Method and device for detecting resource interaction compliance risk and computer equipment

Abstract

The invention relates to the field of big data, and particularly discloses a resource interaction compliance risk detection method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: receiving a resource interaction request; analyzing the resource interaction request to obtain a first sub-request and the category of the resource interaction request; under the condition that the category of the resource interaction request is a penetrating resource interaction category, screening from the first sub-request to obtain a penetrating resource, and generating a second sub-request by utilizing an association relationship between the penetrating resource and other resources; the second sub-request is used for representing a request for interacting with other resources associated with the penetration resource; and detecting the first sub-request and the second sub-request by using different threads to obtain a detection result of the compliance risk corresponding to the resource interaction request. By adopting the method, the efficiency and accuracy of compliance risk detection can be improved.

Description

Technical Field

[0001] The present application relates to the field of big data technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for detecting compliance risks of resource interactions. Background Art

[0002] With the continuous development of asset management plans, financial management companies need to strictly comply with financial supervision and audit requirements during the investment process. In the process of directive investment, it is necessary to verify in real time whether the relevant data meets the risk compliance rules. However, investment in financial products involves multiple investment methods, with complex calculation logic, large amount of calculation and high complexity. Traditional technologies usually process multiple investment instructions concurrently.

[0003] However, during the concurrent calculation of multiple investment instructions, one instruction will be triggered to start the calculation of another instruction concurrently before it is completed, resulting in confusion in the calculation results, which in turn affects the risk compliance detection results of the investment instructions. Therefore, the use of concurrent processing of multiple investment instructions will lead to a decrease in the accuracy of the risk compliance detection results. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product that can detect resource interaction compliance risks in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a method for detecting compliance risks of resource interactions, including:

[0006] receiving resource interaction requests;

[0007] Parsing the resource interaction request to obtain a first sub-request and a category of the resource interaction request; wherein the first sub-request is used to represent the interaction request of each resource in the resource interaction request;

[0008] In the case where the category of the resource interaction request is a penetration resource interaction category, a penetration resource is obtained by screening from the first sub-request, and a second sub-request is generated by using the association relationship between the penetration resource and other resources; the second sub-request is used to represent a request for interaction with other resources associated with the penetration resource; wherein the penetration resource interaction category includes interaction of penetration resources; the penetration resource includes a resource that has an association relationship with other resources;

[0009] The first sub-request and the second sub-request are detected by using different threads respectively to obtain a detection result of the compliance risk corresponding to the resource interaction request.

[0010] In one embodiment, the detecting the first sub-request and the second sub-request to obtain a detection result of a compliance risk corresponding to the resource interaction request includes:

[0011] Interact with resources corresponding to the first sub-request and the second sub-request to obtain data to be detected corresponding to the resource interaction request;

[0012] The data to be detected is detected using a preset risk detection strategy to obtain a detection result of the compliance risk of the resource interaction request.

[0013] In one embodiment, the interacting with the resources corresponding to the first sub-request and the second sub-request to obtain the to-be-detected data corresponding to the resource interaction request includes:

[0014] Interacting the resources corresponding to the first sub-request and the second sub-request to obtain factors to be detected corresponding to the first sub-request and the second sub-request; wherein the factors to be detected include resource data after the resources corresponding to the first sub-request and the second sub-request are interacted;

[0015] The factors to be detected are matched by using a preset data initialization strategy to obtain the data to be detected corresponding to the resource interaction request; wherein the data initialization strategy includes an association relationship between the factors to be detected and the data to be detected.

[0016] In one embodiment, the interacting with the resources corresponding to the first sub-request and the second sub-request to obtain the factors to be detected corresponding to the first sub-request and the second sub-request includes:

[0017] Using the first sub-request and the second sub-request, respectively obtain identifications of factors to be detected corresponding to the first sub-request and the second sub-request;

[0018] The identification of the factors to be detected is grouped by using a preset grouping strategy to obtain the identification group of the factors to be detected; wherein the grouping strategy includes grouping the identification of the factors to be detected by the influence range of the factors to be detected; the influence range is used to characterize the number of other factors that are associated with the factors to be detected;

[0019] Using different processing nodes, the to-be-detected factor identification groups are processed respectively to obtain the to-be-detected factors corresponding to the to-be-detected factor identification groups;

[0020] The factors to be detected corresponding to the group of identifications of the factors to be detected are merged to obtain the factors to be detected corresponding to the first sub-request and the second sub-request.

[0021] In one embodiment, the interacting with the resources corresponding to the first sub-request and the second sub-request to obtain the factors to be detected corresponding to the first sub-request and the second sub-request includes:

[0022] Using a preset first classification strategy, respectively determine first categories of the first sub-request and the second sub-request, and using the first categories, obtain candidate to-be-detected factor identifiers corresponding to the first categories;

[0023] Using a preset second classification strategy, respectively determine the second categories of the first sub-request and the second sub-request;

[0024] Using the second category, matching the candidate to-be-detected factor identifiers to obtain a target to-be-detected factor identifier;

[0025] The target to-be-detected factor identifier is used to obtain the to-be-detected factors corresponding to the first sub-request and the second sub-request.

[0026] In one embodiment, after obtaining the detection result of the compliance risk corresponding to the resource interaction request, the method further includes:

[0027] Outputting the compliance risk detection results to a preset database;

[0028] Outputting the compliance risk detection result to a preset query memory;

[0029] The compliance risk detection result is output to a preset message queue in an asynchronous manner.

[0030] In one embodiment, the method further comprises:

[0031] Establishing a preprocessing statement; wherein the preprocessing statement includes at least one of a pre-created request parsing statement, a statement for obtaining factors to be detected, and a statement for obtaining data to be detected;

[0032] In response to parsing the resource interaction request, the request parsing statement is obtained, and the parameters in the request parsing statement are replaced and executed using the resource interaction request to obtain a sub-request corresponding to the resource interaction request;

[0033] In response to obtaining the factor to be detected, obtaining the statement for obtaining the factor to be detected, replacing and executing the parameters in the statement for obtaining the factor to be detected by using the identifier of the factor to be detected, and obtaining the factor to be detected;

[0034] In response to the acquisition of the data to be detected, the statement for acquiring the data to be detected is obtained, the factor to be detected corresponding to the data to be detected is obtained by using the data initialization strategy, and the parameter in the statement of the data to be detected is replaced and executed by using the factor to be detected to obtain the data to be detected.

[0035] In a second aspect, the present application also provides a detection device for resource interaction compliance risk, including:

[0036] A request receiving module, used for receiving resource interaction requests;

[0037] A request parsing module, used to parse the resource interaction request to obtain a first sub-request and a category of the resource interaction request; wherein the first sub-request is used to represent the interaction request of each resource in the resource interaction request;

[0038] A request derivation module, used for, when the category of the resource interaction request is a penetration resource interaction category, filtering out penetration resources from the first sub-request, and generating a second sub-request by using the association relationship between the penetration resource and other resources; the second sub-request is used to represent a request for interaction with other resources associated with the penetration resource; wherein the penetration resource interaction category includes interaction of penetration resources; and the penetration resource includes a resource that has an association relationship with other resources;

[0039] The request detection module is used to detect the first sub-request and the second sub-request using different threads respectively, and obtain the detection result of the compliance risk corresponding to the resource interaction request.

[0040] In one embodiment, the request detection module includes:

[0041] A data acquisition submodule, used to interact with resources corresponding to the first sub-request and the second sub-request to obtain data to be detected corresponding to the resource interaction request;

[0042] The data detection submodule is used to detect the data to be detected by using a preset risk detection strategy to obtain a detection result of the compliance risk of the resource interaction request.

[0043] In one embodiment, the data acquisition submodule includes:

[0044] a factor acquisition unit, configured to interact with resources corresponding to the first sub-request and the second sub-request to obtain factors to be detected corresponding to the first sub-request and the second sub-request; wherein the factors to be detected include resource data after the resources corresponding to the first sub-request and the second sub-request are interacted;

[0045] The data acquisition unit is used to match the factors to be detected by using a preset data initialization strategy to obtain the data to be detected corresponding to the resource interaction request; wherein the data initialization strategy includes the association relationship between the factors to be detected and the data to be detected.

[0046] In one embodiment, the factor acquisition unit includes:

[0047] an identification acquisition subunit, used to obtain identifications of factors to be detected corresponding to the first sub-request and the second sub-request respectively by using the first sub-request and the second sub-request;

[0048] The identification grouping subunit is used to group the identifications of the factors to be detected by using a preset grouping strategy to obtain the identification group of the factors to be detected; wherein the grouping strategy includes grouping the identifications of the factors to be detected by the influence range of the factors to be detected; the influence range is used to characterize the number of other factors that are associated with the factors to be detected;

[0049] A factor acquisition subunit is used to process the to-be-detected factor identification groups respectively by using different processing nodes to obtain the to-be-detected factors corresponding to the to-be-detected factor identification groups;

[0050] The factor acquisition subunit is further used to merge the factors to be detected corresponding to the group of factor identifications to be detected to obtain the factors to be detected corresponding to the first sub-request and the second sub-request.

[0051] In one embodiment, the factor acquisition unit includes:

[0052] an identification acquisition subunit, configured to determine first categories of the first sub-request and the second sub-request respectively by using a preset first classification strategy, and to acquire identifications of candidate factors to be detected corresponding to the first categories by using the first categories;

[0053] a request classification subunit, configured to determine second categories of the first sub-request and the second sub-request respectively by using a preset second classification strategy;

[0054] The identification acquisition subunit is used to obtain a target to-be-detected factor identification by matching from the candidate to-be-detected factor identifications using the second category;

[0055] The factor acquisition subunit is used to obtain the factors to be detected corresponding to the first sub-request and the second sub-request by using the target factor identifier to be detected.

[0056] In one embodiment, the request detection module further includes:

[0057] A result output module, used to output the compliance risk detection result to a preset database;

[0058] The result output module is further used to output the compliance risk detection result to a preset query memory;

[0059] The result output module is also used to output the compliance risk detection result to a preset message queue in an asynchronous manner.

[0060] In one embodiment, the device further comprises:

[0061] A statement building module, used for building a pre-processing statement; wherein the pre-processing statement includes at least one of a pre-created request parsing statement, a statement for obtaining factors to be detected, and a statement for obtaining data to be detected;

[0062] A statement using module, used for, in response to parsing a resource interaction request, obtaining the request parsing statement, replacing and executing parameters in the request parsing statement using the resource interaction request, and obtaining a sub-request corresponding to the resource interaction request;

[0063] The statement using module is further used to obtain the statement for obtaining the factor to be detected in response to obtaining the factor to be detected, and to replace and execute the parameters in the statement for obtaining the factor to be detected by using the factor to be detected identifier to obtain the factor to be detected;

[0064] The statement usage module is also used to obtain the data to be detected acquisition statement in response to the acquisition of the data to be detected, use the data initialization strategy to obtain the factors to be detected corresponding to the data to be detected, and use the factors to be detected to replace and execute the parameters in the data to be detected statement to obtain the data to be detected.

[0065] In a third aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the resource interaction compliance risk detection method as described in any one of the embodiments of the present disclosure is implemented.

[0066] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for detecting resource interaction compliance risk as described in any one of the embodiments of the present disclosure is implemented.

[0067] In a fifth aspect, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the method for detecting resource interaction compliance risk as described in any one of the embodiments of the present disclosure is implemented.

[0068] The above-mentioned detection method, device, computer equipment, computer-readable storage medium and computer program product for resource interaction compliance risk obtain the first sub-request and the category of the resource interaction request by parsing the resource interaction request, determine the penetration resource according to the category of the resource interaction request, generate the second sub-request, and use different threads to detect the first sub-request and the second sub-request to obtain the detection result of the resource interaction compliance risk. After receiving the resource interaction request, the above-mentioned method automatically parses the resource interaction request to generate the first sub-request and the second sub-request, thereby realizing the automatic processing of the resource interaction request, reducing manual intervention, and improving the accuracy and efficiency of the resource interaction compliance risk detection. The penetration resource interaction category is determined by the association relationship between the penetration resource and other resources, and the second sub-request is generated, which improves the relevance and accuracy of the resource interaction request compliance risk detection and improves the reliability of the resource interaction compliance detection. By using different threads to detect the first sub-request and the second sub-request, parallel processing is realized, and the processing speed and efficiency of the resource interaction compliance risk detection are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0070] Figure 1 This is an application environment diagram of a method for detecting resource interaction compliance risks in an embodiment;

[0071] Figure 2 It is a flowchart of a method for detecting compliance risks of resource interactions in one embodiment;

[0072] Figure 3 A schematic diagram showing how changes in an asset management plan indirectly affect products in an embodiment;

[0073] Figure 4 A schematic diagram of a sub-request generated by an investment instruction in one embodiment;

[0074] Figure 5 A schematic diagram of a risk detection process in an embodiment;

[0075] Figure 6 A schematic diagram of a process for obtaining data to be detected in one embodiment;

[0076] Figure 7 A schematic diagram of a first process for obtaining a factor to be detected in an embodiment;

[0077] Figure 8 A schematic diagram of a second process for obtaining a factor to be detected in one embodiment;

[0078] Fig. 9 A schematic diagram of a process for obtaining factor calculation results in one embodiment;

[0079] Fig.10 A schematic diagram of a flow chart of outputting a detection result in one embodiment;

[0080] Fig.11 A schematic diagram of outputting a detection result in one embodiment;

[0081] Fig.12 A schematic diagram of a first process for obtaining machine extended product information of a penetration asset management plan share ratio in one embodiment;

[0082] Fig.13 A second flow chart of obtaining machine extended product information of a penetration asset management plan share ratio in one embodiment;

[0083] Fig.14 A schematic diagram of a process of using a prepared statement in one embodiment;

[0084] Fig.15 It is a first structural block diagram of a detection device for resource interaction compliance risk in one embodiment;

[0085] Fig.16 It is a second structural block diagram of a detection device for resource interaction compliance risk in one embodiment;

[0086] Fig.17 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0087] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. It should be noted that in the embodiments of the present application, certain software, groups, models and other existing solutions in the industry may be mentioned, which should be considered as exemplary, and their purpose is only to illustrate the feasibility of the implementation of the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0088] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations. The acquisition, storage, use, and processing of data in the technical solution of this application are in compliance with relevant provisions of national laws and regulations.

[0089] The resource interaction compliance risk detection method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The terminal 102 can send a data interaction request to the server 104, and the server 104 performs compliance risk detection on the resource interaction request sent by the terminal 102. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server 104 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0090] In an exemplary embodiment, Figure 2 As shown in FIG, a detection method for resource interaction compliance risk is provided, and the method is applied to Figure 1 The following is an example of a server in the example, including:

[0091] Step S100: receiving a resource interaction request.

[0092] In an exemplary embodiment, the resource interaction request may include resource interaction between two interaction objects, or resource interaction between an interaction object and a source resource object, etc. Specifically, the resource interaction request may include investment instructions, etc. For example, direct investment instructions, channel instructions, asset management plan subscription and redemption instructions, etc. It can be understood that direct investment instructions may include products directly investing in a certain asset (stocks, bonds, funds, non-standard assets, etc.); channel instructions may include an asset management plan special account investing in a certain asset (stocks, bonds, funds, non-standard assets, etc.); asset management plan subscription and redemption instructions may include products investing in a certain asset management plan, etc.

[0093] In an exemplary embodiment, the resource interaction request may include an interaction batch, etc., which may specifically be a batch consisting of one or more resource interaction requests. Among them, the resource interaction request may include a batch of investment instructions, multiple investment instructions constitute an instruction batch, and there is an instruction batch number uniquely identified. The test results are queried by batch number. A single batch supports any combination of direct investment, channel, and asset management plan subscription and redemption instructions.

[0094] Step S200: parsing the resource interaction request to obtain a first sub-request and a category of the resource interaction request; wherein the first sub-request is used to represent the interaction request of each resource in the resource interaction request.

[0095] In an exemplary embodiment, the first sub-request may include parsing the resource interaction request to obtain requests for each resource in the resource interaction request, etc. Specifically, when the resource interaction request involves the interaction of multiple resources, the request is parsed, and each sub-request corresponds to the interaction of one resource. For example, in the case of a direct investment instruction, the product directly invests in a certain asset. The resource changes involved include that the product's share of the asset will increase, and the product's own cash will decrease. Therefore, the direct investment instruction can be parsed into a sub-request for an increase in the product's share of the asset and a sub-request for a decrease in the product's own cash, etc.; in the case of a channel instruction, the asset management plan special account invests in a certain asset. The resource changes involved include that the asset management plan's share of the asset will increase, and the asset of the asset management plan will decrease. Therefore, the channel instruction can be parsed into a sub-request for an increase in the asset management plan's share of the asset and a sub-request for a decrease in the asset management plan's cash, etc.; in the case of an asset management plan subscription and redemption instruction, the resource changes involved include that the product's share of the asset management plan will increase, the product's own cash will decrease, and the asset management plan's cash will increase. Therefore, the asset management plan subscription and redemption instruction can be parsed into a sub-request for an increase in the product's share of the asset management plan, a sub-request for a decrease in the product's own cash, and a sub-request for an increase in the asset management plan's cash, etc.

[0096] Step S300, when the category of the resource interaction request is a penetration resource interaction category, a penetration resource is obtained by screening from the first sub-request, and a second sub-request is generated by using the association relationship between the penetration resource and other resources; the second sub-request is used to represent a request for interaction with other resources associated with the penetration resource; wherein the penetration resource interaction category includes interaction of penetration resources; the penetration resource includes a resource that has an association relationship with other resources;

[0097] In an exemplary embodiment, the penetration resource may include changes in other resources associated with the penetration resource when a resource interaction request causes the penetration resource to change. Specifically, when parsing the asset management plan subscription and redemption instruction, a sub-request will be generated for an increase in the product's share of the asset management plan, a sub-request for a decrease in the product's own cash, and a sub-request for an increase in the asset management plan's cash. When the cash of the asset management plan changes, it will also drive the penetration processing of the underlying assets held by the asset management plan itself, so a second sub-request for the penetration processing of the underlying assets held by the asset management plan itself will be generated. In another exemplary embodiment, Figure 3 As shown, when the asset management plan designed by the directive changes, it will directly affect all products holding the asset management plan, and indirectly affect the proportion of all asset management plans held by all products holding the asset management plan.

[0098] Step S400: Use different threads to detect the first sub-request and the second sub-request respectively to obtain a detection result of the compliance risk corresponding to the resource interaction request.

[0099] In an exemplary embodiment, different threads may be established for the first sub-request and the second sub-request respectively; specifically, the number of sub-requests that exist may be the number of threads, etc. In an exemplary embodiment, the first sub-request and the second sub-request may be detected using a preset rule group, which may include that when all sub-rules under the rule group are in violation, the resource interaction request is considered to be in violation, or when one sub-rule under the rule group is in violation, the resource interaction request is considered to be in violation, etc. In actual use, results integration may be performed based on the detection results. A full set of rule details and all results of the rule group are obtained.

[0100] In an exemplary embodiment, the resource interaction request may include an investment instruction, etc. When the resource interaction request is an investment instruction, the sub-request may be generated as follows: Figure 4As shown, specifically, receiving an investment instruction, determining the type of investment instruction, when the investment instruction is a direct investment instruction, an original direct investment instruction will be generated: the target asset is the original instruction asset (i.e., the share of the asset held by the product changes), and a derivative direct investment instruction: the target asset is the own capital asset (i.e., the product's own capital changes); when the investment instruction is a channel instruction, an original channel instruction will be generated: the target asset is the asset in the original instruction (i.e., the share of the asset held by the asset management plan changes), and a derivative direct investment instruction: the free funds change of the asset management plan (i.e., the asset management plan's own cash changes); when the investment instruction is an asset management plan subscription and redemption instruction, an original asset management plan subscription and redemption instruction will be generated: the target asset is the original instruction asset management plan (i.e., the product's share of the asset management plan changes), a derivative channel execution: the asset management plan's own funds change, a derivative direct investment instruction: the product's own funds change, and a derivative direct investment instruction: the product subscribes and redeems the asset management plan in the original instruction (i.e., the asset management plan itself holds the underlying assets through penetration processing), etc. It is understandable that channel instructions and direct investment instructions are determined by the instruction system when placing an order. When it comes to the compliance detection part, the calculation logic will involve the distinction between the two types of channels and direct investments. If it is a direct investment instruction, it only involves the factors of the product's direct investment assets. If it is a channel instruction or an asset management plan subscription and redemption instruction (also a type of channel instruction), it involves factors such as the product indirectly holding underlying assets through holding asset management plans, which need to be penetrated through asset management plans. Therefore, the instruction type must be distinguished. Channel instructions will involve the value of asset management plan-related factors, etc.

[0101] In an exemplary embodiment, the number of threads can be determined based on the data of the derived instructions. For example, if 20 resource interaction requests are submitted in a batch and a total of 80 sub-requests are parsed and generated, then 80 threads are established and the concurrency is 80.

[0102] In the above-mentioned detection method of resource interaction compliance risk, the resource interaction request is parsed to obtain the first sub-request and the category of the resource interaction request, and the penetration resource is determined according to the category of the resource interaction request, and the second sub-request is generated. The first sub-request and the second sub-request are detected by different threads to obtain the detection result of the resource interaction compliance risk. After receiving the resource interaction request, the above-mentioned method automatically parses the resource interaction request to generate the first sub-request and the second sub-request, thereby realizing the automatic processing of the resource interaction request, reducing manual intervention, and improving the accuracy and efficiency of the resource interaction compliance risk detection. The penetration resource interaction category is determined by the association relationship between the penetration resource and other resources, and the second sub-request is generated, which improves the relevance and accuracy of the resource interaction request compliance risk detection and improves the reliability of the resource interaction compliance detection. By using different threads to detect the first sub-request and the second sub-request, parallel processing is realized, and the processing speed and efficiency of the resource interaction compliance risk detection are improved.

[0103] In an exemplary embodiment, Figure 5 As shown, the first sub-request and the second sub-request are detected to obtain the detection result of the compliance risk corresponding to the resource interaction request, including:

[0104] Step S401: interact with resources corresponding to the first sub-request and the second sub-request to obtain data to be detected corresponding to the resource interaction request.

[0105] In an exemplary embodiment, the interaction of the first sub-request and the second sub-request may include executing the first sub-request and the second sub-request. The data to be detected may include data that changes after executing the first sub-request and the second sub-request, and data that changes indirectly, for example, after affecting the share of assets held by the asset management plan, it will indirectly affect the proportion of other assets in the total assets held by the asset management plan.

[0106] In an exemplary embodiment, after concurrently interacting with the resources corresponding to the first sub-request and the second sub-request, the data to be detected corresponding to the first sub-request and the data to be detected corresponding to the second sub-request can be obtained, and the data to be detected of the first sub-request and the data to be detected of the second sub-request can be merged and deduplicated, thereby reducing subsequent repeated detections, etc.

[0107] Step S402: Detect the data to be detected by using a preset risk detection strategy to obtain a detection result of the compliance risk of the resource interaction request.

[0108] In an exemplary embodiment, the risk detection strategy may include setting a preset threshold range for the data to be detected, and when the data to be detected is not within the preset threshold range, the data to be detected is in violation. In another exemplary embodiment, the risk detection strategy may include considering that the resource interaction request is in violation when all sub-rules under the rule group are in violation, or considering that the resource interaction request is in violation when one sub-rule under the rule group is in violation, etc.

[0109] In this embodiment, by interacting with the resources corresponding to the first sub-request and the second sub-request, the data to be detected corresponding to the resource interaction request is obtained, and the data to be detected is detected to obtain the compliance risk detection result of the resource interaction request. By generating the data to be detected and detecting the data to be detected, subjective interference is reduced, making the detection process more objective and standardized, and improving the accuracy of the detection results. At the same time, the detection process can be traced using the data to be detected, which is convenient for subsequent data analysis and problem tracing. At the same time, the risk detection strategy is used to detect the data to be detected, so that the data to be detected can be expanded and transplanted by changing the risk detection strategy to adapt to different detection requirements, so that the data to be detected has high scalability and transplantability.

[0110] In an exemplary embodiment, Figure 6 As shown, the interacting with the resources corresponding to the first sub-request and the second sub-request to obtain the to-be-detected data corresponding to the resource interaction request includes:

[0111] Step S411, interacting with resources corresponding to the first sub-request and the second sub-request to obtain factors to be detected corresponding to the first sub-request and the second sub-request; wherein the factors to be detected include resource data after the resources corresponding to the first sub-request and the second sub-request are interacted.

[0112] In an exemplary embodiment, the factors to be detected may include data that changes directly after the sub-request is executed.

[0113] Step S412, using a preset data initialization strategy, matching the factors to be detected to obtain the data to be detected corresponding to the resource interaction request; wherein the data initialization strategy includes an association relationship between the factors to be detected and the data to be detected.

[0114] In an exemplary embodiment, the data initialization strategy may include a strategy for the factors to be detected to form the data to be detected. For example, the data initialization strategy may obtain the numerator factor and the denominator factor from the factors to be detected, and then combine them into the data to be detected, etc. It is understandable that the data initialization strategy may include a variety of strategies for initializing the data to be detected, thereby generating multiple data to be detected, etc. It is understandable that the decoupling of the factors to be detected and the data to be detected is achieved through the data initialization strategy, and the dynamic configurability of the factors to be detected and the data to be detected is achieved. Further, the decoupling between the factors and the risk detection strategy is achieved, and the dynamic configurability of the risk detection strategy and the factors is achieved, and the dynamic extensibility of the compliance risk detection rules is achieved through the risk detection strategy.

[0115] In an exemplary embodiment, after the factors to be detected of the first sub-request and the second sub-request are obtained, the factors to be detected are aggregated and merged, the same factors are merged, and the data to be detected are calculated using all the factors to be detected.

[0116] In this embodiment, the factors to be detected are obtained by interacting with the first sub-request and the second sub-request, and the data to be detected is generated by using the data initialization strategy. By using the preset data initialization strategy to match the factors to be detected, the corresponding data to be detected can be accurately generated, thereby improving the accuracy and matching degree of the detection. At the same time, by using the data initialization strategy, the factors to be detected are decoupled from the data to be detected, so that different data to be detected can be obtained only by adjusting the data initialization strategy, thereby achieving high scalability and portability of the factors to be detected and the data to be detected.

[0117] In an exemplary embodiment, Figure 7 As shown, the interacting with the resources corresponding to the first sub-request and the second sub-request to obtain the factors to be detected corresponding to the first sub-request and the second sub-request includes:

[0118] Step S421: using the first sub-request and the second sub-request, respectively obtain identifiers of factors to be detected corresponding to the first sub-request and the second sub-request.

[0119] In an exemplary embodiment, the factor to be detected identifier may include an identifier of the factor to be detected. For example, if the first sub-request causes a change in the X resource, the factor to be detected is identified as the X resource, and the factor to be detected, that is, the quantity of the X resource and / or the change amount, etc., is obtained by using the factor to be detected identifier.

[0120] Step S422, using a preset grouping strategy, grouping the identifications of the factors to be detected to obtain the group of identifications of the factors to be detected; wherein the grouping strategy includes grouping the identifications of the factors to be detected by the influence range of the factors to be detected; the influence range is used to characterize the number of other factors that are associated with the factors to be detected.

[0121] In an exemplary embodiment, the grouping strategy may include the influence scope of the factor to be detected. Specifically, the influence scope may include whether the result is a penetration type, the product scope of the factor, and the factors corresponding to the rules processed by different instances should not overlap as much as possible. For example, when the resource interaction request is an investment instruction, the grouping strategy may include the factors of the data to be detected of the penetration type, that is, there is data to be detected of the penetration type, such as the change of the asset management interaction designed by the instruction, then the products holding the asset management plan must participate in the calculation, then the change of the asset management plan is determined here as the change of the data to be detected of the penetration type, etc. The grouping strategy may also include the product scope of the factor. Specifically, the factor calculation complexity of the product group scope is the highest, and it is necessary to obtain the data of all products under the product group during the calculation process, and then summarize it as the result of the product group, followed by the single product scope, and the least complex is the full product scope, etc. The grouping strategy may also include that the factors corresponding to the data to be detected processed by different groups should not overlap as much as possible. For example, if the data to be detected X and the data to be detected Y both need to use the factor to be detected Z, then the data to be detected X and the data to be detected Y should be distributed in the same strength for processing as much as possible. In another exemplary embodiment, the updates of the factor values ​​of the data to be tested processed by different groups need to be aggregated and persisted uniformly to ensure that all factor updates of the previous instruction are written back completely before the compliance risk detection of the second instruction can be started. It can be understood that this is due to the business requirements of instruction compliance risk detection. The factor value used for each instruction must be based on the results of all previous factor processing, etc.

[0122] In an exemplary embodiment, a specific penetration flag can be set for the factor identifier, which indicates that the change of the factor needs to consider the indirect impact caused by the change of the factor. For example, due to the existence of channel and asset management plan subscription and redemption instructions, the channel instruction is that the asset management plan directly invests in a certain type of asset, which will lead to indirect impact on the change of the investment asset (individual bond) of the product, product group and all product range factors holding the asset management plan; the asset management plan subscription and redemption instruction is that a certain product subscribes or redeems a certain asset management plan, which will bring about a change in the proportion of the product and other products holding the asset management plan, and the change in the proportion will indirectly lead to a change in its specific factor value. In an exemplary embodiment, some factors will be used multiple times, so during the calculation process, these data concurrent synchronization containers will cache the factor values ​​used, and by avoiding multiple queries from the secondary cache, the efficiency is further improved.

[0123] In an exemplary embodiment, the factors to be detected can be obtained in a pre-loaded manner to obtain some factors. Specifically, it can include that when performing compliance detection on each instruction, it will first consider using multiple ready-made concurrent methods to calculate some of the compliance detection of the instructions, such as the proportion of the product holding the asset management plan, and some holding ratios involved in the specific product grouping. These calculated data can be used directly in the subsequent factor calculation process. These calculations are concentrated and multi-threaded concurrent methods are used to improve computing efficiency. Simplify the complexity of subsequent calculations.

[0124] Step S423: using different processing nodes to process the to-be-detected factor identification groups respectively, to obtain the to-be-detected factors corresponding to the to-be-detected factor identification groups.

[0125] In an exemplary embodiment, the different processing nodes may include multiple service instances. Specifically, the multiple service instances may concurrently process the identification groups of factors to be detected, etc. Each service instance processes one identification group of factors to be detected, etc.

[0126] Step 424: merge the factors to be detected corresponding to the group of factors to be detected identification to obtain the factors to be detected corresponding to the first sub-request and the second sub-request.

[0127] In an exemplary embodiment, merging the factors to be detected corresponding to the group of factor identifications to be detected may include ensuring that all factor updates of the previous instruction are written back before starting the compliance risk detection of the second instruction, and aggregating the updates of factor values ​​of different examples and making them uniformly persistent.

[0128] In this embodiment, the association between the factor to be detected and other factors is used to group the factor identifiers to be detected, and different processing nodes are used to obtain the factors to be detected of different groups, and the factors to be detected are merged. It is possible to achieve the parallelism of compliance risk detection by grouping the factor identifiers to be detected and using different processing nodes to obtain the factors to be detected of different groups. At the same time, by merging the factors of different processing nodes, the final factors to be detected are more comprehensive. At the same time, merging the factors to be detected can ensure the consistency of the data and improve the reliability of the factors to be detected.

[0129] In an exemplary embodiment, Figure 8 As shown, the interacting with the resources corresponding to the first sub-request and the second sub-request to obtain the factors to be detected corresponding to the first sub-request and the second sub-request includes:

[0130] Step S431: using a preset first classification strategy, respectively determine the first categories of the first sub-request and the second sub-request, and using the first categories, obtain the candidate to-be-detected factor identifiers corresponding to the first categories.

[0131] In an exemplary embodiment, the first classification strategy can be used to determine the categories of the first sub-request and the second sub-request, and then screen and obtain the corresponding analog candidate factor identifications to be detected. Specifically, when the resource interaction request is an investment instruction, the first classification strategy may include the classification corresponding to the assets in the instruction, for example, the balance of own funds assets, precious metals, securities firm asset management plans, etc. And the category of the sub-request is used to obtain the candidate factor identification corresponding to the sub-request.

[0132] Step S432: using a preset second classification strategy, respectively determine the second categories of the first sub-request and the second sub-request.

[0133] In an exemplary embodiment, the second classification strategy may be based on a subcategory of the first category, or may be a category of another dimension. Specifically, when the resource interaction request is an investment instruction, the second classification strategy may include equity market value, net asset value, holding cost, investment concentration, etc.

[0134] Step S433: using the second category, matching the target to-be-detected factor identifier from the candidate to-be-detected factor identifiers.

[0135] In an exemplary embodiment, the second category can be used to screen the candidate factors to be detected corresponding to the first category to obtain the target factor identification corresponding to the second category. It can be understood that, for example, a factor with a factor attribute of an equity market will only be calculated when the asset type involved is an equity product.

[0136] Step S434: using the target to-be-detected factor identifier, obtain the to-be-detected factors corresponding to the first sub-request and the second sub-request.

[0137] In an exemplary embodiment, when the resource interaction request is an investment instruction, the configuration method can be used to implement the factor matching logic and the change calculation logic to respond to regulatory or business demand changes in a timely manner. For example, when regulatory requirements occur, the New Year's greetings can be quickly responded to changes in rules. Factor calculation mainly includes the calculation of factor matching conditions, the calculation of factor change values, and the value logic of asset codes (individual bond codes). First, all attribute objects involved in factor matching and change are constructed for each instruction to be processed. These attributes include: factor product range, factor asset range (individual bond range), factor attribute classification, asset number, instruction amount, instruction amount, third-level asset type, remaining duration, remaining term, credit rating, issuing entity, individual bond name and other attributes. Specifically, the factor calculation rule configuration can include the following:

[0138] Table 1

[0139]

[0140] Among them, the purpose of factor expression configuration is to realize whether the current instruction should calculate the factor in the instruction compliance detection process through configuration, and if so, how to deal with the asset ID involved in it, and how to calculate the change of the factor. All are configured through expressions. The enable flag refers to whether the expression is effective in each specific configuration. If it is not effective, it is assumed to be matched. Whether the asset ID expression is enabled refers to whether the asset ID is obtained through the expression. When performing compliance risk detection, the instruction set output by the instruction derivation module is processed concurrently, and each instruction is processed by one thread for factor calculation. First, the factor attribute classification is obtained through the three-level asset classification in the instruction, and then the candidate factor set is obtained according to the factor attribute classification. Then, all candidate factors are detected and calculated through the configured factor rules. If they match, the asset ID and factor change are obtained through configuration. Since each original instruction will derive multiple instructions, and instruction batch detection is supported, different instructions may involve the same factor when calculating factors concurrently. After the result is obtained based on the concurrent calculation of each instruction, the output factor calculation result will be aggregated and merged once, and the same factors will be aggregated and merged. In an exemplary embodiment, the information such as the factor ID, the change amount, and the asset ID can be obtained as follows: Fig. 9 As shown, specifically including:

[0141] Step S801, using the instruction derivation module to process the original instruction to obtain derived instruction information;

[0142] Step S802, obtaining a candidate factor set according to the three-level classification of assets in the instruction information;

[0143] Step S803, verify whether the factor matches this compliance test by configuring the factor matching rule, and filter out if it does not match;

[0144] Step S804, calculating the factor change amount by using the configured factor value calculation rule;

[0145] Step S805, calculating the asset ID matched by the factor through the asset determination rule corresponding to the configured factor;

[0146] Step S806, obtaining the factor calculation results including factor ID, change amount, asset ID and other information.

[0147] In this embodiment, the first category of the sub-request is obtained through the first classification strategy, and then the candidate factor to be detected identifier corresponding to the first category is obtained. The second category of the sub-request is determined through the second classification strategy, and the target factor to be detected is obtained from the candidate factor to be detected identifier. It is possible to determine the first category and the second category of the sub-request through the first grouping strategy and the second classification strategy, and automatically and quickly determine the factor to be detected for each request without manual intervention, thereby improving the speed of obtaining the factor to be detected. At the same time, through the classification strategy and the matching algorithm, the target factor to be detected can be accurately matched, thereby improving the accuracy and reliability of obtaining the factor to be detected. The classification strategy and the matching algorithm can be adjusted to make the classification and matching adapt to different scenarios, thereby improving the scalability and portability of compliance risk detection.

[0148] In an exemplary embodiment, Fig.10 As shown, after obtaining the detection result of the compliance risk corresponding to the resource interaction request, it also includes:

[0149] Step S501: output the compliance risk detection result to a preset database.

[0150] In an exemplary embodiment, outputting the detection results to a preset database may include synchronous persistent output. Specifically, synchronous output means directly outputting to the target storage during the compliance risk detection process. If the final output result detail data may be large in volume, if it is a full amount of detail results (violation, warning, compliance), the volume will reach 100,000. If synchronously output to storage, a concurrent output mode is also adopted to establish multiple target output storage clients (connections) to concurrently write the detailed results to the target storage. The concurrency can be configured through parameters. It can support most relational databases and non-relational storage such as Oracle, Mysql, HBase, Elasticsearch, and files.

[0151] In an exemplary embodiment, the detection data is stored in a preset database and synchronously persisted output can be directly output to the database such as (Mysql, HBase, etc.) after the compliance detection results are calculated. The query service then queries the database to obtain the results.

[0152] Step S502: output the compliance risk detection result to a preset query memory.

[0153] In an exemplary embodiment, the output of the detection results to a preset storage memory may include synchronous non-persistent output. Specifically, synchronous non-persistent output refers to passing the results directly to the compliance risk detection result query service through an RPC call through a service call. After receiving the result data, the compliance risk detection result query service caches the data in the memory and directly provides query services to the outside. The data is not persisted during the entire process, and the cached data is eliminated by controlling the number of instructions and LRU strategies. At the same time, these result details and the like will be asynchronously persisted in the result query service.

[0154] In an exemplary embodiment, the detection data is output to a preset query memory using synchronous non-persistent output so that after the compliance detection is completed, the result is not directly written into the database, but sent to another result query service through an RPC call.

[0155] In an exemplary embodiment, the difference between outputting the test results to a preset database and querying the memory may include that outputting to a database may write the test results to the database, and then querying from the database after writing; while storing in the query memory means directly sending the test results to the memory of the query service, without the overhead of writing to the database, and being more efficient. Therefore, when the performance requirements are high and the amount of data is not large, the data to be tested is stored in the query memory. In actual use, the data can be sent to the database and the query memory at the same time, and used in combination.

[0156] Step S503: output the compliance risk detection result to a preset message queue in an asynchronous manner.

[0157] In an exemplary embodiment, the detection results can be output in an asynchronous output manner. Specifically, the rule detail results can be asynchronously output to a message middleware, such as Kafka. Compared with other stored outputs, output to a message middleware can quickly compress the output data and send it to Kafka. Subsequent result processing can be processed by subscribing to messages in Kafka, which is decoupled from the current compliance risk detection process.

[0158] In an exemplary embodiment, the output of the detection result can be as follows Fig.11As shown, the rule result details, that is, the detection results, can be output to the database through synchronous persistence, such as Oracle / Mysql / Hbase / ES, etc. through data access control; it can also be output to the rule result query service through synchronous non-persistence; it can also be output asynchronously to the message middleware, such as Kafka, etc., and provide other result processing services.

[0159] In an exemplary embodiment, when the resource interaction request is an investment instruction, the process of concurrently obtaining the share ratio of the penetration asset management plan and its extended product information can be as follows: Fig.12 As shown, specifically including:

[0160] Step S811, obtaining the asset management plan involved in this batch of instructions;

[0161] Step S812, based on the asset management plan, concurrently query the products holding the asset management plan;

[0162] Step S813, collecting product data based on the concurrent results;

[0163] Step S814, based on the obtained product, concurrently obtain the proportion of the asset management plan machine share held by the product, etc.

[0164] In an exemplary embodiment, when the resource interaction request is an investment instruction, in the case where the asset management plan involves a product group, the process of concurrently obtaining the share ratio of the penetrating asset management plan and its extended product information can be as follows: Fig.13 As shown, specifically including:

[0165] Step S821, obtaining the asset management plans and product groups involved in this batch of instructions;

[0166] Step S822, concurrently querying the relationship between the products and product groups holding the asset management plan based on the asset management plan;

[0167] Step S823, collecting product and product group relationship data based on the concurrent results;

[0168] Step S824, based on the obtained product and product group relationship data, concurrently obtain the share ratio of the asset management plan involved in the product group.

[0169] In this embodiment, by storing the test results in the database respectively, the test results can be persisted and saved, ensuring the security and reliability of the data. By storing the test results in the query memory, it is not necessary to query through the database when querying, and it can be directly sent to the memory of the query service to obtain it, making the query more efficient. By asynchronously outputting the test results to the message queue, the output efficiency of the test results is improved. At the same time, the low-performance IO and other time-consuming operations are isolated from the compliance risk detection, further improving resource utilization and efficiency.

[0170] In an exemplary embodiment, Fig.14 As shown, the method also includes:

[0171] Step S511, establishing a preprocessing statement; wherein the preprocessing statement includes at least one of a pre-created request parsing statement, a statement for obtaining factors to be detected, and a statement for obtaining data to be detected.

[0172] In an exemplary embodiment, the prepared statement may include a pre-established statement for executing a task. In actual use, only the parameters in the prepared statement need to be replaced to execute the task, thereby avoiding the time-consuming task creation.

[0173] In an exemplary embodiment, the preprocessing statement may include establishing an object. By pre-establishing an object, object reuse is achieved, and the time consumption of object creation is reduced, so as to increase the efficiency and performance of resource interaction compliance risk detection. In actual use, due to the complexity of data interaction, a large number of objects may need to be created. Through object reuse, tasks can be quickly executed by simply overwriting the value of the object.

[0174] Step S512, in response to parsing the resource interaction request, obtaining the request parsing statement, using the resource interaction request, replacing and executing the parameters in the request parsing statement, and obtaining a sub-request corresponding to the resource interaction request.

[0175] In an exemplary embodiment, the parsing of the resource interaction request may include different preprocessed statements for different requests; for example, if the resource interaction request is an investment instruction, then the parsing of the resource interaction request may refer to channel instruction parsing, direct investment instruction parsing, and asset management plan subscription and redemption instruction parsing, etc.; the corresponding request parsing can be completed by simply overwriting or replacing the corresponding parameters.

[0176] Step S513, in response to obtaining the factor to be detected, obtaining the statement for obtaining the factor to be detected, using the identifier of the factor to be detected, replacing and executing the parameters in the statement for obtaining the factor to be detected, and obtaining the factor to be detected.

[0177] In an exemplary embodiment, the parameters of the statement for obtaining the factor to be detected may include the identifier and change amount of the factor to be detected, etc. The task of obtaining the factor to be detected can be executed by simply overwriting the identifier and change amount of the factor to be detected with the parameters of the statement for obtaining the factor to be detected.

[0178] Step S514, in response to the acquisition of the data to be detected, the statement for acquiring the data to be detected is acquired, the factors to be detected corresponding to the data to be detected are obtained by using the data initialization strategy, and the parameters in the statement of the data to be detected are replaced and executed by using the factors to be detected to obtain the data to be detected.

[0179] In an exemplary embodiment, the parameters of the statement for obtaining the data to be detected may include corresponding factors to be detected. Since the data to be detected is obtained by combining the factors to be detected through a data initialization strategy, the acquisition of the data to be detected needs to determine the corresponding factors to be detected through a data initialization strategy, and the factors to be detected are used to cover or replace the parameters of the statement for obtaining the data to be detected, so as to execute the task of obtaining the data to be detected, etc.

[0180] In this embodiment, by using the pre-built prepared statements, when parsing resource interaction requests, acquiring factors to be detected, and acquiring data to be detected, the prepared statements can be called and the parameters in the prepared statements can be replaced. This reduces the time consumption of creating objects and statements, and improves the efficiency of parsing resource interaction requests, acquiring factors to be detected, and acquiring data to be detected.

[0181] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0182] Based on the same inventive concept, the embodiment of the present application also provides a detection device for resource interaction compliance risk for implementing the detection method for resource interaction compliance risk involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more detection devices for resource interaction compliance risk provided below can refer to the limitations of the detection method for resource interaction compliance risk above, and will not be repeated here.

[0183] In an exemplary embodiment, Fig.15As shown, a detection device 100 for resource interaction compliance risk is provided, comprising: a request receiving module 101, a request parsing module 102, a request derivation module 103 and a request detection module 104, wherein:

[0184] A request receiving module, used for receiving resource interaction requests;

[0185] A request parsing module, used to parse the resource interaction request to obtain a first sub-request and a category of the resource interaction request; wherein the first sub-request is used to represent the interaction request of each resource in the resource interaction request;

[0186] A request derivation module, used for, when the category of the resource interaction request is a penetration resource interaction category, filtering out penetration resources from the first sub-request, and generating a second sub-request by using the association relationship between the penetration resource and other resources; the second sub-request is used to represent a request for interaction with other resources associated with the penetration resource; wherein the penetration resource interaction category includes interaction of penetration resources; and the penetration resource includes a resource that has an association relationship with other resources;

[0187] The request detection module is used to detect the first sub-request and the second sub-request using different threads respectively, and obtain the detection result of the compliance risk corresponding to the resource interaction request.

[0188] In one embodiment, the request detection module includes:

[0189] A data acquisition submodule, used to interact with resources corresponding to the first sub-request and the second sub-request to obtain data to be detected corresponding to the resource interaction request;

[0190] The data detection submodule is used to detect the data to be detected by using a preset risk detection strategy to obtain a detection result of the compliance risk of the resource interaction request.

[0191] In one embodiment, the data acquisition submodule includes:

[0192] a factor acquisition unit, configured to interact with resources corresponding to the first sub-request and the second sub-request to obtain factors to be detected corresponding to the first sub-request and the second sub-request; wherein the factors to be detected include resource data after the resources corresponding to the first sub-request and the second sub-request are interacted;

[0193] The data acquisition unit is used to match the factors to be detected by using a preset data initialization strategy to obtain the data to be detected corresponding to the resource interaction request; wherein the data initialization strategy includes the association relationship between the factors to be detected and the data to be detected.

[0194] In one embodiment, the factor acquisition unit includes:

[0195] an identification acquisition subunit, used to obtain identifications of factors to be detected corresponding to the first sub-request and the second sub-request respectively by using the first sub-request and the second sub-request;

[0196] The identification grouping subunit is used to group the identifications of the factors to be detected by using a preset grouping strategy to obtain the identification group of the factors to be detected; wherein the grouping strategy includes grouping the identifications of the factors to be detected by the influence range of the factors to be detected; the influence range is used to characterize the number of other factors that are associated with the factors to be detected;

[0197] A factor acquisition subunit is used to process the to-be-detected factor identification groups respectively by using different processing nodes to obtain the to-be-detected factors corresponding to the to-be-detected factor identification groups;

[0198] The factor acquisition subunit is further used to merge the factors to be detected corresponding to the group of factor identifications to be detected to obtain the factors to be detected corresponding to the first sub-request and the second sub-request.

[0199] In one embodiment, the factor acquisition unit includes:

[0200] an identification acquisition subunit, configured to determine first categories of the first sub-request and the second sub-request respectively by using a preset first classification strategy, and to acquire identifications of candidate factors to be detected corresponding to the first categories by using the first categories;

[0201] a request classification subunit, configured to determine second categories of the first sub-request and the second sub-request respectively by using a preset second classification strategy;

[0202] The identification acquisition subunit is used to obtain a target to-be-detected factor identification by matching from the candidate to-be-detected factor identifications using the second category;

[0203] The factor acquisition subunit is used to obtain the factors to be detected corresponding to the first sub-request and the second sub-request by using the target factor identifier to be detected.

[0204] In one embodiment, the request detection module further includes:

[0205] A result output module, used to output the compliance risk detection result to a preset database;

[0206] The result output module is further used to output the compliance risk detection result to a preset query memory;

[0207] The result output module is also used to output the compliance risk detection result to a preset message queue in an asynchronous manner.

[0208] In one embodiment, the device further comprises:

[0209] A statement building module, used for building a pre-processing statement; wherein the pre-processing statement includes at least one of a pre-created request parsing statement, a statement for obtaining factors to be detected, and a statement for obtaining data to be detected;

[0210] A statement using module, used for, in response to parsing a resource interaction request, obtaining the request parsing statement, replacing and executing parameters in the request parsing statement using the resource interaction request, and obtaining a sub-request corresponding to the resource interaction request;

[0211] The statement using module is further used to obtain the statement for obtaining the factor to be detected in response to obtaining the factor to be detected, and to replace and execute the parameters in the statement for obtaining the factor to be detected by using the factor to be detected identifier to obtain the factor to be detected;

[0212] The statement usage module is also used to obtain the data to be detected acquisition statement in response to the acquisition of the data to be detected, use the data initialization strategy to obtain the factors to be detected corresponding to the data to be detected, and use the factors to be detected to replace and execute the parameters in the data to be detected statement to obtain the data to be detected.

[0213] In an exemplary embodiment, the resource interaction compliance risk detection device may be as follows: Fig.16 As shown, multiple compliance detection instances may be included, including:

[0214] Upstream service or message middleware, used to receive instruction data;

[0215] The instruction receiving module is used to receive instruction data provided by the upstream service or message middleware;

[0216] Instruction derived data, used to parse the instruction data and generate a first sub-request and a second sub-request;

[0217] A factor calculation module, used to process the first sub-request and the second sub-request concurrently using different threads to obtain corresponding factors to be detected;

[0218] A rule calculation module is used to combine the factors to be detected into the data to be detected by using the data initialization strategy;

[0219] The rule result integration module is used to integrate the data to be detected from different sub-requests;

[0220] The global rule result integration module integrates the data to be detected obtained from different instances;

[0221] The global result output module is used to detect the data to be detected through risk detection strategies, and output the detection results to the database, query memory and message queue in a synchronous persistence, synchronous non-persistence or asynchronous manner.

[0222] Each module in the above-mentioned detection device for compliance risk of resource interaction can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0223] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.17 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store sub-request data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for detecting resource interaction compliance risks is implemented.

[0224] Those skilled in the art will understand that Fig.17 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0225] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0226] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0227] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for detecting compliance risks of resource interactions, characterized in that: The method comprises: receiving resource interaction requests; Parsing the resource interaction request to obtain a first sub-request and a category of the resource interaction request; wherein the first sub-request is used to represent the interaction request of each resource in the resource interaction request; In the case where the category of the resource interaction request is a penetration resource interaction category, a penetration resource is obtained by screening from the first sub-request, and a second sub-request is generated by using the association relationship between the penetration resource and other resources; the second sub-request is used to represent a request for interaction with other resources associated with the penetration resource; wherein the penetration resource interaction category includes interaction of penetration resources; the penetration resource includes a resource that has an association relationship with other resources; The first sub-request and the second sub-request are detected by using different threads respectively to obtain a detection result of the compliance risk corresponding to the resource interaction request.

2. The method according to claim 1, characterized in that: The detecting the first sub-request and the second sub-request to obtain a detection result of the compliance risk corresponding to the resource interaction request includes: Interact with resources corresponding to the first sub-request and the second sub-request to obtain data to be detected corresponding to the resource interaction request; The data to be detected is detected using a preset risk detection strategy to obtain a detection result of the compliance risk of the resource interaction request.

3. The method according to claim 2, characterized in that The interacting with the resources corresponding to the first sub-request and the second sub-request to obtain the to-be-detected data corresponding to the resource interaction request includes: Interacting the resources corresponding to the first sub-request and the second sub-request to obtain factors to be detected corresponding to the first sub-request and the second sub-request; wherein the factors to be detected include resource data after the resources corresponding to the first sub-request and the second sub-request are interacted; The factors to be detected are matched by using a preset data initialization strategy to obtain the data to be detected corresponding to the resource interaction request; wherein the data initialization strategy includes an association relationship between the factors to be detected and the data to be detected.

4. The method according to claim 3, characterized in that The interacting with the resources corresponding to the first sub-request and the second sub-request to obtain the factors to be detected corresponding to the first sub-request and the second sub-request includes: Using the first sub-request and the second sub-request, respectively obtain identifications of factors to be detected corresponding to the first sub-request and the second sub-request; The identification of the factors to be detected is grouped by using a preset grouping strategy to obtain the identification group of the factors to be detected; wherein the grouping strategy includes grouping the identification of the factors to be detected by the influence range of the factors to be detected; the influence range is used to characterize the number of other factors that are associated with the factors to be detected; Using different processing nodes, the to-be-detected factor identification groups are processed respectively to obtain the to-be-detected factors corresponding to the to-be-detected factor identification groups; The factors to be detected corresponding to the group of identifications of the factors to be detected are merged to obtain the factors to be detected corresponding to the first sub-request and the second sub-request.

5. The method according to claim 3, characterized in that: The interacting with the resources corresponding to the first sub-request and the second sub-request to obtain the factors to be detected corresponding to the first sub-request and the second sub-request includes: Using a preset first classification strategy, respectively determine first categories of the first sub-request and the second sub-request, and using the first categories, obtain candidate to-be-detected factor identifiers corresponding to the first categories; Using a preset second classification strategy, respectively determine the second categories of the first sub-request and the second sub-request; Using the second category, matching the candidate to-be-detected factor identifiers to obtain a target to-be-detected factor identifier; The target to-be-detected factor identifier is used to obtain the to-be-detected factors corresponding to the first sub-request and the second sub-request.

6. The method according to claim 1, characterized in that After obtaining the detection result of the compliance risk corresponding to the resource interaction request, the method further includes: Outputting the compliance risk detection results to a preset database; Outputting the compliance risk detection result to a preset query memory; The compliance risk detection result is output to a preset message queue in an asynchronous manner.

7. The method according to claim 4, characterized in that The method further comprises: Establishing a preprocessing statement; wherein the preprocessing statement includes at least one of a pre-created request parsing statement, a statement for obtaining factors to be detected, and a statement for obtaining data to be detected; In response to parsing the resource interaction request, the request parsing statement is obtained, and the parameters in the request parsing statement are replaced and executed using the resource interaction request to obtain a sub-request corresponding to the resource interaction request; In response to obtaining the factor to be detected, obtaining the statement for obtaining the factor to be detected, replacing and executing the parameters in the statement for obtaining the factor to be detected by using the identifier of the factor to be detected, and obtaining the factor to be detected; In response to the acquisition of the data to be detected, the data acquisition statement to be detected is obtained, the factors to be detected corresponding to the data to be detected are obtained by using the data initialization strategy, and the parameters in the data to be detected statement are replaced and executed by using the factors to be detected to obtain the data to be detected.

8. A detection device for resource interaction compliance risk, characterized in that: The device comprises: A request receiving module, used for receiving resource interaction requests; A request parsing module, used to parse the resource interaction request to obtain a first sub-request and a category of the resource interaction request; wherein the first sub-request is used to represent the interaction request of each resource in the resource interaction request; A request derivation module, used for, when the category of the resource interaction request is a penetration resource interaction category, filtering out penetration resources from the first sub-request, and generating a second sub-request by using the association relationship between the penetration resource and other resources; the second sub-request is used to represent a request for interaction with other resources associated with the penetration resource; wherein the penetration resource interaction category includes interaction of penetration resources; and the penetration resource includes a resource that has an association relationship with other resources; The request detection module is used to detect the first sub-request and the second sub-request using different threads respectively, and obtain the detection result of the compliance risk corresponding to the resource interaction request.

9. The device according to claim 8, characterized in that The request detection module comprises: A data acquisition submodule, used to interact with resources corresponding to the first sub-request and the second sub-request to obtain data to be detected corresponding to the resource interaction request; The data detection submodule is used to detect the data to be detected by using a preset risk detection strategy to obtain a detection result of the compliance risk of the resource interaction request.

10. The device according to claim 9, characterized in that The data acquisition submodule includes: a factor acquisition unit, configured to interact with resources corresponding to the first sub-request and the second sub-request to obtain factors to be detected corresponding to the first sub-request and the second sub-request; wherein the factors to be detected include resource data after the resources corresponding to the first sub-request and the second sub-request are interacted; The data acquisition unit is used to match the factors to be detected by using a preset data initialization strategy to obtain the data to be detected corresponding to the resource interaction request; wherein the data initialization strategy includes the association relationship between the factors to be detected and the data to be detected.

11. The device according to claim 10, characterized in that The factor acquisition unit comprises: an identification acquisition subunit, used to obtain identifications of factors to be detected corresponding to the first sub-request and the second sub-request respectively by using the first sub-request and the second sub-request; The identification grouping subunit is used to group the identifications of the factors to be detected by using a preset grouping strategy to obtain the identification group of the factors to be detected; wherein the grouping strategy includes grouping the identifications of the factors to be detected by the influence range of the factors to be detected; the influence range is used to characterize the number of other factors that are associated with the factors to be detected; A factor acquisition subunit is used to process the to-be-detected factor identification groups respectively by using different processing nodes to obtain the to-be-detected factors corresponding to the to-be-detected factor identification groups; The factor acquisition subunit is further used to merge the factors to be detected corresponding to the group of factor identifications to be detected to obtain the factors to be detected corresponding to the first sub-request and the second sub-request.

12. The device according to claim 10, characterized in that The factor acquisition unit comprises: an identification acquisition subunit, configured to determine first categories of the first sub-request and the second sub-request respectively by using a preset first classification strategy, and to acquire identifications of candidate factors to be detected corresponding to the first categories by using the first categories; a request classification subunit, configured to determine second categories of the first sub-request and the second sub-request respectively by using a preset second classification strategy; The identification acquisition subunit is used to obtain a target to-be-detected factor identification by matching from the candidate to-be-detected factor identifications using the second category; The factor acquisition subunit is used to obtain the factors to be detected corresponding to the first sub-request and the second sub-request by using the target factor identifier to be detected.

13. The device according to claim 8, characterized in that The request detection module further includes: A result output module, used to output the compliance risk detection result to a preset database; The result output module is further used to output the compliance risk detection result to a preset query memory; The result output module is also used to output the compliance risk detection result to a preset message queue in an asynchronous manner.

14. The device according to claim 11, characterized in that The device also includes: A statement building module, used for building a pre-processing statement; wherein the pre-processing statement includes at least one of a pre-created request parsing statement, a statement for obtaining factors to be detected, and a statement for obtaining data to be detected; A statement using module, used for, in response to parsing a resource interaction request, obtaining the request parsing statement, replacing and executing parameters in the request parsing statement using the resource interaction request, and obtaining a sub-request corresponding to the resource interaction request; The statement using module is further used to obtain the statement for obtaining the factor to be detected in response to obtaining the factor to be detected, and to replace and execute the parameters in the statement for obtaining the factor to be detected by using the factor to be detected identifier to obtain the factor to be detected; The statement usage module is also used to obtain the data to be detected acquisition statement in response to the acquisition of the data to be detected, use the data initialization strategy to obtain the factors to be detected corresponding to the data to be detected, and use the factors to be detected to replace and execute the parameters in the data to be detected statement to obtain the data to be detected.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

16. 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 7 are implemented.

17. A computer program product comprising a computer program, 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 7 are implemented.

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