asset optimization suggestions based on target indicators, is specifically used to: determine whether to enable the intelligent analysis function; if it is determined that the target user enables the intelligent analysis function, then generate input data based on each target indicator and prompt word template; input the input data into the preset asset optimization model, use the preset asset optimization model to analyze the target user's asset situation, and output target asset optimization suggestions.

[0158] Optionally, the asset data analysis device provided in this embodiment further includes a comparison module.

[0159] Accordingly, the acquisition module 31 is used to acquire the target asset data of the target user by automatically triggering the asset analysis request according to a preset period before receiving the asset analysis request sent by the client device. The determination module 33 is further used to determine at least two target indicators based on the target asset data. The comparison module is used to compare the value of each target indicator with the corresponding preset threshold. The generation module is further used to generate an early warning message and send it to the client device if the value of at least one target indicator is greater than the preset threshold.

[0160] Optionally, the acquisition module 31 is used to respond to receiving an income and expenditure analysis request triggered by a target user; the income and expenditure analysis request includes a target time period and analysis dimensions; the analysis dimensions are transaction type and transaction user; and it acquires the target user's income and expenditure data within the target time period; the income data includes income details and amounts corresponding to the transaction type, and the expenditure data includes expenditure details and amounts corresponding to the transaction user. The determination module 33 is further used to: use a preset anomaly identification strategy and determine abnormal transaction data based on the income and expenditure data. The analysis module is further used to classify and summarize the income and expenditure data according to the analysis dimensions, and generate an income and expenditure analysis report based on the classification and summary results and the abnormal transaction data. The sending module 34 is further used to send the income and expenditure analysis report to the client device and display it.

[0161] Optionally, the acquisition module 31 is used to respond to receiving an asset structure optimization request triggered by a target user; the asset structure optimization request includes a preset asset structure; and to acquire the target user's current asset structure and target asset data. The determination module 33 is further used to: determine a target deviation value based on the target asset data, the current asset structure, and the preset asset structure; the target deviation value is the deviation value between the current asset structure and the preset asset structure. The generation module is further used to generate asset structure adjustment suggestions based on the target deviation value and the target asset data. The sending module 34 is further used to send the asset structure adjustment suggestions and the current structure deviation analysis to the client device and display them.

[0162] The asset data analysis device provided in this application embodiment can be used to execute the technical solution of the asset data analysis method in the above embodiment. Its implementation principle and technical effect are similar, and will not be described again here.

[0163] It should be noted that the division of the various modules in the above device is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software through processing element calls; they can be implemented entirely in hardware; or some modules can be implemented by processing element calls to software, while others are implemented in hardware.

[0164] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 40 may include: a processor 41 and a memory 42.

[0165] The memory 42 stores computer-executable instructions; the processor 41 executes the computer-executable instructions stored in the memory 42 to implement the asset data analysis method provided in any of the above embodiments. Related explanations can be understood by referring to the relevant descriptions and effects corresponding to the steps in the accompanying drawings, and will not be elaborated upon here.

[0166] Processor 41 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0167] The memory 42 is connected to the processor 41 via the system bus and completes communication between them. The memory 42 is used to store computer program instructions.

[0168] The system bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory.

[0169] The electronic device provided in this application embodiment can be the terminal device described in the above embodiments.

[0170] This application also provides a chip for executing instructions, which is used to execute the asset data analysis method described in the above embodiments.

[0171] This application also provides a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the asset data analysis method described in the above embodiments.

[0172] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, it can implement the technical solution of the asset data analysis method in the above embodiments.

[0173] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0174] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0175] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0176] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0177] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0178] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0179] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0180] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0181] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0182] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0183] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0184] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0185] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0186] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0187] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for analyzing asset data, characterized in that, include: In response to receiving an asset analysis request from a client device, the system obtains the target user's target asset data. The target asset data includes both positive asset data and negative asset data; The positive asset data refers to the asset data held by the target user; The negative asset data refers to the debt data of the target user. Construct a target asset data analysis table based on the target asset data; The target asset data analysis table includes the total amount of positive and negative assets, the asset-liability structure ratio, and target indicators; the target indicators are used to identify the asset status of the target user. Determine the user type of the target user; The target asset health analysis result is determined by using a preset asset health analysis model and based on the user type and the target asset data; The target asset health analysis results are used to identify the asset health status of the target user; An optimization recommendation for the target asset is determined using a pre-defined asset optimization model and based on the target indicators. The target asset data analysis table, the target asset health analysis results, and the target asset optimization suggestions are sent to the client device and displayed.

2. The method according to claim 1, characterized in that, The target asset health analysis results include a target asset health score and at least one abnormal indicator; The step of using a preset asset health analysis model and determining the target asset health analysis result based on the user type and the target asset data includes: The user type and the target asset data are input into the preset asset health analysis model, and the asset data of the target user is analyzed using the preset asset health analysis model, and the target asset health analysis results are output.

3. The method according to claim 1, characterized in that, The step of using a preset asset optimization model and determining target asset optimization suggestions based on the target indicators includes: Determine whether to enable the intelligent analysis function; If it is determined that the target user has enabled the intelligent analysis function, then input data is generated based on each of the target indicators and prompt word templates; The input data is fed into a preset asset optimization model, which is then used to analyze the target user's asset situation and output target asset optimization suggestions.

4. The method according to claim 1, characterized in that, The response prior to receiving the asset analysis request from the client device includes: In response to automatically triggering an asset analysis request according to a preset period, the target asset data of the target user is obtained; At least two target indicators are determined based on the target asset data; The values ​​of each target indicator are compared with the corresponding preset thresholds; If the value of at least one of the target indicators is greater than the preset threshold, an early warning message is generated and sent to the client device.

5. The method according to claim 1, characterized in that, The method further includes: In response to receiving an income and expenditure analysis request triggered by the target user; the income and expenditure analysis request includes a target time period and analysis dimensions; the analysis dimensions are transaction type and transaction user; Obtain the income and expenditure data of the target user within the target time period; the income data includes income details and amounts corresponding to the transaction type, and the expenditure data includes expenditure details and amounts corresponding to the transaction user; An anomaly detection strategy is adopted, and abnormal transaction data is determined based on the income data and the expenditure data; The income and expenditure data are categorized and summarized according to the analysis dimensions, and an income and expenditure analysis report is generated based on the categorization and summarization results and the abnormal transaction data. The income and expenditure analysis report is sent to the client device and displayed.

6. The method according to claim 1, characterized in that, The method further includes: In response to receiving an asset structure optimization request triggered by the target user; the asset structure optimization request includes a preset asset structure; Obtain the current asset structure and target asset data of the target user; A target deviation value is determined based on the target asset data, the current asset structure, and the preset asset structure; the target deviation value is the deviation value between the current asset structure and the preset asset structure. Based on the target deviation value and the target asset data, generate asset structure adjustment suggestions; The proposed asset structure adjustment and the current structural deviation analysis are sent to the client device and displayed.

7. An asset data analysis device, characterized in that, include: The acquisition module is used to acquire the target asset data of the target user in response to the asset analysis request sent by the client device; The target asset data includes positive asset data and negative asset data; the positive asset data refers to the asset data held by the target user. The negative asset data refers to the debt data of the target user. The construction module is used to construct a target asset data analysis table based on the target asset data; The target asset data analysis table includes the total amount of positive and negative assets, the asset-liability structure ratio, and target indicators; the target indicators are used to identify the asset status of the target user. The determination module is used to determine the user type of the target user; The determination module is also used to determine the target asset health analysis result based on the user type and the target asset data using a preset asset health analysis model; The target asset health analysis results are used to identify the asset health status of the target user; The determination module is also used to determine the target asset optimization suggestions based on the preset asset optimization model and the target indicators; The sending module is used to send the target asset data analysis table, the target asset health analysis results, and the target asset optimization suggestions to the client device and display them.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1-6.

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