Asset quality monitoring method and device, electronic equipment, medium and program product

Abstract

The invention provides an asset quality monitoring method and device, electronic equipment, a medium and a computer program product, which can be applied to the technical field of big data. The method comprises the steps of obtaining asset data of m assets in an asset system; according to the asset data, an asset quality result is obtained by using a pre-constructed asset monitoring model, the asset monitoring model comprises a liquidity reserve fluctuation index weight of each of m predetermined assets, and the liquidity reserve fluctuation index weight of each of the m predetermined assets is greater than the liquidity reserve fluctuation index weight of each of the m predetermined assets; the asset monitoring model further comprises a mapping relation among a single-item flowability reserve fluctuation index, an investment cycle, a yield rate and an asset quality result of each item of asset in the m items of assets; the single liquidity reserve fluctuation index of each asset in the m assets is calculated by the asset monitoring model according to the day end balance of the current day, the day end balance of the previous day and the weight of the liquidity reserve fluctuation index of each asset, and the asset quality result is high-quality assets or poor assets; and displaying the asset quality result as monitoring information.

Description

Technical Field

[0001] The present disclosure relates to the field of big data technology, and more specifically, to an asset quality monitoring method, device, electronic device, medium and computer program product. Background Art

[0002] In the sustainable development of commercial banks, asset quality is the foundation for their survival. Therefore, commercial banks regard the monitoring of asset quality as one of their important daily tasks. At present, the monitoring of asset quality requires manual analysis of asset data in the system, so as to judge which assets in the system are high-quality assets and which assets are non-performing assets based on the analysis results. Manual analysis of asset data requires a lot of labor costs and is inefficient. More importantly, manual analysis of asset data is limited by human cognition and energy, and is prone to errors, resulting in low accuracy of analysis results, which affects the smooth progress of asset quality monitoring. Therefore, how to monitor asset quality efficiently and accurately has become a technical problem that technicians in this field urgently need to solve. Summary of the invention

[0003] In view of this, the present disclosure provides an asset quality monitoring method, device, electronic device, computer-readable storage medium and computer program product that can monitor asset quality efficiently and accurately.

[0004] One aspect of the present disclosure provides an asset quality monitoring method, comprising: acquiring asset data of m assets in an asset system, wherein the asset data of each asset includes the asset's day-end balance, the day-end balance of the previous day, the investment cycle and the rate of return, and m is an integer greater than or equal to 1; obtaining an asset quality result based on the asset data using a pre-constructed asset monitoring model, wherein the asset monitoring model includes a predetermined liquidity reserve volatility index weight of each of the m assets, and the asset monitoring model also includes a mapping relationship between the single liquidity reserve volatility index of each of the m assets, the investment cycle and the rate of return and the asset quality result, the single liquidity reserve volatility index of each of the m assets is calculated by the asset monitoring model based on the day-end balance of each asset, the day-end balance of the previous day and the liquidity reserve volatility index weight, and the asset quality result is a high-quality asset or a non-performing asset; and displaying the asset quality result as monitoring information.

[0005] According to the asset quality monitoring method of the embodiment of the present disclosure, by acquiring the asset data of m assets in the asset system, wherein the asset data of each asset includes the day-end balance of the asset, the day-end balance of the previous day, the investment cycle and the rate of return; the asset quality result can be obtained according to the asset data by using a pre-constructed asset monitoring model, wherein the asset monitoring model includes a predetermined liquidity reserve volatility index weight of each of the m assets, and the asset monitoring model also includes a mapping relationship between the single liquidity reserve volatility index of each of the m assets, the investment cycle and the rate of return and the asset quality result, the single liquidity reserve volatility index of each of the m assets is calculated by the asset monitoring model according to the day-end balance of each asset, the day-end balance of the previous day and the liquidity reserve volatility index weight, and the asset quality result is a high-quality asset or a non-performing asset; and the asset quality result is then displayed as monitoring information. The asset detection model pre-built in the present disclosure can obtain the single liquidity reserve volatility index of each asset through the end-of-day balance of each asset, the end-of-day balance of the previous day and the liquidity reserve volatility index weight in the model; the single liquidity reserve volatility index, investment cycle and rate of return of each asset can be used as multiple dimensions to judge the asset quality results, making the analysis of asset quality results more accurate. Moreover, the method disclosed in the present disclosure is automatically executed, without manual analysis, with high efficiency and saving a lot of manpower.

[0006] In some embodiments, the asset quality monitoring method further includes: calculating a total asset liquidity reserve volatility index based on the single liquidity reserve volatility index of each of the m assets; and displaying the total asset liquidity reserve volatility index as monitoring information.

[0007] In some embodiments, the asset monitoring model calculates the single liquidity reserve volatility index based on the current day-end balance, the previous day-end balance and the liquidity reserve volatility index weight of each asset, including: calculating the increase data of each asset compared with the previous day based on the current day-end balance and the previous day-end balance of each of the m assets; calculating the single liquidity reserve volatility index of each asset based on the increase data compared with the previous day and the liquidity reserve volatility index weight of each asset among the m assets.

[0008] The step of calculating the total asset liquidity reserve volatility index according to the single liquidity reserve volatility index of each asset in the m assets comprises: adding the single liquidity reserve volatility index of each asset in the m assets to obtain the total asset liquidity reserve volatility index.

[0009] In some embodiments, the step of predetermining the liquidity reserve volatility index weight of each of the m assets includes: obtaining the total amount of all historical amounts realized for liquidity payments for each of the m assets in the asset system as the first total amount of single assets; obtaining the total amount of all liquidity payments realized on day t for each of the m assets in the asset system as the second total amount of single assets; and determining the liquidity reserve volatility index weight of each of the m assets based on the first total amount of single assets and the second total amount of single assets.

[0010] In some embodiments, the step of determining the liquidity reserve volatility index weight of each of the m assets based on the total amount of the first single asset and the total amount of the second single asset includes: constructing an error function based on the total amount of the first single asset, the total amount of the second single asset and the single intermediate value of each of the m assets, solving the error function for the minimum value to obtain the single intermediate value; determining the liquidity reserve volatility index weight of each of the m assets based on the single intermediate value and the total amount of the first single asset.

[0011] In some embodiments, an error function is constructed based on the first single asset total amount, the second single asset total amount and the single intermediate value of each asset in the m assets, and the error function is solved for the minimum value to obtain the single intermediate value, including: adding the first single asset total amount of each asset in the m assets to obtain the first asset total amount; dividing the first single asset total amount of each asset by the first asset total amount to obtain the historical proportion of the total amount of each asset that has been realized for liquidity payment as the proportion of the total amount of single assets; adding the second single asset total amount of each asset in the m assets to obtain the second asset total amount; constructing an error function based on the proportion of the total amount of single assets, the second asset total amount, the second single asset total amount and the single intermediate value of each asset in the m assets; solving the error function for the minimum value to obtain the single intermediate value.

[0012] In some embodiments, the step of determining the liquidity reserve volatility index weight of each of the m assets based on the single intermediate value and the total amount of the first single asset includes: determining the liquidity reserve volatility index weight of each of the m assets based on the proportion of the single intermediate value and the total amount of the single asset.

[0013] In some embodiments, the total asset liquidity reserve volatility index is displayed as monitoring information in a dashboard display manner, and the dashboard is configured as follows: the interval of the dashboard is -50% to +50%, wherein the interval of 0% to +50% is a first color, and the text shows healthy, the interval of -25% to 0% is a second color, and the text shows good, and the interval of -50% to -25% is a third color, and the text shows attention. The step of displaying the total asset liquidity reserve volatility index as monitoring information includes: the dashboard pointer points to the interval of the dashboard according to the total asset liquidity reserve volatility index.

[0014] The step of displaying the asset quality results as monitoring information includes: displaying high-quality assets or non-performing assets as text.

[0015] Another aspect of the present disclosure provides an asset quality monitoring device, including: an acquisition module, the acquisition module is used to execute acquisition of asset data of m assets in an asset system, wherein the asset data of each asset includes the asset's end-of-day balance, the previous day's end-of-day balance, the investment cycle and the rate of return, and m is an integer greater than or equal to 1; a monitoring module, the monitoring module is used to execute, according to the asset data, using a pre-constructed asset monitoring model to obtain an asset quality result, wherein the asset monitoring model includes a predetermined liquidity reserve volatility index weight of each of the m assets, the asset monitoring model also includes a mapping relationship between the single liquidity reserve volatility index of each of the m assets, the investment cycle and the rate of return and the asset quality result, the single liquidity reserve volatility index of each of the m assets is calculated by the asset monitoring model according to the asset's end-of-day balance, the previous day's end-of-day balance and the liquidity reserve volatility index weight of each asset, and the asset quality result is a high-quality asset or a non-performing asset; a display module, the display module is used to display the asset quality result as monitoring information.

[0016] Another aspect of the present disclosure provides an electronic device, comprising one or more processors and one or more memories, wherein the memories are used to store executable instructions, and when the executable instructions are executed by the processors, the above-mentioned method is implemented.

[0017] Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the above method when executed.

[0018] Another aspect of the present disclosure provides a computer program product, comprising a computer program, wherein the computer program comprises computer executable instructions, and the instructions are used to implement the method as described above when executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0020] Figure 1 An exemplary system architecture to which the method and apparatus according to an embodiment of the present disclosure can be applied is schematically shown;

[0021] Figure 2 A flowchart of an asset quality monitoring method according to an embodiment of the present disclosure is schematically shown;

[0022] Figure 3 A flowchart schematically shows the steps of calculating a single liquidity reserve volatility index according to the asset monitoring model of an embodiment of the present disclosure based on the current day-end balance, the previous day-end balance and the liquidity reserve volatility index weight of each asset;

[0023] Figure 4 A flowchart schematically illustrates the steps of predetermining the liquidity reserve volatility index weight of each asset in m assets according to an embodiment of the present disclosure;

[0024] Figure 5 A flowchart schematically illustrates the steps of determining the liquidity reserve volatility index weight of each of m assets according to the total amount of the first single asset and the total amount of the second single asset according to an embodiment of the present disclosure;

[0025] Figure 6 A flowchart schematically shows the steps of constructing an error function according to the total amount of the first single asset, the total amount of the second single asset, and the single intermediate value of each asset in the m assets, solving the error function for the minimum value, and obtaining the single intermediate value according to an embodiment of the present disclosure;

[0026] Figure 7 A block diagram of an asset quality monitoring device according to an embodiment of the present disclosure is schematically shown;

[0027] Figure 8 A block diagram of an electronic device according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0028] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0029] In the technical solution of the present disclosure, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0030] In the scenario of using personal information for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure provide users with corresponding operation portals for users to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating a person's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs, and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge and skills, and have reached a certain level of professionalism.

[0031] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0032] When using expressions such as "at least one of A, B or C, etc.", it should generally be interpreted in accordance with the meaning of the expression generally understood by those skilled in the art (for example, "a system having at least one of A, B or C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.). The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, features defined as "first" and "second" may explicitly or implicitly include one or more of the said features.

[0033] In the sustainable development of commercial banks, asset quality is the foundation for their survival. Therefore, commercial banks regard the monitoring of asset quality as one of their important daily tasks. At present, the monitoring of asset quality requires manual analysis of asset data in the system, so as to judge which assets in the system are high-quality assets and which assets are non-performing assets based on the analysis results. Manual analysis of asset data requires a lot of labor costs and is inefficient. More importantly, manual analysis of asset data is limited by human cognition and energy, and is prone to errors, resulting in low accuracy of analysis results, which affects the smooth progress of asset quality monitoring. Therefore, how to monitor asset quality efficiently and accurately has become a technical problem that technicians in this field urgently need to solve.

[0034] The embodiments of the present disclosure provide an asset quality monitoring method, device, electronic device, computer-readable storage medium and computer program product. The asset quality monitoring method includes: obtaining asset data of m assets in an asset system, wherein the asset data of each asset includes the asset's end-of-day balance, the previous day's end-of-day balance, investment cycle and rate of return, and m is an integer greater than or equal to 1; obtaining asset quality results based on the asset data using a pre-built asset monitoring model, wherein the asset monitoring model includes a predetermined liquidity reserve volatility index weight for each of the m assets, and the asset monitoring model also includes a mapping relationship between the single liquidity reserve volatility index, investment cycle and rate of return of each of the m assets and the asset quality results, the single liquidity reserve volatility index of each of the m assets is calculated by the asset monitoring model based on the asset's end-of-day balance, the previous day's end-of-day balance and the liquidity reserve volatility index weight of each asset, and the asset quality result is a high-quality asset or a non-performing asset; and the asset quality result is displayed as monitoring information.

[0035] It should be noted that the asset quality monitoring method, device, electronic device, computer-readable storage medium and computer program product disclosed in the present invention can be used in the field of big data technology, and can also be used in any field other than the field of big data technology, such as the financial field. The field of the present invention is not limited here.

[0036] Figure 1 The exemplary system architecture 100 to which the asset quality monitoring method, apparatus, electronic device, computer-readable storage medium and computer program product according to the embodiments of the present disclosure can be applied is schematically shown. It should be noted that: Figure 1 What is shown is merely an example of a system architecture to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0037] like Figure 1As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0038] The user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0039] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0040] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0041] It should be noted that the asset quality monitoring method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the asset monitoring device based on the liquidity reserve volatility index provided in the embodiment of the present disclosure can generally be set in the server 105. The asset quality monitoring method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the asset monitoring device based on the liquidity reserve volatility index provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0042] It should be understood that Figure 1The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0043] The following will be based on Figure 1 The scene described by Figure 2~Figure 6 The asset quality monitoring method of the embodiment of the present disclosure is described in detail.

[0044] Figure 2 The flowchart of the asset quality monitoring method according to the embodiment of the present disclosure is schematically shown.

[0045] like Figure 2 As shown, the asset quality monitoring method of this embodiment includes operations S210 to S230.

[0046] In operation S210, asset data of m assets in the asset system are obtained, wherein the asset data of each asset includes the day-end balance of the asset, the day-end balance of the previous day, the investment period and the rate of return, wherein m is an integer greater than or equal to 1.

[0047] In operation S220, the asset quality result is obtained according to the asset data using a pre-built asset monitoring model, wherein the asset monitoring model includes a predetermined liquidity reserve volatility index weight for each of the m assets, and the asset monitoring model also includes a mapping relationship between the single liquidity reserve volatility index of each of the m assets, the investment cycle, and the rate of return and the asset quality result, the single liquidity reserve volatility index of each of the m assets is calculated by the asset monitoring model based on the current day-end balance, the previous day's day-end balance and the liquidity reserve volatility index weight of each asset, and the asset quality result is a high-quality asset or a non-performing asset.

[0048] As some feasible ways, such as Figure 3 As shown, operation S220 is a step in which the asset monitoring model calculates a single liquidity reserve volatility index based on the current day-end balance, the previous day's end balance and the liquidity reserve volatility index weight of each asset, including operation S221 and operation S222.

[0049] In operation S221, the increase data of each asset compared with the previous day is calculated based on the end-of-day balance of each asset among the m assets and the end-of-day balance of the previous day.

[0050] In operation S222, the single liquidity reserve volatility index of each asset is calculated based on the increase data compared with the previous day and the liquidity reserve volatility index weight of each asset in the m assets. Here, the single liquidity reserve volatility index of each asset can be expressed as r iIndicates that i is the i-th asset among m assets. The single liquidity reserve volatility index of asset i can be obtained through formula (1).

[0051] r i = .( ) / (1)

[0052] in, represents the end-of-day balance of asset i, represents the previous day's end balance of asset i, ( ) / Indicates the increase in asset i compared to the previous day. Represents the liquidity reserve volatility index weight of asset i.

[0053] Operations S221 and S222 can facilitate the implementation of the step in which the asset monitoring model calculates the individual liquidity reserve volatility index based on the current day-end balance, the previous day's end balance and the liquidity reserve volatility index weight of each asset.

[0054] In some specific examples, the asset monitoring model also includes a mapping relationship between the single liquidity reserve volatility index, investment cycle and rate of return of each asset in the m assets and the asset quality results. The mapping relationship corresponding to each of the above assets can be obtained by training based on multiple historical asset data samples, and each historical asset data sample includes a historical single liquidity reserve volatility index, historical investment cycle, historical rate of return within a set historical time period, and a marked quality result of the asset within the historical time period.

[0055] As some possible implementation methods, such as Figure 4 As shown, the step of predetermining the liquidity reserve volatility index weight of each asset in m assets includes operations S310 to S330.

[0056] In operation S310, the total amount of all historical cash realized for liquidity payment of each asset in the m assets in the asset system is obtained as the total amount of the first single asset. Here, the total amount of the first single asset can be expressed as express.

[0057] In operation S320, the total amount of all liquidity payments realized on day t for each of the m assets in the asset system is obtained as the total amount of the second single asset. Here, the total amount of the second single asset can be expressed as express.

[0058] In operation S330, a liquidity reserve volatility index weight of each of the m assets is determined according to the total amount of the first single asset and the total amount of the second single asset.

[0059] As some possible implementation methods, such as Figure 5 As shown, operation S330 is a step of determining the liquidity reserve volatility index weight of each of the m assets according to the total amount of the first single asset and the total amount of the second single asset, including operation S331 and operation S332.

[0060] In operation S331, an error function is constructed according to the total amount of the first single asset, the total amount of the second single asset, and the single intermediate value of each asset in the m assets, and the error function is solved for a minimum value to obtain the single intermediate value.

[0061] As some feasible ways, such as Figure 6 As shown, operation S331 constructs an error function according to the total amount of the first single asset, the total amount of the second single asset and the single intermediate value of each asset in the m assets, solves the error function for the minimum value, and obtains the single intermediate value, including operation S3311~operation S3315.

[0062] In operation S3311, the total amount of the first single asset of each asset in the m assets is added up to obtain the total amount of the first asset. Here, the total amount of the first asset can be expressed as express.

[0063] In operation S3312, the total amount of the first single asset of each asset is divided by the total amount of the first asset to obtain the total amount of each asset that has been realized for liquidity payment in history as the total amount of the single asset. Here, the total amount of the single asset can be expressed as R i Indicates that the total amount of a single asset accounts for R i It can be obtained by formula (2).

[0064] (2)

[0065] In operation S3313, the second single asset total amount of each asset in the m assets is added up to obtain the second asset total amount. Here, the second asset total amount can be expressed as express.

[0066] In operation S3314, an error function is constructed according to the proportion of the total amount of the single asset, the total amount of the second asset, the total amount of the second single asset, and the single intermediate value of each asset in the m assets.

[0067] In operation S3315, the error function is solved for a minimum value to obtain a single intermediate value.

[0068] It can be understood that the error function can be constructed by formula (3), and the single intermediate value is expressed as r i express.

[0069] (3)

[0070] In formula (3), solving for the minimum value of L can give the single intermediate value r i Operations S3311 to S3315 can be used to easily implement the steps of constructing an error function according to the total amount of the first single asset, the total amount of the second single asset, and the single intermediate value of each asset in the m assets, solving the error function for the minimum value, and obtaining the single intermediate value.

[0071] In operation S332, the liquidity reserve volatility index weight of each of the m assets is determined according to the single intermediate value and the total amount of the first single asset.

[0072] As some possible implementation methods, operation S332 is a step of determining the liquidity reserve volatility index weight of each of the m assets based on the single intermediate value and the total amount of the first single asset, including operation S3321.

[0073] In operation S3321, the liquidity reserve volatility index weight of each asset in the m assets is determined based on the single intermediate value and the proportion of the total amount of the single asset. Here, the liquidity reserve volatility index weight of each asset can be obtained by formula (4): .

[0074] (4)

[0075] Operation S3321 can facilitate the step of determining the liquidity reserve volatility index weight of each of the m assets based on the single intermediate value and the total amount of the first single asset.

[0076] Operations S331 and S332 can facilitate the step of determining the liquidity reserve volatility index weight of each of the m assets according to the total amount of the first single asset and the total amount of the second single asset.

[0077] Operations S310 to S320 may facilitate the step of predetermining the liquidity reserve volatility index weight of each of the m assets.

[0078] In operation S230, the asset quality result is displayed as monitoring information.

[0079] According to the asset quality monitoring method of the embodiment of the present disclosure, by acquiring the asset data of m assets in the asset system, wherein the asset data of each asset includes the day-end balance of the asset, the day-end balance of the previous day, the investment cycle and the rate of return; the asset quality result can be obtained according to the asset data by using a pre-constructed asset monitoring model, wherein the asset monitoring model includes a predetermined liquidity reserve volatility index weight of each of the m assets, and the asset monitoring model also includes a mapping relationship between the single liquidity reserve volatility index of each of the m assets, the investment cycle and the rate of return and the asset quality result, the single liquidity reserve volatility index of each of the m assets is calculated by the asset monitoring model according to the day-end balance of each asset, the day-end balance of the previous day and the liquidity reserve volatility index weight, and the asset quality result is a high-quality asset or a non-performing asset; and the asset quality result is then displayed as monitoring information. The asset detection model pre-built in the present disclosure can obtain the single liquidity reserve volatility index of each asset through the end-of-day balance of each asset, the end-of-day balance of the previous day and the liquidity reserve volatility index weight in the model; the single liquidity reserve volatility index, investment cycle and rate of return of each asset can be used as multiple dimensions to judge the asset quality results, making the analysis of asset quality results more accurate. Moreover, the method disclosed in the present disclosure is automatically executed, without manual analysis, with high efficiency and saving a lot of manpower.

[0080] According to some embodiments of the present disclosure, the asset quality monitoring method further includes operation S001 and operation S002.

[0081] In operation S001, the total asset liquidity reserve volatility index is calculated according to the single liquidity reserve volatility index of each asset in the m assets.

[0082] As some implementable methods, the asset monitoring model calculates the steps of a single liquidity reserve volatility index based on the end-of-day balance of each asset, the end-of-day balance of the previous day and the liquidity reserve volatility index weight, including operation S221 and operation S222.

[0083] In operation S221, the increase data of each asset compared with the previous day is calculated based on the end-of-day balance of each asset among the m assets and the end-of-day balance of the previous day.

[0084] In operation S222, the single liquidity reserve volatility index of each asset is calculated based on the increase data compared with the previous day and the liquidity reserve volatility index weight of each asset in the m assets. Here, the single liquidity reserve volatility index of each asset can be expressed as r i Indicates that i is the i-th asset among m assets. The single liquidity reserve volatility index of asset i can be obtained through formula (1).

[0085] ri = .( ) / (1)

[0086] in, represents the end-of-day balance of asset i, represents the previous day's end balance of asset i, ( ) / Indicates the increase in asset i compared to the previous day. Represents the liquidity reserve volatility index weight of asset i.

[0087] Operations S221 and S222 can facilitate the implementation of the step in which the asset monitoring model calculates the individual liquidity reserve volatility index based on the current day-end balance, the previous day's end balance and the liquidity reserve volatility index weight of each asset.

[0088] Operation S001 is a step of calculating the total asset liquidity reserve volatility index according to the single liquidity reserve volatility index of each asset in the m assets, including operation S0011.

[0089] In operation S0011, the single liquidity reserve volatility index of each asset in the m assets is added up to obtain the total asset liquidity reserve volatility index.

[0090] It can be understood that the total asset liquidity reserve volatility index can be represented by Y, and the total asset liquidity reserve volatility index Y can be calculated by formula (5).

[0091] (5)

[0092] in, represents the end-of-day balance of asset i, represents the previous day's end balance of asset i, ( ) / Indicates the increase in asset i compared to the previous day. represents the liquidity reserve volatility index weight of asset i, .( ) / Represents the single liquidity reserve volatility index of asset i.

[0093] Operation S0011 can facilitate the step of calculating the total asset liquidity reserve volatility index based on the single liquidity reserve volatility index of each asset in the m assets.

[0094] In operation S002, the total asset liquidity reserve fluctuation index is displayed as monitoring information. It can be understood that the disclosure uses the end-of-day balance of m assets and the end-of-day balance of the previous day as reference dimensions to obtain the total asset liquidity reserve fluctuation index, which can accurately characterize the liquidity reserve fluctuation of the bank's total assets. Displaying the liquidity reserve fluctuation index as monitoring information can accurately monitor the liquidity reserve fluctuation of the bank's total assets, thereby providing decision makers with accurate decision-making basis.

[0095] As some possible implementation methods, the total asset liquidity reserve volatility index is displayed as a dashboard display as monitoring information, and the dashboard is configured as follows: the dashboard interval is -50% to +50%, wherein the 0% to +50% interval is a first color (e.g., green), and the text shows health, the -25% to 0% interval is a second color (e.g., yellow), and the text shows good, and the -50% to -25% interval is a third color (e.g., red), and the text shows attention, and operation S230 is a step of displaying the total asset liquidity reserve volatility index as monitoring information, including operation S231: the dashboard pointer points to the interval of the dashboard according to the total asset liquidity reserve volatility index. In this way, the monitoring results (total asset liquidity reserve volatility index) can be clearly displayed, which is convenient for monitors to obtain key information. For example, the monitor can perform operations based on the monitoring results and actual conditions: if the text displays as "healthy", no adjustment to the liquidity reserve may be made, and asset investment may be considered to increase the profitability of assets; if the text displays as "good", whether liquidity reserve adjustments are needed can be determined based on actual conditions; if the text displays as "concern", it is necessary to focus on the liquidity reserve asset situation, take inventory of existing assets and future cash flows based on actual conditions, and make corresponding liquidity reserve adjustments to enhance liquidity reserves.

[0096] The step of displaying the asset quality results as monitoring information includes: displaying high-quality assets or non-performing assets as text, so that the monitor can obtain the asset quality result information at a glance.

[0097] The asset quality monitoring method according to the embodiment of the present disclosure is described in detail below. It is worth noting that the following description is only an exemplary description and is not a specific limitation of the present disclosure.

[0098] At present, there is a lack of accurate and efficient monitoring methods for liquidity reserve monitoring. The present invention evaluates the liquidity of each asset based on its trading conditions and assigns weights accordingly. The volatility of each liquidity reserve asset is weighted and calculated to finally obtain a liquidity reserve volatility index. The index result is then compared with a preset range to obtain the final situation of the current liquidity reserve volatility index.

[0099] The asset quality monitoring method of the embodiment of the present disclosure includes the following steps.

[0100] Step 1: Data selection and preprocessing: Obtain product data that can be used as liquidity reserve assets through the database of commercial banks or financial institutions. The statistical caliber of business varieties includes, for example, "account deposits", "borrowing repayments" by term, "buy-back repayments", "open-ended repurchases", "interbank deposit certificate investment remittances" and other assets that can be used as liquidity reserve calculations. The balances of various assets on day t are , store the daily end-of-day balance data of each asset in the database for calculation in step 2. Through more refined maturity distinction, the liquidity of each liquidity reserve asset can be better reflected and calculated.

[0101] Step 2: Establish a liquidity reserve volatility index model: The liquidity reserve volatility index model is designed to reflect the volatility of liquidity reserves. The data in step 1 is used as the basic data for calculation. The increase of each asset compared with the previous day = (the balance at the end of the day of this day - the balance at the end of the day of the previous day) / the balance at the end of the day of the previous day. The weight ratio assigned to each type of asset is calculated based on historical data. The liquidity reserve volatility index model is established as follows: Assume that the end-of-day scale of each type of asset selected in step 1 is Yuan, the previous day's end scale was Yuan, the liquidity reserve volatility index weight corresponding to asset i is , then the calculation formula of liquidity reserve volatility index Y is: .

[0102] Step 3: Determination of the weight of the liquidity reserve volatility index: In the calculation formula of step 2, and The data can be directly obtained from the commercial bank database, but the weight corresponding to asset i It needs to be calculated by statistically analyzing the historical data of commercial banks or financial institutions. The asset types with stronger liquidity conversion ability should have a greater weight ratio. The liquidity conversion ability can be determined by the historical data of all product amounts used by commercial banks to convert into cash for liquidity payments.

[0103] Suppose the total amount of all historical cash realized for liquidity payment recorded in the i-asset system is , the total amount of all liquidity reserve assets recorded in the system that are converted into cash for liquidity payment during the same period is ,but The obtained product is used for the historical data of liquidity payment ratio result set Based on the existing database of commercial banks, the actual amount of liquidity payment realized by each product on day t of product i is , the amount actually realized for liquidity payment on day t for each product , establish the supervision result vector = , establish the error function: .

[0104] Solving the minimum value of the error function yields , then the weight of the liquidity reserve volatility index of product i is .

[0105] Step 4: Front-end display of the liquidity reserve volatility index model: The system uses the calculation results stored in the database in step 1, calculates the liquidity reserve volatility index results according to the model calculation method established in steps 2 and 3, and stores the results in the database. The liquidity reserve volatility index data stored in the database is displayed in the form of a dashboard at the front end of the system. The range of the dashboard is -50% to +50%: 0% to +50% is green, and the text shows "healthy"; -25% to 0% is yellow, and the text shows "good"; -50% to -25% is red, and the text shows "attention". The final result calculated according to the model is displayed in the dashboard in the form of a pointer.

[0106] Step 5: Application of the liquidity reserve volatility index model: Based on the results obtained from the above model, the monitor can perform operations based on the monitoring results and actual conditions: If the text displays as "healthy", no liquidity reserve adjustment is required, and asset investment can be considered to increase the profitability of assets; if the text displays as "good", it can be determined based on actual conditions whether liquidity reserve adjustments are needed; if the text displays as "concern", it is necessary to focus on the liquidity reserve assets, take inventory of existing assets and future cash flows based on actual conditions, and make corresponding liquidity reserve adjustments to enhance liquidity reserves.

[0107] The main effect of the present disclosure is to help commercial banks or other financial institutions quickly obtain the current liquidity reserve situation, make timely adjustments when the liquidity reserve is insufficient, and ensure that the current liquidity reserve is in the most reasonable state range. Compared with the existing methods, the present disclosure has the following main advantages: 1. The present disclosure provides an index model tool that represents the current liquidity reserve situation, which is completely calculated by AI. Compared with the previous process of manually determining the weights according to the term classification and then calculating, the present disclosure can directly calculate the current liquidity reserve volatility index through AI. 2. In the existing methods, the liquidity reserve assessment is graded according to the asset term, and fixed weights are assigned to liquidity reserve assets at each level. The present disclosure analyzes the transaction data of each asset that has been realized for liquidity payment in history, assigns different weights to each asset, and calculates the current liquidity reserve volatility index through AI. The asset weights in the model are calculated through the loss function model. The result is more meaningful than the term classification.

[0108] Based on the above asset quality monitoring method, the present disclosure also provides an asset monitoring device based on the liquidity reserve volatility index. Figure 7 An asset monitoring device based on a liquidity reserve volatility index is described in detail.

[0109] Figure 7 The structural block diagram of the asset monitoring device based on the liquidity reserve volatility index according to an embodiment of the present disclosure is schematically shown.

[0110] The asset monitoring device 10 based on the liquidity reserve volatility index includes an acquisition module 1 , a monitoring module 2 and a display module 3 .

[0111] The acquisition module 1 is used to execute the acquisition of asset data of m assets in the asset system, wherein the asset data of each asset includes the end-of-day balance of the asset, the end-of-day balance of the previous day, the investment period and the rate of return, and m is an integer greater than or equal to 1;

[0112] The monitoring module 2 is used to execute the asset quality result according to the asset data using a pre-built asset monitoring model, wherein the asset monitoring model includes a predetermined liquidity reserve volatility index weight of each of the m assets, and the asset monitoring model also includes a mapping relationship between the single liquidity reserve volatility index of each of the m assets, the investment cycle, the yield and the asset quality result, the single liquidity reserve volatility index of each of the m assets is calculated by the asset monitoring model based on the day-end balance of each asset, the day-end balance of the previous day and the liquidity reserve volatility index weight, and the asset quality result is a high-quality asset or a non-performing asset;

[0113] The display module 3 is used to display the asset quality results as monitoring information.

[0114] According to some embodiments of the present disclosure, the asset quality monitoring device further includes an auxiliary calculation module and an auxiliary display module.

[0115] The auxiliary calculation module is used to calculate the total asset liquidity reserve volatility index based on the single liquidity reserve volatility index of each asset in the m assets.

[0116] The auxiliary display module is used to display the total asset liquidity reserve volatility index as monitoring information.

[0117] According to some embodiments of the present disclosure, the calculation module includes: a first calculation unit, a second calculation unit and a first determination unit.

[0118] The first calculation unit is used to calculate the increase data of each asset compared with the previous day based on the end-of-day balance of each asset in the m assets and the end-of-day balance of the previous day.

[0119] The second calculation unit is used to calculate the single liquidity reserve volatility index of each asset according to the increase data compared with the previous day and the predetermined liquidity reserve volatility index weight of each asset in the m assets.

[0120] The first determining unit is used to add up the single liquidity reserve volatility index of each asset in the m assets to obtain the total asset liquidity reserve volatility index.

[0121] According to some embodiments of the present disclosure, the asset monitoring device based on the liquidity reserve volatility index also includes a pre-determination module, which is used to pre-determine the liquidity reserve volatility index weight of each asset among the m assets, and the pre-determination module includes: a first acquisition unit, a second acquisition unit and a second determination unit.

[0122] The first acquisition unit is used to acquire the total amount of each asset in the m assets in the asset system that has been historically converted into cash due to liquidity payment as the total amount of the first single asset.

[0123] The second acquisition unit is used to acquire the total amount of all liquidity payments realized on day t for each of the m assets in the asset system as the total amount of the second single asset.

[0124] The second determining unit is used to determine the liquidity reserve volatility index weight of each of the m assets according to the total amount of the first single asset and the total amount of the second single asset.

[0125] According to some embodiments of the present disclosure, the second determining unit includes: a first determining element and a second determining element.

[0126] The first determining element is used to construct an error function according to the first single asset total amount, the second single asset total amount and the single intermediate value of each asset in the m assets, and solve the error function for a minimum value to obtain the single intermediate value.

[0127] The second determining element is used to determine the liquidity reserve volatility index weight of each of the m assets according to the single intermediate value and the total amount of the first single asset.

[0128] According to some embodiments of the present disclosure, the first determining element includes: a first determining unit, a second determining unit, a third determining unit, a constructing unit, and a fourth determining unit.

[0129] The first determining unit is used to add up the first single asset total amount of each asset in the m assets to obtain a first asset total amount.

[0130] The second determination unit is used to divide the total amount of the first single asset of each asset by the total amount of the first assets to obtain the historical proportion of the total amount of each asset realized for liquidity payment as the proportion of the total amount of the single asset.

[0131] The third determining unit is used to add up the second single asset total amount of each asset in the m assets to obtain the second asset total amount.

[0132] The construction unit is used to construct an error function according to the proportion of the total amount of the single asset, the total amount of the second asset, the total amount of the second single asset and the single intermediate value of each asset in the m assets.

[0133] The fourth determination unit is used to solve the minimum value of the error function to obtain the single intermediate value.

[0134] According to the asset monitoring device 10 based on the liquidity reserve volatility index of the embodiment of the present disclosure, by acquiring the asset data of m assets in the asset system, wherein the asset data of each asset includes the asset's day-end balance, the day-end balance of the previous day, the investment cycle and the rate of return; the asset quality result can be obtained according to the asset data by using a pre-constructed asset monitoring model, wherein the asset monitoring model includes a predetermined liquidity reserve volatility index weight of each of the m assets, and the asset monitoring model also includes a mapping relationship between the single liquidity reserve volatility index, the investment cycle and the rate of return of each of the m assets and the asset quality result, the single liquidity reserve volatility index of each of the m assets is calculated by the asset monitoring model according to the day-end balance of each asset, the day-end balance of the previous day and the liquidity reserve volatility index weight, and the asset quality result is a high-quality asset or a non-performing asset; and the asset quality result is then displayed as monitoring information. The asset detection model pre-built in the present disclosure can obtain the single liquidity reserve volatility index of each asset through the end-of-day balance of each asset, the end-of-day balance of the previous day and the liquidity reserve volatility index weight in the model; the single liquidity reserve volatility index, investment cycle and rate of return of each asset can be used as multiple dimensions to judge the asset quality results, making the analysis of asset quality results more accurate. Moreover, the method disclosed in the present disclosure is automatically executed, without manual analysis, with high efficiency and saving a lot of manpower.

[0135] In addition, according to an embodiment of the present disclosure, any multiple modules among the acquisition module 1, the monitoring module 2 and the display module 3 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module.

[0136] According to an embodiment of the present disclosure, at least one of the acquisition module 1, the monitoring module 2 and the display module 3 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or can be implemented in any one of the three implementation methods of software, hardware and firmware, or in an appropriate combination of any of them.

[0137] Alternatively, at least one of the acquisition module 1, the monitoring module 2 and the display module 3 may be at least partially implemented as a computer program module, and when the computer program module is executed, the corresponding function may be executed.

[0138] Figure 8A block diagram of an electronic device suitable for implementing the above method according to an embodiment of the present disclosure is schematically shown.

[0139] like Figure 8 As shown, the electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage part 908 to a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include an onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0140] In RAM 903, various programs and data required for the operation of electronic device 900 are stored. Processor 901, ROM 902 and RAM 903 are connected to each other via bus 904. Processor 901 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 902 and / or RAM 903. It should be noted that the program can also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0141] According to an embodiment of the present disclosure, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to the bus 904. The electronic device 900 may further include one or more of the following components connected to the input / output (I / O) interface 905: an input portion 906 including a keyboard, a mouse, etc.; an output portion 907 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 908 including a hard disk, etc.; and a communication portion 909 including a network interface card such as a LAN card, a modem, etc. The communication portion 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the input / output (I / O) interface 905 as needed. A removable medium 911, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 910 as needed, so that a computer program read therefrom is installed into the storage portion 908 as needed.

[0142] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0143] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 902 and / or RAM 903 described above and / or one or more memories other than ROM 902 and RAM 903.

[0144] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the method provided by the embodiment of the present disclosure.

[0145] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 901. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0146] In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium, and downloaded and installed through the communication part 909, and / or installed from a removable medium 911. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0147] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0148] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0149] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0150] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0151] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for monitoring asset quality, characterized in that: include: Obtain asset data of m assets in the asset system, wherein the asset data of each asset includes the end-of-day balance of the asset, the end-of-day balance of the previous day, the investment period and the rate of return, and m is an integer greater than or equal to 1; According to the asset data, an asset quality result is obtained by using a pre-constructed asset monitoring model, wherein the asset monitoring model includes a predetermined liquidity reserve volatility index weight of each of the m assets, and the asset monitoring model also includes a mapping relationship between the single liquidity reserve volatility index of each of the m assets, the investment cycle, the yield and the asset quality result, the single liquidity reserve volatility index of each of the m assets is calculated by the asset monitoring model based on the current day-end balance of each asset, the previous day's day-end balance and the liquidity reserve volatility index weight, and the asset quality result is a high-quality asset or a non-performing asset; The asset quality results are presented as monitoring information.

2. The asset quality monitoring method according to claim 1, characterized in that: Also includes: Calculate the total asset liquidity reserve volatility index based on the single liquidity reserve volatility index of each asset in the m assets; The total asset liquidity reserve volatility index is displayed as monitoring information.

3. The asset quality monitoring method according to claim 2, characterized in that: The step of calculating the single liquidity reserve volatility index according to the asset monitoring model's end-of-day balance of each asset, the end-of-day balance of the previous day and the liquidity reserve volatility index weight includes: Calculate the increase of each asset compared with the previous day based on the end-of-day balance of each asset among the m assets and the end-of-day balance of the previous day; Calculate the single liquidity reserve volatility index of each asset based on the increase data compared to the previous day and the liquidity reserve volatility index weight of each asset in the m assets; The step of calculating the total asset liquidity reserve volatility index according to the single liquidity reserve volatility index of each asset in the m assets comprises: The single liquidity reserve volatility index of each asset in the m assets is added up to obtain the total asset liquidity reserve volatility index.

4. The asset quality monitoring method according to claim 1, characterized in that: The step of predetermining the liquidity reserve volatility index weight of each of the m assets comprises: Obtain the total amount of all historical cash realized for liquidity payment for each of the m assets in the asset system as the total amount of the first single asset; Obtain the total amount of all liquidity payments realized on day t for each of the m assets in the asset system as the total amount of the second single asset; The liquidity reserve volatility index weight of each of the m assets is determined according to the total amount of the first single asset and the total amount of the second single asset.

5. The asset quality monitoring method according to claim 4, characterized in that: The step of determining the liquidity reserve volatility index weight of each of the m assets according to the total amount of the first single asset and the total amount of the second single asset includes: Constructing an error function according to the total amount of the first single asset, the total amount of the second single asset, and the single intermediate value of each asset in the m assets, solving the error function for a minimum value, and obtaining the single intermediate value; The liquidity reserve volatility index weight of each of the m assets is determined based on the single intermediate value and the total amount of the first single asset.

6. The asset quality monitoring method according to claim 5, characterized in that: The step of constructing an error function according to the total amount of the first single asset, the total amount of the second single asset, and the single intermediate value of each asset in the m assets, solving the error function for a minimum value, and obtaining the single intermediate value includes: Add the total amount of the first single asset of each of the m assets to obtain the total amount of the first assets; Divide the total amount of the first single asset of each asset by the total amount of the first asset to obtain the total amount of each asset that has been realized for liquidity payment in history as the total amount of the single asset; Add the total amount of the second single asset of each of the m assets to obtain the total amount of the second assets; Constructing an error function according to the proportion of the total amount of the single asset, the total amount of the second asset, the total amount of the second single asset, and the single intermediate value of each asset in the m assets; The error function is solved for a minimum value to obtain the single intermediate value.

7. The asset quality monitoring method according to claim 6, characterized in that: The step of determining the liquidity reserve volatility index weight of each of the m assets according to the single intermediate value and the total amount of the first single asset includes: The liquidity reserve volatility index weight of each of the m assets is determined based on the single intermediate value and the proportion of the total amount of the single asset.

8. The asset quality monitoring method according to claim 2, characterized in that: The total asset liquidity reserve volatility index is displayed as a dashboard as monitoring information. The dashboard is configured as follows: the interval of the dashboard is -50% to +50%, wherein the interval of 0% to +50% is the first color, and the text shows health; the interval of -25% to 0% is the second color, and the text shows good; the interval of -50% to -25% is the third color, and the text shows concern. The step of displaying the total asset liquidity reserve volatility index as monitoring information includes: The pointer of the dashboard points to the interval of the dashboard according to the total asset liquidity reserve fluctuation index; The step of displaying the asset quality results as monitoring information includes: Present good assets or bad assets as text.

9. An asset quality monitoring device, characterized in that: include: An acquisition module, the acquisition module is used to execute acquisition of asset data of m assets in the asset system, wherein the asset data of each asset includes the end-of-day balance of the asset, the end-of-day balance of the previous day, the investment period and the rate of return, and m is an integer greater than or equal to 1; A monitoring module, the monitoring module is used to execute, according to the asset data, using a pre-built asset monitoring model to obtain an asset quality result, wherein the asset monitoring model includes a predetermined liquidity reserve volatility index weight of each of the m assets, and the asset monitoring model also includes a mapping relationship between a single liquidity reserve volatility index of each of the m assets, the investment cycle, and the rate of return and the asset quality result, the single liquidity reserve volatility index of each of the m assets is calculated by the asset monitoring model based on the day-end balance of each asset, the day-end balance of the previous day and the liquidity reserve volatility index weight, and the asset quality result is a high-quality asset or a non-performing asset; A display module is used to display the asset quality results as monitoring information.

10. An electronic device comprising: one or more processors; a storage device for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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