Data processing method and device for intelligent decision system, equipment and medium

Abstract

The invention provides a data processing method and device for an intelligent decision-making system, equipment, a medium and a product, and can be applied to the technical field of big data, the technical field of artificial intelligence and the field of financial science and technology. The data processing method comprises the following steps: according to a target time period, a target product and a historical time period, obtaining target holder attribute information, target product holding state information, target economic state information, historical holder attribute information, historical product holding state information, historical economic state information and historical resource quantity increment information in a historical time period; determining a resource quantity prediction model by using the historical holder attribute information, the historical holding state information, the historical economic state information and the historical resource quantity value-added information; inputting the target holder attribute information, the target holding state information and the target economic state information into a resource quantity prediction model to obtain target resource quantity value-added information; and generating early warning decision information according to the target resource quantity value-added information.

Description

Technical Field

[0001] The present disclosure relates to the fields of big data technology, artificial intelligence technology, and financial technology, and more specifically to a data processing method, device, equipment, medium, and product for an intelligent decision-making system. Background Art

[0002] When using decision-making systems to make early warning decisions, it's usually necessary to sequentially match different indicator information with rules and generate early warning decision information based on the matching results. However, this rule-matching approach is not only inflexible and difficult to tailor to specific needs, but also time-consuming when the amount of indicator information is large, reducing decision-making efficiency. Summary of the Invention

[0003] In view of the above problems, the present disclosure provides a data processing method, apparatus, device, medium and program product for an intelligent decision-making system.

[0004] According to the first aspect of the present disclosure, a data processing method for an intelligent decision-making system is provided, comprising: in response to receiving a data processing instruction sent by a user, determining a target time period and a target product from the above data processing instruction; obtaining, from a database, target holder attribute information and target product holding status information of the holder of the above target product within the above target time period, target economic status information of the above target time period, historical holder attribute information and historical product holding status information of the holder of the above target product within the above historical time period, historical economic status information of the above historical time period, and historical resource value-added information of the above target product within the above historical time period, according to the above target time period, the above target product and the historical time period corresponding to the above target product, wherein the duration of the above historical time period is equal to the duration of the above target time period; determining a resource quantity prediction model by using the above historical holder attribute information, the above historical holding status information, the above historical economic status information, and the above historical resource quantity value-added information; inputting the above target holder attribute information, the above target holding status information, and the above target economic status information into the above resource quantity prediction model to obtain target resource quantity value-added information; and generating early warning decision information based on the above target resource quantity value-added information.

[0005] According to an embodiment of the present disclosure, the above method also includes: generating a first mapping relationship based on the above target product and the above target resource value-added information; storing the above first mapping relationship in a product value-added mapping table; and generating query permission information for the above first mapping relationship in response to the above user's query permission setting operation for the above first mapping relationship, wherein the above query permission information includes the user identity information having the permission to query the above first mapping relationship.

[0006] According to an embodiment of the present disclosure, the above method includes: in response to receiving a query request, determining the product to be queried from the above query request; determining a second mapping relationship matching the above product to be queried from the above product value-added mapping table; verifying the user identity information corresponding to the above query request based on the query authority information of the above second mapping relationship to obtain an authority verification result; and displaying the above second mapping relationship when the above authority verification result indicates that the user identity information corresponding to the above query request has the authority to query the above second mapping relationship.

[0007] According to an embodiment of the present disclosure, the above-mentioned data processing instruction also includes a target screening range, and the above-mentioned acquisition of target holder attribute information and target product holding status information of the holder of the above-mentioned target product within the above-mentioned target period, target economic status information of the above-mentioned target period, historical holder attribute information and historical product holding status information of the holder of the above-mentioned target product within the above-mentioned historical period, historical economic status information of the above-mentioned historical period, and historical resource value-added information of the above-mentioned target product within the above-mentioned historical period includes: determining from the above-mentioned database the target holder attribute information set and target product holding status information set of the holder of the above-mentioned target product within the above-mentioned target period, and the target economic status information set of the above-mentioned target period; determining from the above-mentioned database the historical holder attribute information and historical product holding status information of the holder of the above-mentioned target product within the above-mentioned historical period The holder attribute information set and the historical product holding status information set, the historical economic status information set of the above-mentioned historical period, and the above-mentioned historical resource value-added information; according to the above-mentioned target screening range, screening is performed in the above-mentioned target holder attribute information set, the above-mentioned target product holding status information set and the above-mentioned target economic status information set respectively, and the above-mentioned target holder attribute information, the above-mentioned target product holding status information and the above-mentioned target economic status information corresponding to the above-mentioned target screening range are obtained; and according to the above-mentioned target screening range, screening is performed in the above-mentioned historical holder attribute information set, the above-mentioned historical product holding status information set and the above-mentioned historical economic status information set respectively, and the above-mentioned historical holder attribute information, the above-mentioned historical product holding status information and the above-mentioned historical economic status information set are obtained.

[0008] According to an embodiment of the present disclosure, the above-mentioned target holder attribute information includes target holder attribute sub-information corresponding to each of the multiple preset scenarios, the above-mentioned target holding status information includes target holding status sub-information corresponding to each of the above-mentioned multiple preset scenarios, and the above-mentioned target economic status information includes target economic status sub-information corresponding to each of the above-mentioned multiple preset scenarios. The above-mentioned target holder attribute information, the above-mentioned target holding status information and the above-mentioned target economic status information are input into the above-mentioned resource quantity prediction model to obtain target resource quantity value-added information, including: for each of the above-mentioned preset scenarios, the target holder attribute sub-information, target holding status sub-information and target economic status information corresponding to the above-mentioned preset scenario are input into the above-mentioned resource quantity prediction model to obtain the target resource quantity value-added sub-information corresponding to the above-mentioned preset scenario; and the above-mentioned target resource quantity value-added information is generated according to the target resource quantity value-added sub-information of each of the above-mentioned preset scenarios.

[0009] According to an embodiment of the present disclosure, each of the above-mentioned preset scenarios has its own weight. The above-mentioned target resource value-added information is generated according to the target resource value-added sub-information of each of the above-mentioned preset scenarios, including: performing a weighted operation on the target resource value-added sub-information of each of the above-mentioned preset scenarios according to the weight of each of the above-mentioned preset scenarios to obtain the weighted target resource value-added sub-information of each of the above-mentioned preset scenarios; and performing a summation operation on the weighted target resource value-added sub-information of each of the above-mentioned multiple preset scenarios to obtain the above-mentioned target resource value-added information.

[0010] According to an embodiment of the present disclosure, the method further includes: for each of the preset scenarios, generating a weight of the preset scenario according to the objective sub-weight and the subjective sub-weight of the preset scenario.

[0011] According to an embodiment of the present disclosure, the above-mentioned utilization of the above-mentioned historical holder attribute information, the above-mentioned historical holding status information, the above-mentioned historical economic status information and the above-mentioned historical resource quantity value-added information to determine the resource quantity prediction model includes: configuring the hyperparameters of the initial resource quantity prediction model; using the above-mentioned historical holder attribute information, the above-mentioned historical holding status information and the above-mentioned historical economic status information as the independent variables of the above-mentioned initial resource quantity prediction model, using the above-mentioned historical resource quantity value-added information as the dependent variable of the above-mentioned initial resource quantity prediction model, adjusting the adjustable initial model parameters in the above-mentioned initial resource quantity prediction model to obtain adjusted target model parameters; and determining the above-mentioned resource quantity prediction model based on the above-mentioned hyperparameters and the above-mentioned target model parameters.

[0012] The second aspect of the present disclosure provides a data processing device for an intelligent decision-making system, characterized in that the device comprises: a first determining module for determining a target time period and a target product from the data processing instruction in response to receiving a data processing instruction sent by a user; an acquiring module for acquiring, from a database, target holder attribute information and target product holding status information of the holder of the target product within the target time period, target economic status information of the target time period, historical holder attribute information and historical product holding status information of the holder of the target product within the historical time period, and the historical period corresponding to the target product. The historical economic status information of the historical period and the historical resource value-added information of the target product during the historical period, wherein the length of the historical period is equal to the length of the target period; a second determination module is used to determine a resource quantity prediction model by using the historical holder attribute information, the historical holding status information, the historical economic status information and the historical resource value-added information; a third determination module is used to input the target holder attribute information, the target holding status information and the target economic status information into the resource quantity prediction model to obtain the target resource value-added information; and a generation module is used to generate early warning decision information based on the target resource value-added information.

[0013] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0014] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the computer program or instructions are executed by a processor.

[0015] The fifth aspect of the present disclosure further provides a computer program product, comprising a computer program or instructions, which implement the steps of the above method when executed by a processor.

[0016] According to the embodiments of the present disclosure, by issuing early warnings based on historical information and target information that meet user needs according to the target time period and target product in the data processing instructions, not only can the resulting early warning decision information meet user needs, but it can also reduce the amount of data required to generate the early warning decision information, reduce the impact of redundant data on the early warning decision process, and thus improve the efficiency of generating early warning decision information. Furthermore, by automatically constructing a resource quantity prediction model based on historical information, using the resource quantity prediction model to predict target resource quantity value-added information based on target information, and then generating early warning decision information based on the target resource value-added information, the intelligence level of the early warning decision process is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above contents 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:

[0018] Figure 1 Schematically illustrates an application scenario diagram of a data processing method, apparatus, device, medium, and program product for an intelligent decision-making system according to an embodiment of the present disclosure;

[0019] Figure 2 The flowchart of the data processing method for the intelligent decision-making system according to the embodiment of the present disclosure is schematically shown;

[0020] Figure 3 Schematically shows a data flow diagram for generating early warning decision information according to a specific embodiment of the present disclosure;

[0021] Figure 4 A structural block diagram schematically shows a data processing device for an intelligent decision-making system according to an embodiment of the present disclosure; and

[0022] Figure 5 A block diagram of an electronic device suitable for implementing a data processing method for an intelligent decision-making system according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0023] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, 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 well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0024] 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 presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0025] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0026] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and 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.).

[0027] In the technical solutions disclosed herein, 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.

[0028] In scenarios where personal information is used for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure all provide users with corresponding operation portals for them 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 an individual'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.

[0029] An embodiment of the present disclosure provides a data processing method for an intelligent decision-making system, the method comprising: in response to receiving a data processing instruction sent by a user, determining a target time period and a target product from the data processing instruction; obtaining target holder attribute information and target product holding status information of the holder of the target product within the target time period, target economic status information of the target time period, historical holder attribute information and historical product holding status information of the holder of the target product within the historical time period, historical economic status information of the historical time period, and historical resource value-added information of the target product within the historical time period from a database according to the target time period, the target product, and the historical time period corresponding to the target product, wherein the length of the historical time period is equal to the length of the target time period; determining a resource quantity prediction model by using the historical holder attribute information, the historical holding status information, the historical economic status information, and the historical resource quantity value-added information; inputting the target holder attribute information, the target holding status information, and the target economic status information into the resource quantity prediction model to obtain target resource quantity value-added information; and generating early warning decision information based on the target resource quantity value-added information.

[0030] Figure 1 The application scenario diagram of the data processing method, apparatus, device, medium and program product for an intelligent decision-making system according to an embodiment of the present disclosure is schematically shown.

[0031] like Figure 1 As 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 as a medium for providing 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 or wireless communication links or optical fiber cables.

[0032] A user may use a first terminal device 101, a second terminal device 102, or a third terminal device 103 to interact with a server 105 via a network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, or the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (for example only).

[0033] 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.

[0034] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports 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 received data such as user requests, and feed back processing results (e.g., web pages, information, or data obtained or generated based on user requests) to the terminal devices.

[0035] It should be noted that the data processing method for the intelligent decision-making system provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the data processing device for the intelligent decision-making system provided in the embodiment of the present disclosure can generally be set in the server 105. The data processing method for the intelligent decision-making system 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 data processing device for the intelligent decision-making system 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.

[0036] For example, the user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to send a data processing instruction to the server 105 through the network 104. In response to receiving the data processing instruction sent by the user, the server 105 determines the target time period and the target product from the data processing instruction; according to the target time period, the target product, and the historical time period corresponding to the target product, the target holder attribute information and the target product holding status information of the holder of the target product in the target time period, the target economic status information of the target time period, the historical holder attribute information and the historical product holding status information of the holder of the target product in the historical time period, and the historical economic status of the historical time period are obtained from the database. status information and historical resource value-added information of the target product in the historical period, wherein the length of the historical period is equal to the length of the target period; using the historical holder attribute information, the historical holding status information, the historical economic status information and the historical resource value-added information to determine the resource quantity prediction model; inputting the target holder attribute information, the target holding status information and the target economic status information into the resource quantity prediction model to obtain the target resource value-added information; and generating early warning decision information based on the target resource value-added information, and sending the early warning decision information to the first terminal device 101, the second terminal device 102, and the third terminal device 103 through the network 104, so that the user can view the early warning decision information.

[0037] It should be understood that Figure 1The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0038] The following will be based on Figure 1 The scene described by Figure 2~Figure 3 The data processing method for the intelligent decision-making system of the disclosed embodiment is described in detail.

[0039] Figure 2 The flowchart of the data processing method for the intelligent decision-making system according to the embodiment of the present disclosure is schematically shown.

[0040] like Figure 2 As shown, the data processing for the intelligent decision-making system of this embodiment includes operations S210 to S250.

[0041] In operation S210 , in response to receiving a data processing instruction sent by a user, a target period and a target product are determined from the data processing instruction.

[0042] According to an embodiment of the present disclosure, the target period may include a future period. Specifically, the period may include annual, quarterly, monthly and other types. The intelligent decision-making system may display a selection box on the user terminal interface and determine the target period based on the period type selected by the user in the selection box.

[0043] According to an embodiment of the present disclosure, the target product may be a product that the user is interested in. Specifically, the product may be of multiple product types. The intelligent decision-making system may display a selection box on the user terminal interface and determine the target product based on the product type selected by the user in the selection box.

[0044] In operation S220, based on the target period, the target product and the historical period corresponding to the target product, the target holder attribute information and target product holding status information of the holder of the target product in the target period, the target economic status information of the target period, the historical holder attribute information and historical product holding status information of the holder of the target product in the historical period, the historical economic status information of the historical period and the historical resource value-added information of the target product in the historical period are obtained from the database.

[0045] According to an embodiment of the present disclosure, the duration of the historical period is equal to the duration of the target period. Specifically, when the target period type selected by the user is annual, the duration of the target period is one year, and the corresponding historical period duration should also be one year, that is, the type of the historical period is annual.

[0046] According to embodiments of the present disclosure, the holder may be, for example, a company that holds the target product. The holder's attribute information may include company information, such as the company's industry and region. Product holding status information may include information such as the quantity and duration of the target product held by the holder. Economic status information may include information such as the price index and production index.

[0047] According to an embodiment of the present disclosure, the holder of the target product in the target period may be the same as or different from the holder of the target product in the historical period.

[0048] According to an embodiment of the present disclosure, the historical resource value-added information can represent the resource value-added of the target product caused by the holders in the historical period. For example, when the target product is a personal loan, legal loan, bill, etc., the historical resource value-added information can be the credit loss of the target product caused by the holders in the historical period.

[0049] In operation S230 , a resource quantity prediction model is determined using the historical holder attribute information, the historical holding status information, the historical economic status information, and the historical resource quantity value-added information.

[0050] According to an embodiment of the present disclosure, a resource quantity prediction model can be obtained by training a machine learning model or a deep learning model using historical holder attribute information, historical holding status information, historical economic status information, and historical resource quantity value-added information.

[0051] According to an embodiment of the present disclosure, a resource quantity prediction model can be used to predict resource quantity value-added information based on holder attribute information, holding status information, and economic status information.

[0052] In operation S240, the target holder attribute information, the target holding status information, and the target economic status information are input into a resource quantity prediction model to obtain target resource quantity value-added information.

[0053] According to the embodiments of the present disclosure, since the resource quantity value-added model can predict resource quantity value-added information based on the holder attribute information, holding status information and economic status information, the target holder attribute information, target holding status information and target economic status information are input into the resource quantity prediction model, and the target resource quantity value-added information output by the resource quantity model can be obtained.

[0054] In operation S250, early warning decision information is generated according to the target resource quantity value-added information.

[0055] According to the embodiment of the present disclosure, a resource value-added threshold can be set. When the target resource value-added information exceeds the resource value-added threshold, an early warning is issued and corresponding early warning decision information is generated. Otherwise, no early warning is issued.

[0056] According to an embodiment of the present disclosure, when the target resource quantity value-added information exceeds the resource quantity value-added threshold, the target factor information that causes the target resource quantity value-added information to exceed the resource quantity value-added threshold can also be determined from the target holder attribute information, the target holding status information and the target economic status information, and early warning decision information can be generated based on the target factor information and the target resource quantity value-added information.

[0057] According to the embodiments of the present disclosure, by issuing early warnings based on historical information and target information that meet user needs according to the target time period and target product in the data processing instructions, not only can the resulting early warning decision information meet user needs, but it can also reduce the amount of data required to generate the early warning decision information, reduce the impact of redundant data on the early warning decision process, and thus improve the efficiency of generating early warning decision information. Furthermore, by automatically constructing a resource quantity prediction model based on historical information, using the resource quantity prediction model to predict target resource quantity value-added information based on target information, and then generating early warning decision information based on the target resource value-added information, the intelligence level of the early warning decision process is improved.

[0058] Figure 3 A data flow diagram for generating early warning decision information according to a specific embodiment of the present disclosure is schematically shown.

[0059] like Figure 3 As shown, according to the target product 310 and the target period 320, target information 340 and historical information 350 are obtained from the database 330, wherein the target information 340 may include target holder attribute information, target holding status information and target economic status information, and the historical information 350 may include historical holder attribute information, historical holding status information, historical economic status information and historical resource value-added information.

[0060] like Figure 3 As shown, historical information can be used to determine the resource quantity prediction model 360, and the target information 340 can be input into the resource quantity prediction model 360 to obtain the target resource quantity value-added information 370 output by the resource quantity prediction model 360, and then the early warning decision information 380 can be generated based on the target resource quantity value-added information 370.

[0061] According to an embodiment of the present disclosure, the data processing method for an intelligent decision-making system also includes: generating a first mapping relationship based on the target product and the target resource quantity value-added information; storing the first mapping relationship in a product value-added mapping table; and generating query permission information for the first mapping relationship in response to the user's query permission setting operation for the first mapping relationship.

[0062] According to an embodiment of the present disclosure, the first mapping relationship may represent a mapping relationship between a target product and target resource value-added information. The product value-added mapping table may be used to store a mapping relationship between a product and resource value-added information.

[0063] According to an embodiment of the present disclosure, the user may set a query authority for the first mapping relationship, and specifically, may set a department with the query authority.

[0064] According to an embodiment of the present disclosure, the query authority information includes identity information of users with authority to query the first mapping relationship. Specifically, multiple users in a department with query authority can be determined, and query authority information can be generated based on the identity information of each of the multiple users.

[0065] According to the embodiments of the present disclosure, by setting query permissions, the data security of the intelligent decision-making system can be improved.

[0066] According to an embodiment of the present disclosure, the data processing method for an intelligent decision-making system also includes: in response to receiving a query request, determining a product to be queried from the query request; determining a second mapping relationship that matches the product to be queried from a product value-added mapping table; verifying the user identity information corresponding to the query request based on the query authority information of the second mapping relationship to obtain an authority verification result; and displaying the second mapping relationship when the authority verification result indicates that the user identity information corresponding to the query request has the authority to query the second mapping relationship.

[0067] According to an embodiment of the present disclosure, a user may query a corresponding mapping relationship based on a product. In addition, a user may also query based on query conditions such as a time period and a department.

[0068] According to an embodiment of the present disclosure, the second mapping relationship can be a mapping relationship that matches the product to be queried. Specifically, the product to be queried can be determined from the product value-added mapping table, and the mapping relationship of the product to be queried can be determined from the product value-added mapping table as the second mapping relationship.

[0069] According to an embodiment of the present disclosure, there may be multiple second mapping relationships that match the product to be queried, or multiple second mapping relationships may be matched according to other query conditions in the query request to obtain a target second mapping relationship.

[0070] According to an embodiment of the present disclosure, when the second mapping relationship is set with query authority, the user's identity needs to be verified. Specifically, the user identity information can be verified according to the query authority information matching the second mapping relationship to obtain an authority verification result.

[0071] According to an embodiment of the present disclosure, the second mapping relationship may be displayed in a table format, and a table file download function may be provided to the user, so that the user may download or export the table file storing the second mapping relationship.

[0072] According to an embodiment of the present disclosure, if the authority verification result indicates that the user identity information corresponding to the query request does not have the authority to query the second mapping relationship, the second mapping relationship is not displayed, and the user is prompted that he does not have the query authority.

[0073] According to the embodiments of the present disclosure, by verifying the identity information of the user, data security is guaranteed, thereby improving the security of the intelligent decision-making system.

[0074] According to an embodiment of the present disclosure, target holder attribute information and target product holding status information of a holder of a target product within a target period, target economic status information of the target period, historical holder attribute information and historical product holding status information of a holder of a target product within a historical period, historical economic status information of the historical period, and historical resource value-added information of the target product within a historical period are obtained, including: determining from a database a target holder attribute information set and a target product holding status information set of a holder of a target product within a target period, and a target economic status information set of a holder of a target product within a target period; determining from a database historical holder attribute information of a holder of a target product within a historical period The target screening range is selected according to the target holder attribute information set, the target product holding status information set and the target economic status information set, and the target holder attribute information, the target product holding status information and the target economic status information set corresponding to the target screening range is selected; and the target screening range is selected according to the historical holder attribute information set, the historical product holding status information set and the historical economic status information set, and the historical holder attribute information, the historical product holding status information and the historical economic status information set are selected.

[0075] According to an embodiment of the present disclosure, the data processing instructions also include a target screening range. Specifically, the intelligent decision-making system can display the types of information included in the holder attribute information set, the product holding status information set, and the economic status information set. Users can select the required information type based on actual needs to obtain the target screening range.

[0076] For example, the holder attribute information set may include information on the industry to which the enterprise belongs, the region where the enterprise is located, and the type of enterprise. Users can select only the industry information to which the enterprise belongs as the target screening range according to actual needs.

[0077] According to an embodiment of the present disclosure, the target holder attribute information set, the target product holding status information set and the target economic status information set can be determined based on the target time period and the target product, and then the target holder attribute information, the target product holding status information and the target economic status information can be obtained by filtering according to the information type selected by the user in the target filtering range.

[0078] According to the embodiments of the present disclosure, the historical holder attribute information set, the historical product holding status information set, and the historical economic status information set can be determined based on the historical time period and the target product, and then filtered according to the information type selected by the user in the target filtering range to obtain the historical holder attribute information, the historical product holding status information, and the historical economic status information.

[0079] According to an embodiment of the present disclosure, by filtering information according to a target filtering range, the intelligence level of the intelligent decision-making system in making decisions is improved.

[0080] According to an embodiment of the present disclosure, target holder attribute information, target holding status information and target economic status information are input into a resource quantity prediction model to obtain target resource quantity value-added information, including: for each preset scenario, the target holder attribute sub-information, target holding status sub-information and target economic status sub-information corresponding to the preset scenario are input into the resource quantity prediction model to obtain target resource quantity value-added sub-information corresponding to the preset scenario; and target resource quantity value-added information is generated according to the target resource quantity value-added sub-information of each preset scenario.

[0081] According to an embodiment of the present disclosure, the target holder attribute information includes target holder attribute sub-information corresponding to each of the multiple preset scenarios, the target holding status information includes target holding status sub-information corresponding to each of the multiple preset scenarios, and the target economic status information includes target economic status sub-information corresponding to each of the multiple preset scenarios.

[0082] According to embodiments of the present disclosure, the multiple preset scenarios may include positive scenarios, negative scenarios, and normal scenarios. Since the target holder attribute information, target holding status information, and target economic status information are predicted, predictions can be performed based on different preset scenarios to obtain target holder attribute sub-information, target holding status sub-information, and target economic status sub-information for each preset scenario.

[0083] According to the embodiments of the present disclosure, a resource quantity prediction model can be used to calculate the target resource quantity value-added sub-information under each preset scenario. Specifically, the target holder attribute sub-information, target holding status sub-information and target economic status sub-information under a preset scenario can be input into the resource quantity prediction model to obtain the target resource quantity value-added sub-information of the preset scenario.

[0084] According to an embodiment of the present disclosure, when generating target resource quantity value-added information, the average value of multiple target resource quantity value-added sub-information can be determined as the target resource quantity value-added information, or the target resource quantity value-added sub-information can be weighted and summed according to the weights of each preset scenario to obtain the target resource quantity value-added information.

[0085] According to the embodiments of the present disclosure, by considering different preset scenarios for intelligent decision-making, the intelligence level of the intelligent decision-making system is improved, and the prediction accuracy is further improved.

[0086] According to an embodiment of the present disclosure, target resource quantity value-added information is generated based on the target resource quantity value-added sub-information of each preset scenario, including: performing a weighted operation on the target resource quantity value-added sub-information of each preset scenario according to the weight of each preset scenario to obtain the weighted target resource quantity value-added sub-information of each preset scenario; and performing a summation operation on the weighted target resource quantity value-added sub-information of multiple preset scenarios to obtain the target resource quantity value-added information.

[0087] According to an embodiment of the present disclosure, each preset scenario has its own weight. Specifically, a greater weight of a preset scenario indicates a greater possibility that the target time period is in the preset scenario.

[0088] According to an embodiment of the present disclosure, the weight may be a subjective weight, an objective weight, or a comprehensive weight obtained by combining the subjective weight and the objective weight.

[0089] According to the embodiments of the present disclosure, early warning is performed by considering the weights of respective preset scenarios, thereby improving the accuracy of early warning.

[0090] According to an embodiment of the present disclosure, the data processing method for an intelligent decision-making system further includes: for each preset scenario, generating a weight of the preset scenario according to the objective sub-weight and subjective sub-weight of the preset scenario.

[0091] According to an embodiment of the present disclosure, the subjective sub-weight can be set based on expert experience. Specifically, it can be based on the theory of ranking learning. Experts subjectively rank the resource value-added information of different preset scenarios in the front-end system, construct a subjective ranking matrix, and obtain the partial order relationship of different preset scenarios, and then obtain the subjective sub-weight based on the partial order relationship.

[0092] According to an embodiment of the present disclosure, the objective sub-weights can be obtained by using objective weighting methods such as entropy method and principal component analysis. Specifically, a comprehensive ranking loss function can be constructed for each preset scenario, and the convergence results can be normalized using the gradient descent method to obtain the optimal weight for the preset scenario.

[0093] According to an embodiment of the present disclosure, when generating the weight of a preset scenario, the subjective sub-weight and the objective sub-weight may be weighted and summed according to their respective credibility to obtain the weight of the preset scenario.

[0094] According to the embodiments of the present disclosure, by determining the weights of preset scenarios based on subjective sub-weights and objective sub-weights, the impact of subjective experience bias on intelligent decision-making can be reduced and the accuracy of intelligent decision-making can be improved.

[0095] According to an embodiment of the present disclosure, a resource quantity prediction model is determined by utilizing historical holder attribute information, historical holding status information, historical economic status information, and historical resource quantity value-added information, including: configuring hyperparameters of the initial resource quantity prediction model; using the historical holder attribute information, historical holding status information, and historical economic status information as independent variables of the initial resource quantity prediction model, using the historical resource quantity value-added information as the dependent variable of the initial resource quantity prediction model, adjusting the adjustable initial model parameters in the initial resource quantity prediction model to obtain adjusted target model parameters; and determining the resource quantity prediction model based on the hyperparameters and the target model parameters.

[0096] According to an embodiment of the present disclosure, the initial resource quantity prediction model may be a linear regression model, specifically, a logistic model. Hyperparameters may be parameters in the initial resource quantity prediction model that do not require tuning.

[0097] According to an embodiment of the present disclosure, when adjusting the initial model parameters, multiple sets of historical holder attribute information, historical holding status information, historical economic status information and corresponding historical resource value-added information can be used to continuously iterate the initial model parameters. When it is determined that the initial model parameters meet the preset conditions or the number of iterations reaches a preset threshold, the initial model parameters corresponding to the current iteration round are determined as the target model parameters.

[0098] According to an embodiment of the present disclosure, after the target model parameters are determined, a resource quantity prediction model can be determined based on the target model parameters and hyperparameters.

[0099] According to the embodiments of the present disclosure, the intelligence level of the intelligent early warning system is improved by automatically constructing a resource quantity prediction model by utilizing historical information.

[0100] The present disclosure also provides a data processing device for an intelligent decision-making system. Figure 4 The device is described in detail.

[0101] Figure 4 The structural block diagram of the data processing device for the intelligent decision-making system according to an embodiment of the present disclosure is schematically shown.

[0102] like Figure 4 As shown, the data processing device 400 for the intelligent decision-making system of this embodiment includes a first determination module 410 , an acquisition module 420 , a second determination module 430 , a third determination module 440 and a generation module 450 .

[0103] The first determining module 410 is configured to determine the target time period and target product from the data processing instruction in response to receiving the data processing instruction sent by the user. In one embodiment, the first determining module 410 may be configured to execute the operation S210 described above, which will not be described in detail herein.

[0104] Acquisition module 420 is configured to obtain, from a database, target holder attribute information and target product holding status information for holders of the target product during the target period, target economic status information for the target period, historical holder attribute information and historical product holding status information for holders of the target product during the historical period, historical economic status information for the historical period, and historical resource value-added information for the target product during the historical period, based on the target period, the target product, and the historical period corresponding to the target product. In one embodiment, acquisition module 420 may be configured to execute operation S220 described above, which will not be further described herein.

[0105] The second determination module 430 is used to determine a resource quantity prediction model using historical holder attribute information, historical holding status information, historical economic status information, and historical resource quantity value-added information. In one embodiment, the second determination module 430 can be used to perform the operation S230 described above, which will not be repeated here.

[0106] The third determination module 440 is used to input the target holder attribute information, target holding status information, and target economic status information into the resource quantity prediction model to obtain target resource quantity value-added information. In one embodiment, the third determination module 440 can be used to perform the operation S240 described above, which will not be repeated here.

[0107] The generation module 450 is used to generate early warning decision information based on the target resource value-added information. In one embodiment, the generation module 450 can be used to perform the operation S250 described above, which will not be repeated here.

[0108] According to an embodiment of the present disclosure, the data processing device 400 for an intelligent decision-making system further includes a mapping generation module, a mapping storage module, and a permission setting module.

[0109] The mapping generation module is used to generate a first mapping relationship according to the target product and the target resource value-added information.

[0110] The mapping storage module is used to store the first mapping relationship in the product value-added mapping table.

[0111] The permission setting module is used to generate query permission information for the first mapping relationship in response to a user's query permission setting operation for the first mapping relationship, wherein the query permission information includes identity information of a user having permission to query the first mapping relationship.

[0112] According to an embodiment of the present disclosure, the data processing device 400 for an intelligent decision-making system further includes a query determination module, a mapping matching module, a permission verification module, and a mapping display module.

[0113] The query determination module is used to determine the product to be queried from the query request in response to receiving the query request.

[0114] The mapping matching module is used to determine a second mapping relationship that matches the product to be queried from the product value-added mapping table.

[0115] The authority verification module is used to verify the user identity information corresponding to the query request according to the query authority information of the second mapping relationship to obtain an authority verification result.

[0116] The mapping display module is used to display the second mapping relationship when the authority verification result indicates that the user identity information corresponding to the query request has the authority to query the second mapping relationship.

[0117] According to an embodiment of the present disclosure, the data processing instruction also includes a target screening range. The acquisition module 420 also includes a first set determination submodule, a second set determination submodule, a first screening submodule, and a second screening submodule.

[0118] The first set determination submodule is used to determine from the database a target holder attribute information set and a target product holding status information set of a holder of a target product within a target period, as well as a target economic status information set for the target period.

[0119] The second set determination submodule is used to determine from the database the historical holder attribute information set and historical product holding status information set of the holder of the target product in the historical period, the historical economic status information set of the historical period, and historical resource value-added information.

[0120] The first screening submodule is used to screen the target holder attribute information set, the target product holding status information set and the target economic status information set according to the target screening range, and obtain the target holder attribute information, target product holding status information and target economic status information corresponding to the target screening range.

[0121] The second screening submodule is used to screen the historical holder attribute information set, the historical product holding status information set and the historical economic status information set according to the target screening range, and obtain the historical holder attribute information, historical product holding status information and historical economic status information corresponding to the target screening range.

[0122] According to an embodiment of the present disclosure, the target holder attribute information includes target holder attribute sub-information corresponding to each of the multiple preset scenarios, the target holding status information includes target holding status sub-information corresponding to each of the multiple preset scenarios, and the target economic status information includes target economic status sub-information corresponding to each of the multiple preset scenarios.

[0123] According to an embodiment of the present disclosure, the third determination module includes an input submodule and a generation submodule.

[0124] The input submodule is used to input the target holder attribute sub-information, target holding status sub-information and target economic status information corresponding to each preset scenario into the resource quantity prediction model to obtain the target resource quantity value-added sub-information corresponding to the preset scenario.

[0125] The generating submodule is used to generate target resource quantity value-added information according to the target resource quantity value-added sub-information of each preset scenario.

[0126] According to an embodiment of the present disclosure, each preset scenario has its own weight.

[0127] According to an embodiment of the present disclosure, the generating submodule includes a weighting unit and a summing unit.

[0128] The weighting unit is used to perform a weighting operation on the target resource quantity increment sub-information of each preset scenario according to the weight of each preset scenario, so as to obtain the weighted target resource quantity increment sub-information of each preset scenario.

[0129] The summing unit is used to perform a summing operation on the weighted target resource quantity increment sub-information of each of the plurality of preset scenarios to obtain the target resource quantity increment information.

[0130] According to an embodiment of the present disclosure, the generation submodule further includes a weight generation unit.

[0131] The weight generating unit is configured to generate, for each preset scenario, a weight of the preset scenario according to the objective sub-weight and the subjective sub-weight of the preset scenario.

[0132] According to an embodiment of the present disclosure, the second determination module 430 includes a parameter configuration submodule, a parameter determination submodule, and a model determination submodule.

[0133] The parameter configuration submodule is used to configure the hyperparameters of the initial resource quantity prediction model.

[0134] The parameter determination submodule is used to use historical holder attribute information, historical holding status information and historical economic status information as independent variables of the initial resource quantity prediction model, and use historical resource value-added information as the dependent variable of the initial resource quantity prediction model, and adjust the adjustable initial model parameters in the initial resource quantity prediction model to obtain the adjusted target model parameters.

[0135] The model determination submodule is used to determine the resource quantity prediction model based on the hyperparameters and target model parameters.

[0136] According to embodiments of the present disclosure, any multiple of the first determination module 410, acquisition module 420, second determination module 430, third determination module 440, and generation module 450 may be combined into a single module, or any one of these modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in a single module. According to embodiments of the present disclosure, at least one of the first determination module 410, acquisition module 420, second determination module 430, third determination module 440, and generation module 450 may 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 may be implemented in hardware or firmware through any other reasonable means of circuit integration or packaging, or may be implemented in any one of software, hardware, and firmware, or any appropriate combination of these. Alternatively, at least one of the first determination module 410 , the acquisition module 420 , the second determination module 430 , the third determination module 440 and the generation module 450 may be at least partially implemented as a computer program module, which may perform corresponding functions when executed.

[0137] Figure 5 A block diagram of an electronic device suitable for implementing a data processing method for an intelligent decision-making system according to an embodiment of the present disclosure is schematically shown.

[0138] like Figure 5As shown, the electronic device 500 according to an embodiment of the present disclosure includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage unit 508 into a random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to the embodiment of the present disclosure.

[0139] Various programs and data required for the operation of the electronic device 500 are stored in the RAM 503. The processor 501, ROM 502, and RAM 503 are connected to each other via a bus 504. The processor 501 executes the various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 502 and / or RAM 503. It should be noted that the programs may also be stored in one or more memories other than the ROM 502 and RAM 503. The processor 501 may also execute the various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in one or more memories.

[0140] According to an embodiment of the present disclosure, electronic device 500 may further include an input / output (I / O) interface 505, which is also connected to bus 504. Electronic device 500 may also include one or more of the following components connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 508 including a hard disk; and a communication section 509 including a network interface card such as a LAN card or modem. Communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. Removable media 511, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 510 as needed, so that computer programs read from the removable media can be installed into storage section 508 as needed.

[0141] 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 and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when executed, implements the method according to the embodiments of the present disclosure.

[0142] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, and may include, for example, but 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 that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 502 and / or RAM 503 described above, and / or one or more memories other than ROM 502 and RAM 503.

[0143] The embodiments of the present disclosure also include a computer program product, which includes a computer program containing program code for executing the method shown in the flowchart. When the computer program product is executed in a computer system, the program code is used to cause the computer system to implement the data processing method for an intelligent decision-making system provided by the embodiments of the present disclosure.

[0144] The computer program executes the above functions defined in the system / device of the embodiment of the present disclosure when the computer program is executed by the processor 501. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0145] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 509, and / or installed from a removable medium 511. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0146] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 509, and / or installed from a removable medium 511. When the computer program is executed by the processor 501, the above-described functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0147] 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 computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving 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 via the Internet).

[0148] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation 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 flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that 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 flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0149] Those skilled in the art will appreciate that the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present disclosure. In particular, the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways without departing from the spirit and teachings of the present disclosure. All such combinations and / or couplings fall within the scope of the present disclosure.

[0150] The above describes the embodiments of the present disclosure. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A data processing method for an intelligent decision-making system, characterized in that: The method comprises: In response to receiving a data processing instruction sent by a user, determining a target time period and a target product from the data processing instruction; According to the target period, the target product, and the historical period corresponding to the target product, acquiring from a database target holder attribute information and target product holding status information of a holder of the target product during the target period, target economic status information of the target period, historical holder attribute information and historical product holding status information of a holder of the target product during the historical period, historical economic status information of the historical period, and historical resource value-added information of the target product during the historical period, wherein the duration of the historical period is equal to the duration of the target period; Determining a resource quantity prediction model using the historical holder attribute information, the historical holding status information, the historical economic status information, and the historical resource quantity value-added information; Inputting the target holder attribute information, the target holding status information, and the target economic status information into the resource quantity prediction model to obtain target resource quantity value-added information; and According to the target resource value-added information, early warning decision information is generated.

2. The method according to claim 1, characterized in that The method further comprises: generating a first mapping relationship according to the target product and the target resource value-added information; Storing the first mapping relationship in a product value-added mapping table; and In response to the user's query permission setting operation for the first mapping relationship, query permission information for the first mapping relationship is generated, wherein the query permission information includes identity information of a user having permission to query the first mapping relationship.

3. The method according to claim 2, characterized in that The method comprises: In response to receiving a query request, determining a product to be queried from the query request; Determining a second mapping relationship that matches the product to be queried from the product value-added mapping table; Verifying the user identity information corresponding to the query request according to the query authority information of the second mapping relationship to obtain an authority verification result; and In a case where the authority verification result indicates that the user identity information corresponding to the query request has the authority to query the second mapping relationship, the second mapping relationship is displayed.

4. The method according to claim 1, wherein The data processing instruction also includes a target screening range, The acquiring of target holder attribute information and target product holding status information of the holder of the target product within the target period, target economic status information of the target period, historical holder attribute information and historical product holding status information of the holder of the target product within the historical period, historical economic status information of the historical period, and historical resource value-added information of the target product within the historical period includes: Determining from the database a target holder attribute information set and a target product holding status information set of a holder of the target product within the target period, and a target economic status information set for the target period; Determining from the database a historical holder attribute information set and a historical product holding status information set of the holder of the target product during the historical period, a historical economic status information set during the historical period, and the historical resource quantity value-added information; According to the target screening range, filtering is performed on the target holder attribute information set, the target product holding status information set, and the target economic status information set, respectively, to obtain the target holder attribute information, the target product holding status information, and the target economic status information corresponding to the target screening range; and According to the target screening range, screening is performed on the historical holder attribute information set, the historical product holding status information set and the historical economic status information set respectively to obtain the historical holder attribute information, the historical product holding status information and the historical economic status information corresponding to the target screening range.

5. The method according to claim 1, wherein The target holder attribute information includes target holder attribute sub-information corresponding to each of the plurality of preset scenarios, the target holding status information includes target holding status sub-information corresponding to each of the plurality of preset scenarios, and the target economic status information includes target economic status sub-information corresponding to each of the plurality of preset scenarios. The step of inputting the target holder attribute information, the target holding status information, and the target economic status information into the resource quantity prediction model to obtain target resource quantity value-added information includes: For each of the preset scenarios, inputting the target holder attribute sub-information, target holding status sub-information, and target economic status sub-information corresponding to the preset scenario into the resource quantity prediction model to obtain the target resource quantity value-added sub-information corresponding to the preset scenario; and The target resource quantity increment information is generated according to the target resource quantity increment sub-information of each preset scenario.

6. The method according to claim 5, characterized in that Each of the preset scenarios has its own weight, Generating the target resource amount value-added information according to the target resource amount value-added sub-information of each preset scenario includes: performing a weighting operation on the target resource quantity increment sub-information of each preset scenario according to the respective weight of each preset scenario to obtain the weighted target resource quantity increment sub-information of each preset scenario; and The weighted target resource quantity increment sub-information of each of the plurality of preset scenarios is summed up to obtain the target resource quantity increment information.

7. The method according to claim 6, characterized in that The method further comprises: For each of the preset scenarios, a weight of the preset scenario is generated according to the objective sub-weight and the subjective sub-weight of the preset scenario.

8. The method according to claim 1, characterized in that The determining of a resource quantity prediction model by utilizing the historical holder attribute information, the historical holding status information, the historical economic status information, and the historical resource quantity value-added information includes: Configure the hyperparameters of the initial resource prediction model; Using the historical holder attribute information, the historical holding status information, and the historical economic status information as independent variables of the initial resource quantity prediction model, using the historical resource quantity value-added information as the dependent variable of the initial resource quantity prediction model, and adjusting the adjustable initial model parameters in the initial resource quantity prediction model to obtain adjusted target model parameters; and The resource quantity prediction model is determined according to the hyperparameters and the target model parameters.

9. A data processing device for an intelligent decision-making system, characterized in that: The device comprises: a first determining module, configured to, in response to receiving a data processing instruction sent by a user, determine a target time period and a target product from the data processing instruction; an acquisition module, configured to acquire, from a database, based on the target period, the target product, and a historical period corresponding to the target product, target holder attribute information and target product holding status information of a holder of the target product during the target period, target economic status information of the target period, historical holder attribute information and historical product holding status information of the holder of the target product during the historical period, historical economic status information of the historical period, and historical resource value-added information of the target product during the historical period, wherein the duration of the historical period is equal to the duration of the target period; A second determining module is configured to determine a resource quantity prediction model using the historical holder attribute information, the historical holding status information, the historical economic status information, and the historical resource quantity value-added information; A third determining module is configured to input the target holder attribute information, the target holding status information, and the target economic status information into the resource quantity prediction model to obtain target resource quantity value-added information; and A generation module is used to generate early warning decision information based on the target resource value-added information.

10. An electronic device comprising: one or more processors; a memory 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 or instruction stored thereon, characterized in that: When the computer program or instruction 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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