Engine configuration method and device, risk assessment method and device, equipment and storage medium

Abstract

The invention provides an engine configuration method which can be applied to the technical field of big data. The engine configuration method comprises the following steps: constructing a risk model in a risk assessment engine based on business information related to risk assessment and a rule engine integrated with different decision tools; according to service logic in the service information, integrating a plurality of index items defined in the risk model and calculation rules of the index items in the decision tool to generate a decision flow corresponding to the risk model, the decision flow comprising a plurality of decision nodes, and the decision nodes comprising a plurality of decision nodes; each decision node is correspondingly provided with an evaluation strategy determined according to the index item and the calculation rule; and configuring the decision flow into a risk assessment engine so as to perform risk assessment by using the configured risk assessment engine. The invention further provides a risk assessment method, an engine configuration device, equipment, a storage medium and a program product.

Description

Technical Field

[0001] The present disclosure relates to the field of big data, and more specifically, to an engine configuration method, a risk assessment method, an apparatus, a device, and a storage medium. Background Art

[0002] Customs needs to take risk management measures during customs inspection. In related technologies, when conducting customs inspection, the basic information of the assessment object is input into the risk assessment system, and the assessment rules in the risk assessment system are used to conduct the assessment, thereby completing the inspection of the assessment object.

[0003] In the process of realizing the concept of the present disclosure, the inventors found that there are at least the following problems in the related technology: since the evaluation rules in the risk assessment system are relatively simple, the accuracy of the risk assessment results output by the risk assessment system is low when facing different business scenarios. Summary of the invention

[0004] In view of this, the present disclosure provides an engine configuration method, a risk assessment method, an apparatus, a device, a storage medium, and a program product.

[0005] One aspect of the present disclosure provides an engine configuration method, comprising: constructing a risk model in a risk assessment engine based on business information related to risk assessment and a rule engine integrated with different decision tools; integrating multiple indicator items defined in the risk model and calculation rules for each of the indicator items in the decision tool according to the business logic in the business information, and generating a decision flow corresponding to the risk model, wherein the decision flow includes multiple decision nodes, and each decision node corresponds to an assessment strategy determined according to the indicator items and the calculation rules; configuring the decision flow to the risk assessment engine to perform risk assessment using the configured risk assessment engine.

[0006] According to an embodiment of the present disclosure, the multiple indicator items defined in the above-mentioned risk model and the calculation rules for each of the above-mentioned indicator items in the above-mentioned decision-making tool are integrated according to the business logic in the above-mentioned business information to generate a decision flow corresponding to the above-mentioned risk model, including: defining multiple process nodes, wherein the multiple process nodes include a start node, the above-mentioned decision node and an end node; integrating the above-mentioned indicator items with the multiple calculation rules respectively to obtain multiple evaluation strategies; configuring the multiple evaluation strategies into corresponding decision nodes according to the above-mentioned business logic to orchestrate the multiple process nodes to generate the above-mentioned decision flow.

[0007] According to an embodiment of the present disclosure, the above-mentioned indicator items are respectively integrated with the multiple above-mentioned calculation rules to obtain multiple evaluation strategies, including: determining the indicator threshold corresponding to each of the above-mentioned indicator items; integrating the multiple above-mentioned indicator thresholds according to the calculation logic in each of the above-mentioned calculation rules and the association relationship between the multiple above-mentioned indicator items to obtain multiple above-mentioned evaluation strategies.

[0008] According to an embodiment of the present disclosure, the above-mentioned multiple process nodes are orchestrated to generate the above-mentioned decision flow, including: based on the above-mentioned business logic, the decision nodes corresponding to the different above-mentioned calculation rules are orchestrated to obtain multiple decision branches, wherein the multiple above-mentioned decision branches can be calculated in parallel when performing risk assessment; the multiple above-mentioned decision branches are respectively connected to the above-mentioned start node and the corresponding end node to obtain the above-mentioned decision flow.

[0009] According to an embodiment of the present disclosure, the above method also includes: determining the data source of each of the above indicator items in the above business information, wherein the above data source is the connection information between the application system, and the above application system is used to provide the above business information; configuring the above data source in the above risk assessment engine to obtain the data sent by the above application system based on the above data source information.

[0010] According to an embodiment of the present disclosure, the above-mentioned method also includes: configuring multiple data processing indicators in the above-mentioned risk assessment engine, wherein the above-mentioned data processing indicators are used to obtain the business data required for risk assessment, and the above-mentioned business data includes database information and service interface information and service framework information used when performing risk assessment; according to the calculation logic in the above-mentioned calculation rules, configuring multiple data calculation indicators in the above-mentioned risk assessment engine, wherein the above-mentioned data calculation indicators are used to operate on the results calculated by the above-mentioned assessment strategy when performing risk assessment.

[0011] Another aspect of the present disclosure provides a risk assessment method, comprising: in response to a received risk assessment request, inputting the data to be assessed in the risk assessment request into a configured risk assessment engine; performing risk assessment on the data to be assessed using a decision flow corresponding to a risk model in the risk assessment engine, and outputting a risk assessment result.

[0012] According to an embodiment of the present disclosure, the above-mentioned decision flow corresponding to the risk model in the above-mentioned risk assessment engine is used to perform risk assessment on the above-mentioned data to be assessed, and a risk assessment result is output, including: determining multiple evaluation indicators in the above-mentioned data to be assessed; according to the multiple above-mentioned evaluation indicators, corresponding indicator data is obtained from business information related to the risk assessment; and the above-mentioned indicator data is risk assessed using the evaluation strategy in the above-mentioned decision flow to obtain the above-mentioned risk assessment result.

[0013] Another aspect of the present disclosure provides an engine configuration device, including: a model construction module, which is used to construct a risk model in the risk assessment engine based on business information related to risk assessment and a rule engine integrated with different decision tools; a decision flow generation module, which is used to integrate multiple indicator items defined in the above risk model and the calculation rules for each of the above indicator items in the above decision tool according to the business logic in the above business information, and generate a decision flow corresponding to the above risk model, wherein the above decision flow includes multiple decision nodes, and each of the above decision nodes corresponds to an assessment strategy determined according to the above indicator items and the above calculation rules; an engine configuration module, which is used to configure the above decision flow into the above risk assessment engine, so as to use the configured risk assessment engine to perform risk assessment.

[0014] Another aspect of the present disclosure provides a risk assessment device, including: a request output module, used to respond to a received risk assessment request and input the data to be assessed in the above risk assessment request into a configured risk assessment engine; a risk assessment module, used to perform risk assessment on the above data to be assessed using a decision flow corresponding to the risk model in the above risk assessment engine, and output a risk assessment result.

[0015] Another aspect of the present disclosure provides an electronic device, including: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described above.

[0016] Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the above method when executed.

[0017] Another aspect of the present disclosure provides a computer program product, the computer program product comprising computer executable instructions, and the instructions are used to implement the above method when executed.

[0018] According to an embodiment of the present disclosure, a risk model in a risk assessment engine is constructed based on business information related to risk assessment and a rule engine integrated with different decision-making tools, so that when facing different businesses, the corresponding risk model can be selected for risk assessment. According to the business logic in the business information, multiple indicator items defined in the risk model and the calculation rules for each indicator item in the decision tool are integrated to generate a decision flow corresponding to the risk model. Since the decision flow is generated according to the business logic, and the decision flow includes evaluation decisions corresponding to multiple calculation rules, the decision flow is configured in the risk assessment engine, so that the target risk assessment engine can be evaluated more accurately, and the efficiency of risk verification is effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0020] Figure 1 A diagram schematically illustrates an application scenario of an engine configuration method, a risk assessment method, an apparatus, a device, a medium, and a program product according to an embodiment of the present disclosure;

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

[0022] Figure 3 A schematic diagram of a risk assessment engine according to an embodiment of the present disclosure is schematically shown;

[0023] Figure 4 A schematic structural block diagram of an indicator system of a risk assessment engine according to an embodiment of the present disclosure is schematically shown;

[0024] Figure 5 A flowchart of an engine configuration method according to another embodiment of the present disclosure is schematically shown;

[0025] Figure 6 A schematic block diagram of a message service for risk assessment according to an embodiment of the present disclosure is schematically shown;

[0026] Figure 7 A flow chart schematically shows a risk assessment method according to an embodiment of the present disclosure;

[0027] Figure 8 The following schematically shows an assessment flow chart applicable to the risk assessment method according to an embodiment of the present disclosure;

[0028] Fig. 9 The structure block diagram of the engine configuration device according to the embodiment of the present disclosure is schematically shown;

[0029] Fig.10 A schematic diagram of a risk assessment device according to an embodiment of the present disclosure is shown; and

[0030] Fig.11 A block diagram of an electronic device suitable for implementing an engine configuration method and a risk assessment method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0031] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0032] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0033] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0034] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0035] In the embodiments of the present disclosure, the collection, updating, analysis, processing, use, transmission, provision, disclosure, storage, etc. of the data involved (for example, including but not limited to user personal information) are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information and network security.

[0036] In the embodiments of the present disclosure, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0037] The embodiments of the present disclosure provide an engine configuration method, a risk assessment method, an apparatus, a device, a storage medium, and a program product. The engine configuration method includes: constructing a risk model in a risk assessment engine based on business information related to risk assessment and a rule engine integrated with different decision tools; integrating multiple indicator items defined in the risk model and the calculation rules for each indicator item in the decision tool according to the business logic in the business information, generating a decision flow corresponding to the risk model, wherein the decision flow includes multiple decision nodes, and each decision node corresponds to an assessment strategy determined according to the indicator items and the calculation rules; configuring the decision flow to the risk assessment engine to perform risk assessment using the configured risk assessment engine.

[0038] Figure 1 The application scenario diagram of the engine configuration method, risk assessment method, apparatus, device, medium and program product according to the embodiment of the present disclosure is schematically shown. It should be noted that: Figure 1 What is shown is merely an example of a system architecture to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0039] like Figure 1 As shown, the system architecture 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0040] The user may use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, and / or social platform software, etc. (only for example).

[0041] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0042] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0043] It should be noted that the engine configuration method and risk assessment method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the engine configuration device and risk assessment device provided in the embodiment of the present disclosure can generally be set in the server 105. The engine configuration method and risk assessment method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Correspondingly, the engine configuration device and risk assessment device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Alternatively, the engine configuration method and risk assessment method provided in the embodiment of the present disclosure can also be executed by the first terminal device 101, the second terminal device 102 or the third terminal device 103, or by other terminal devices that are different from the first terminal device 101, the second terminal device 102 or the third terminal device 103. Accordingly, the engine configuration device and risk assessment device provided in the embodiments of the present disclosure may also be arranged in the first terminal device 101, the second terminal device 102 or the third terminal device 103, or in other terminal devices different from the first terminal device 101, the second terminal device 102 or the third terminal device 103.

[0044] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0045] Figure 2 The flowchart of the engine configuration method according to the embodiment of the present disclosure is schematically shown.

[0046] like Figure 2 As shown, the method includes operations S201 to S230.

[0047] In operation S210, a risk model in a risk assessment engine is constructed based on business information related to risk assessment and a rule engine integrated with different decision-making tools.

[0048] In operation S220, multiple indicator items defined in the risk model and the calculation rules for each indicator item in the decision tool are integrated according to the business logic in the business information to generate a decision flow corresponding to the risk model, wherein the decision flow includes multiple decision nodes, and each decision node corresponds to an evaluation strategy determined according to the indicator items and the calculation rules.

[0049] In operation S230, the decision flow is configured in the risk assessment engine to perform risk assessment using the configured risk assessment engine.

[0050] According to the embodiments of the present disclosure, the customs needs to take risk management measures during the customs clearance inspection. Specifically, the customs clearance risk application submits the basic information of the vehicle or personnel to the risk assessment engine for risk assessment, and feeds back the risk assessment results to the customs clearance risk application. Among them, the customs clearance risk application refers to any application system that requires risk assessment in the customs clearance business.

[0051] According to the embodiments of the present disclosure, the customs clearance risk application will develop and change according to actual needs. The customs clearance risk application includes customs clearance risk application, vehicle clearance risk application and cargo clearance risk application. The system provides a risk assessment engine client to facilitate the communication between the customs clearance risk application and the risk assessment engine. The customs clearance risk application can also directly send a risk assessment request to the risk assessment system through the message queue service and monitor the risk assessment results.

[0052] According to the embodiments of the present disclosure, the risk assessment engine needs to use a large amount of business information from other customs systems when conducting risk assessment. Since different systems provide data in different ways, many different communication protocols are involved, such as database protocols and RESTful (Representational State Transfer) interface protocols. In order to facilitate the acquisition of business information from different systems through different protocols, the risk assessment engine is supported by an indicator system. The indicator system of the risk assessment engine supports the ability to obtain assessment indicators for a large number of different protocols, and supports the plug-in expansion capability of new protocols, which can easily expand capabilities.

[0053] According to the embodiments of the present disclosure, business information may include the following types of information: Real-time information, information being generated by related business, such as customs declaration information of goods for customs inspection. Historical information, historical information of related business, such as records of drivers or customs declaration companies not meeting regulations, and records of passengers' travel. Related information, other auxiliary information of related business, such as the special list of customs' recent review. It should be noted that the personal information used by this technical solution is limited to information with the individual's separate consent.

[0054] According to the embodiments of the present disclosure, business information may specifically include data of a variety of different businesses. Common business information related to customs clearance risks is as follows: Enterprise information, including basic enterprise information such as registration information, business information, management information and certification information of the enterprise. Customs clearance supervision information, including historical statistics of supervision information of relevant enterprises, especially alarms and processing information generated by supervision. Non-compliant information, including historical records of non-compliant information of relevant enterprises, vehicles, drivers, passengers, etc. Tax information, including tax history records of relevant enterprises or tax payment information of goods, such as the amount of tax owed, the scale of tax payment, etc. Logistics information, including logistics enterprise information, vehicle information, driver information and transportation-related information, etc. Goods information, including goods declaration information, goods origin information, goods price information, etc. Transaction information, including basic information of buyers and sellers, the relationship between buyers and sellers, transaction amount, transaction characteristics, etc. Environmental information, including policy information, market change information, key keywords or goods for strict inspection, etc.

[0055] Figure 3 A schematic diagram of a risk assessment engine according to an embodiment of the present disclosure is schematically shown.

[0056] According to the embodiment of the present disclosure, the risk assessment engine is composed of three parts, namely, the real-time risk calculation engine 310, the risk engine management 320 and the risk assessment configuration management 330. The real-time risk calculation engine 310 is the core component of the risk assessment engine and is responsible for the execution of the actual risk calculation task. The risk engine management 320 is responsible for monitoring and managing the real-time risk calculation engine. The risk assessment configuration management 330 configures and builds the risk model through a visual configuration system. After the risk assessment configuration management 330 completes the configuration of the risk model, it sends the risk model to the risk engine management 320 for model publishing by operating the decision flow publishing action. The risk engine management 320 will parse the risk model, allocate computing resource instances according to the resource configuration, publish the decision flow to the real-time risk calculation engine 310, and after the publishing action is executed, the risk engine management 320 returns the publishing result to the risk assessment configuration management 330. When the real-time risk calculation engine 310 is running daily, it will send the running status to the risk engine management 320, so that the risk engine management 320 can monitor and manage all risk computing resources.

[0057] According to the embodiments of the present disclosure, in the process of constructing a risk model, the objectives and requirements of the risk model are determined based on business information, mainly including the type of data to be processed, the business scenario of risk assessment, the complexity of the decision-making process, etc. After determining the objectives and requirements, the risk model is designed to construct a risk model that meets the objectives and requirements and integrates different decision-making tools.

[0058] According to the embodiments of the present disclosure, multiple indicators defined in the risk model and the calculation rules for each indicator in each decision tool are determined. The decision tools include decision tables, decision trees, score cards, complex rule sets, scripts and other tools. The evaluation strategy is determined based on the multiple indicators and calculation rules, and each risk model has a specific evaluation strategy.

[0059] According to an embodiment of the present disclosure, multiple indicator items and the calculation rules of each indicator item are integrated according to the business logic in the business information to generate a decision flow corresponding to the risk model. Among them, the decision flow is a flowchart that puts the indicator items and the evaluation strategy together. Specifically, the decision flow can be configured in a visual way, and the decision flow is used to control the calculation process of the indicator items and the evaluation strategy. The decision flow consists of nodes and lines. The nodes of the decision flow represent the evaluation strategy to be executed or the way to control the process. The lines represent the execution order between the nodes. The nodes also provide attribute configuration to set the execution characteristics of the nodes.

[0060] According to an embodiment of the present disclosure, the configured decision flow is published to the risk assessment engine for risk assessment. The decision flow publishing submits a request to the risk engine management through the message service, and also receives the publishing result of the risk engine management through the message service, and returns the publishing result to the risk assessment configuration management. When performing risk assessment, the evaluation strategy and the calculation tasks related to the indicator items are created according to the definition of the risk model, and the evaluation process of the risk model is coordinated by the decision flow calculation service.

[0061] According to the embodiments of the present disclosure, based on the business information related to risk assessment and the rule engine integrated with different decision-making tools, the risk model in the risk assessment engine is constructed so that when facing different businesses, the corresponding risk model can be selected for risk assessment. According to the business logic in the business information, the multiple indicator items defined in the risk model and the calculation rules for each indicator item in the decision tool are integrated to generate a decision flow corresponding to the risk model. Since the decision flow is generated according to the business logic, and the decision flow includes evaluation decisions corresponding to multiple calculation rules, the decision flow is configured in the risk assessment engine, making the target risk assessment engine more accurate and effectively improving the efficiency of risk verification.

[0062] According to an embodiment of the present disclosure, based on the business logic in the business information, multiple indicator items defined in the risk model and the calculation rules for each indicator item in the decision tool are integrated to generate a decision flow corresponding to the risk model, including: defining multiple process nodes, wherein the multiple process nodes include a start node, a decision node and an end node; integrating the indicator items with the multiple calculation rules respectively to obtain multiple evaluation strategies; configuring the multiple evaluation strategies to the corresponding decision nodes according to the business logic to orchestrate the multiple process nodes to generate a decision flow.

[0063] According to the embodiments of the present disclosure, the decision flow can be configured in a visual manner. The configuration of the decision flow mainly includes the process nodes and the lines between the nodes. The nodes represent the execution strategy or control the execution process, and the lines represent the execution order of the nodes. The process nodes mainly include the following: start node. A decision flow has one and only one start node, which is the entry point for the decision flow calculation. Strategy node. A decision flow has multiple strategy nodes. Each strategy node can select an evaluation strategy to calculate the risk assessment results. The calculation results can be assigned to the basic indicators and can be used as calculation parameters for other evaluation strategies. End node. There can be one or more end nodes. When the decision flow is calculated to any end node, the decision flow calculation is completed. After the result is fed back, the task is completed.

[0064] According to an embodiment of the present disclosure, after defining multiple process nodes, the indicator items are integrated with multiple calculation rules to obtain multiple evaluation strategies. Specifically, each evaluation strategy may include the calculation of multiple indicator items, and there is a correlation between the multiple indicator items. According to the business logic, multiple evaluation strategies are configured to the corresponding decision nodes to orchestrate multiple process nodes and generate a decision flow. Among them, the business logic is the business logic applied to each customs clearance risk. By configuring the evaluation strategy and integrating the evaluation strategy with the indicator items to generate a decision flow, the decision flow is more in line with the business logic, and can perform targeted risk assessment in the face of different characteristics, and the risk assessment is more accurate.

[0065] According to an embodiment of the present disclosure, the indicator items are respectively integrated with multiple calculation rules to obtain multiple evaluation strategies, including: determining the indicator threshold corresponding to each indicator item; integrating the multiple indicator thresholds according to the calculation logic in each calculation rule and the association relationship between the multiple indicator items to obtain multiple evaluation strategies.

[0066] According to the embodiments of the present disclosure, each indicator item has a corresponding indicator threshold. When comparing the indicator data corresponding to the indicator item with the indicator threshold, the indicator data is first converted into a numerical value for comparison. According to the calculation logic in each calculation rule and the association between multiple indicator items, multiple indicator thresholds are integrated to obtain a multi-evaluation strategy.

[0067] According to the embodiments of the present disclosure, the evaluation strategy is to calculate the comprehensive measurement values ​​of some specific meanings in the risk model based on the indicator items. The evaluation strategy is some mature models and is the main methodology for risk assessment. Specifically, the evaluation strategy mainly includes the following: decision table, which is used to judge the hit features with the decision table model and calculate the comprehensive quantitative measurement value. Decision tree, which is used to judge the hit features with the decision tree model and calculate the comprehensive quantitative measurement value. Score card, which is used to calculate the comprehensive result of various quantitative measurement values ​​with the score card model. Complex rule set, which is used to verify the hit conditions with the rule pattern, so as to calculate the quantitative measurement value. Script strategy, which is used to set up a personalized strategy model and calculate the measurement value through a custom scripting language. Machine learning model, which is used to import the trained machine learning model into the evaluation strategy and calculate the strategy value according to the parameter index.

[0068] According to the embodiments of the present disclosure, the configuration of the assessment strategy is to configure the structure, rules and attributes through a visual configuration method. The calculation of the assessment strategy will calculate the assessment strategy according to the configuration and input parameters of the risk model to obtain the corresponding measurement value. By configuring the assessment strategy, a variety of risk assessment methods can be implemented, making the risk assessment results more accurate.

[0069] According to an embodiment of the present disclosure, multiple process nodes are orchestrated to generate a decision flow, including: based on business logic, the decision nodes corresponding to different calculation rules are orchestrated to obtain multiple decision branches, wherein the multiple decision branches can be calculated in parallel when performing risk assessment; the multiple decision branches are respectively connected to the start node and the corresponding end node to obtain a decision flow.

[0070] According to the embodiments of the present disclosure, the decision flow is the final presentation of the risk model, which integrates relevant indicators and evaluation strategies, and calculates the final risk assessment results by controlling the execution process and coordinating the calculation rules. The decision flow is the core module that responds to risk assessment requests and calculates risk assessment results. By configuring a visual decision flow flowchart, the risk real-time calculation engine is responsible for calculating and executing the flowchart. The flowchart configuration of the decision flow mainly includes process nodes and connections between nodes. The nodes represent the execution strategy or control execution process, and the connections represent the execution order of the nodes. The process nodes mainly include start nodes, strategy nodes, and end nodes.

[0071] According to an embodiment of the present disclosure, the decision flow also includes a control node for controlling the calculation process, including diversion grouping, branch parallelism and convergence and merging. The decision flow supports multiple control methods so that it can be based on rules and requested parameters. The control method performs calculations through different strategies or sequences, mainly including the following control methods: diversion grouping, configuring multiple risk models in the decision flow, and using the diversion grouping method to select different risk models to calculate risk assessment results. Diversion grouping can be based on weights or attributes, rules. The diversion grouping method is convenient for AB testing, and it is also convenient to use different risk models for evaluation according to different characteristics.

[0072] According to the embodiments of the present disclosure, the control method also includes branch parallelism, which can branch the process according to the rules, multiple branches can be calculated in parallel, and each branch can also decide whether to execute through the rule configuration. Convergence and merging can converge and merge the processes of multiple parallel branches, and after summarizing the calculation results of each branch, calculate the measurement value of the comprehensive model.

[0073] According to the embodiments of the present disclosure, in order to better reuse the decision flow configuration, the decision flow system also supports sub-processes, which can utilize the calculation results of other decision flows. In the risk assessment embodiment, it is necessary to evaluate the effect of the risk model. The decision flow system also supports champion challenges, which can calculate and compare multiple risk models at the same time, and can compare with measured values ​​or actual effects to evaluate the effect of the risk model. By setting a variety of control methods according to the decision branches, the risk assessment process is more diversified.

[0074] According to an embodiment of the present disclosure, the engine configuration method also includes: determining the data source of each indicator item in the business information, wherein the data source is the connection information between the application system and the application system is used to provide business information; configuring the data source in the risk assessment engine to obtain the data sent by the application system based on the data source information.

[0075] According to the embodiments of the present disclosure, the data source includes the communication protocol, address, user name, password, parameters and other connection information of the application system. The application system here refers to the customs third-party system in the risk assessment-related business information. Different application systems provide different protocol methods, so the configuration of the data source also supports different protocols, including but not limited to relational databases, KV (key-value) key-value databases, graph databases, Restful interfaces, Web Service (programmable network) application program interfaces, etc. Configuring the data source in the risk assessment engine enables the data corresponding to each indicator item to be obtained from the business information, effectively improving the efficiency of risk assessment.

[0076] Figure 4A schematic structural block diagram of an indicator system of a risk assessment engine according to an embodiment of the present disclosure is schematically shown.

[0077] According to an embodiment of the present disclosure, the engine configuration method also includes: configuring multiple data processing indicators in the risk assessment engine, wherein the data processing indicators are used to obtain business data required for risk assessment, and the business data includes database information and service interface information and service framework information used when performing risk assessment; according to the calculation logic in the calculation rules, configuring multiple data calculation indicators in the risk assessment engine, wherein the data calculation indicators are used to operate on the results calculated by the assessment strategy when performing risk assessment.

[0078] According to the embodiments of the present disclosure, in order to effectively utilize the indicator data provided by various application systems, the risk assessment engine indicator system supports multiple data sources. The indicator system is the internal support of the risk assessment system, including data processing indicators for obtaining external application system data, and also includes data calculation indicators corresponding to relevant functional modules of the indicator system.

[0079] like Figure 4 As shown, the data processing indicators 410 mainly include the following: database indicators, which are used to obtain relevant indicators in the database 431 from the business data center, including relational databases and non-relational databases. Non-relational databases include: KV databases, document databases, and graph databases, etc. The specific data is provided by the database part of the business data center. Database indicators can obtain specific data items, aggregated statistical values, or retrieved relationship values, and can be configured as needed.

[0080] According to an embodiment of the present disclosure, the data processing index 410 also includes an interface index, which is used to obtain relevant indicators from the data service 432 in the business data center, which is generally a data service provided through a Restful or Webservice interface. Stream computing indicators are used to obtain relevant indicators provided by the stream processing service 433 from the business data center, where the stream processing service 433 includes Spark, Storm and Flink (open source stream processing framework), etc. The computing indicators generated by the stream processing service 433 can be cached in the real-time indicator cache 434 service, and the stream processing service 433 obtains the indicator value of the real-time stream computing through the real-time indicator cache 434.

[0081] According to the embodiments of the present disclosure, the data calculation indicators 420 mainly include the following: Derived indicators, which are used to obtain indirect indicator values ​​through calculations of other indicator items, and can be configured through scripts. Constant indicators, which are used to facilitate configuration and maintenance of constant indicators. Basic indicators, which are indicator items used to transfer and store intermediate calculation results.

[0082] According to the embodiment of the present disclosure, the indicator system configures the data sources and related parameters of the above various indicator items through the indicator configuration function, and then publishes them to the real-time risk calculation engine, and calculates the real-time indicator value through the indicator calculation function. Since some indicators are frequently used, but the indicator values ​​will not change in a short time, the indicator values ​​will be cached, and when querying the indicators, the indicator values ​​in the cache will be queried first. The indicators are configured to facilitate the normal execution of the risk assessment service.

[0083] Figure 5 The flowchart of the engine configuration method according to another embodiment of the present disclosure is schematically shown.

[0084] According to an embodiment of the present disclosure,

[0085] In operation S510, a risk model is designed.

[0086] In operation S520, risk indicator data is prepared.

[0087] In operation S530 , risk data sources are configured.

[0088] In operation S540, risk indicators are configured.

[0089] In operation S550, an evaluation strategy is configured.

[0090] In operation S560, a decision flow is configured.

[0091] In operation S570, the decision flow is published.

[0092] In operation S580, risk assessment is performed.

[0093] According to the embodiments of the present disclosure, before risk assessment, it is necessary to first design a risk model, which includes the data involved, data sources and assessment methods. Then prepare the risk indicator data, determine all application systems that need to provide indicator data, and the method for obtaining the indicator data. After clarifying the method for obtaining the indicator data, first configure the risk data source and determine the data source of various indicator items. Then configure the risk indicators, such as database indicators, interface indicators, basic indicators, etc. On this basis, configure the assessment strategy to obtain and calculate complex comprehensive indicators.

[0094] According to the embodiments of the present disclosure, after the configuration of the indicator items and the evaluation strategy is completed, the decision flow can be configured, and the various nodes, processes and related attributes of the decision flow can be configured. After the decision flow configuration is completed, the risk model can be published. When publishing the decision flow, the data source, indicator items and evaluation strategy will be published together. After the decision flow is published, the risk assessment can be performed. The application system that requires risk assessment sends a risk assessment request message to the message service, and the risk assessment engine will execute it and return the result to the message service. At this point, the risk assessment implementation steps are completed.

[0095] According to the embodiments of the present disclosure, data sources, indicator items and evaluation strategies can be shared between different risk models. In the customs clearance risk application, many different risk models will be included, and many of the same indicator data will be used in different risk models. There is no need to reconfigure the reused indicator items and evaluation strategies. In order to improve the efficiency of indicator data acquisition, the system will cache the indicator values. When configuring the indicator items, it is necessary to determine the effective time of the cache, etc., to prevent the cached indicator values ​​from being updated untimely and affecting the evaluation.

[0096] According to the embodiments of the present disclosure, the risk engine management is the scheduling coordination and manager of the risk assessment engine, which is mainly used to allocate the execution instance resources of the risk model, monitor the running status of the risk real-time computing engine, and repair faults. In a risk assessment engine embodiment, it includes a risk engine management instance and multiple risk real-time computing engine instances. The risk engine management module is responsible for the resource allocation and monitoring management of all risk real-time computing engine instances. The risk engine management includes the following main functions:

[0097] Resource allocation is used to allocate computing resources of the risk real-time computing engine. Based on the computing power of each risk real-time computing engine and the computing resource requirements of the risk model, the computing instances of each risk real-time computing engine are evenly allocated to help maintain a balanced overall system performance. When allocating computing resources for risk models, risk models will be allocated to multiple different risk real-time computing engine services as much as possible to ensure that the evaluation calculations of related risk models are available at any time.

[0098] Computing instance publishing: Based on the result of resource allocation, the risk model computing instance is published or released to the risk real-time computing engine service. The publishing of computing instances is coordinated through the message service. The use of the message service can reduce the coupling of each module and facilitate the independent expansion of each module.

[0099] Resource operation monitoring, the risk engine management module manages multiple risk real-time computing engine instances, which are registered with the risk engine management service. The risk engine management monitors the operation of each risk real-time computing engine. The risk engine management monitors the risk real-time computing engine through asynchronous communication through the message service, which is conducive to increasing the utilization of computing resources and improving computing throughput, and is also conducive to the switching flexibility of the risk engine management module, facilitating deployment and maintenance.

[0100] Computing resource recovery is used to recover failed computing resources. When the system detects a computing resource failure, it will rebalance the computing resources based on the current overall operating conditions and redistribute the failed computing resources to the available risk real-time computing engines. Packet loss or flash interruptions may occur in network communications, which will lead to inaccurate status judgments, and some failures will recover quickly and automatically, so there is no need to spend more money to recover computing resources. Therefore, time requirements will be set for fault judgments, and there is no need to switch and recover for failures that can be automatically and quickly recovered or false failures; in the case of long-term failures, the system will redistribute resources to reallocate the computing instances of the failed system to other computing resource services.

[0101] Figure 6 A schematic block diagram of a message service for risk assessment according to an embodiment of the present disclosure is schematically shown.

[0102] According to the embodiments of the present disclosure, in customs applications, risk assessment involves a large number of assessment objects, has high real-time requirements, and needs to provide risk assessment services for many different systems. The risk assessment system mainly communicates and coordinates through message services to ensure service performance and convenience.

[0103] like Figure 6 As shown, when the third-party system of risk assessment related information 610 makes an indicator data request and an indicator data result feedback, it is necessary to use the message service 620 to communicate between other modules according to the communication protocol of the related information system.

[0104] According to the embodiments of the present disclosure, the message service 620 of the customs clearance risk assessment system adopts a message queue service, which can not only ensure the immediacy of the message, but also ensure the loose coupling of the modules. At the same time, it adopts an asynchronous non-blocking communication mode, which is conducive to improving the system throughput, shortening the system response time, and improving the user experience.

[0105] According to an embodiment of the present disclosure, when the risk assessment system needs to calculate a certain risk value, the user configures the risk model visually through the risk assessment configuration management 630, and sends a risk model publishing message to the message service 620 to trigger the risk model publishing command. After the sending is completed, the message listening service is used to receive the model publishing result on the message service 620. When the risk assessment system completes the processing of the publishing command, it will send the processing result of the publishing command to the message service 620. The risk assessment configuration management 630 can understand the execution status of the risk model release by receiving this message and feedback to the user.

[0106] According to an embodiment of the present disclosure, the risk engine management 640 monitors the model release instruction message on the message service 620. Once a model release command is received, it immediately performs release processing, allocates computing resources, and sends a model allocation command to the message service 620, and receives the processing result of the allocation instruction through message monitoring.

[0107] According to an embodiment of the present disclosure, the risk real-time calculation engine 650 monitors the model release message on the message service 620. After receiving the message, it will create a computing instance and a message queue, start the monitoring service of the relevant risk assessment request, monitor the risk assessment request, and prepare to respond to the relevant processing request. When the model release is completed, the command execution result is sent to the message service 620.

[0108] According to the embodiments of the present disclosure, the real-time risk calculation engine 650 is the core of the risk assessment engine, and all risk value calculations are completed by this module. According to the definition of the risk model, the real-time risk calculation engine 650 decomposes the model into different calculation units such as indicators, strategies and decision flows for calculation respectively. The system coordinates the results and processes of different calculation units through the message service 620, so that the IO-intensive indicator calculation and the CPU-intensive strategy calculation can be separated, and resources can be optimized and allocated separately. The real-time risk calculation engine 650 adopts an asynchronous non-blocking task scheduling model to coordinate the scheduling of the overall model, which can effectively utilize computing resources and improve the overall throughput of the system. The real-time risk calculation engine 650 includes the following main functions:

[0109] Indicator calculation is used to obtain and calculate the indicator items defined in the risk model. Indicator acquisition is mainly an IO-intensive task. The system supports the concurrent execution and calculation of multiple indicators. Through the asynchronous non-blocking thread model, it can support the concurrent execution of as many indicators as possible to improve the overall response efficiency.

[0110] Strategy calculation is used to calculate the rules and logic defined in the strategy model in the risk model and obtain the measurement value of the strategy model. The strategy model mainly calculates the indicators according to the model design rules and logic. It is a CPU-intensive task. The system controls the number of strategy instances to avoid a large number of thread switches affecting CPU utilization when threads are concurrent.

[0111] Decision flow calculation is used to coordinate the execution order and process of indicators and strategies in the risk model, calculate and obtain risk assessment values. When executing decision flow calculation, it is necessary to decompose the process, send the calculation tasks of indicators and strategies, and receive the calculation results of indicators and strategies. According to the feedback results, the further execution of the process is controlled. In order to reduce the efficiency of network data transmission, the strategies and indicator tasks sent by the decision flow are sent to the current risk real-time calculation engine 650 for execution. When controlling the overall process, the asynchronous non-blocking control model is used to improve the resource utilization and overall throughput of the risk model.

[0112] Local resource management is used to manage the local computing resources of the risk real-time computing engine 650, mainly the creation, allocation and maintenance of indicators, policies and decision flow computing instances. At the same time, this function also maintains the current resource usage and records the computing resource load. When the load is large, new computing tasks will be assigned to other risk real-time computing engines 650 for execution.

[0113] Task scheduling is used to coordinate and schedule the execution of all computing tasks. The risk real-time computing engine 650 adopts asynchronous task scheduling to ensure the throughput of the engine. The computing tasks of indicators, strategies and decision flows are all controlled and executed by task scheduling. For tasks waiting for IO execution, the computing instance resources are first released and wait in the queue. When the result is received, an execution request is immediately issued and the computing instance resources are waited for allocation. Once the computing instance is obtained, the task execution continues immediately.

[0114] Status reporting: the real-time risk calculation engine 650 regularly reports the operating status, so that the risk engine management 640 can grasp the operating status of the real-time risk calculation engine 650 in order to balance resource utilization or recover from failures.

[0115] Message processing is used to communicate with external systems, respond to service requests, and coordinate task execution. The customs clearance risk application 660 sends a risk calculation request message through the message service 620. After the risk real-time calculation engine 650 receives the message, it is parsed by the message processing and triggers the calculation task. The calculation result is also fed back to the requester through the message processing. The task coordination between indicators, strategies, and decision flows is also the responsibility of message processing.

[0116] According to an embodiment of the present disclosure, when the customs clearance risk application 660 needs to evaluate the risk value of an object, it will send a risk evaluation request and its related parameter message of the evaluation object to the message service 620, and then monitor the feedback result of the message service 620.

[0117] According to an embodiment of the present disclosure, after receiving a risk assessment request, the real-time risk calculation performs a risk assessment calculation service through a computing instance. After the calculation is completed, the risk assessment value is sent to the message service 620 to feed back to the risk assessment request end.

[0118] According to an embodiment of the present disclosure, when the risk real-time computing engine 650 evaluates the risk, it will decompose the risk model into multiple different computing instances, and the creation, execution and result feedback of each computing instance on different risk real-time computing engines 650 are also provided through the message service 620.

[0119] According to an embodiment of the present disclosure, the risk real-time computing engine 650 periodically sends the running status message to the message service 620. The risk engine management 640 updates the running status of the risk real-time computing engine 650 after obtaining the relevant message through the message monitoring service. For the computing instance with a fault, it is reallocated to other computing resources. The allocation process is also communicated through the message service 620.

[0120] Figure 7 Schematically shows a flow chart of a risk assessment method according to an embodiment of the present disclosure.

[0121] In operation S710, in response to the received risk assessment request, the data to be assessed in the risk assessment request is input into a configured risk assessment engine.

[0122] In operation S720, a decision flow corresponding to the risk model in the risk assessment engine is used to perform risk assessment on the data to be assessed, and a risk assessment result is output.

[0123] According to the embodiment of the present disclosure, when the customs clearance risk application needs to assess the risk value of the object, it will send the risk assessment request and the relevant parameters of the assessment object, i.e., the data to be assessed, to the risk assessment engine, and then monitor the feedback of the risk assessment results. After receiving the risk assessment request, the risk assessment engine will use the decision flow corresponding to the risk model to perform risk assessment on the data to be assessed and obtain the risk assessment results.

[0124] According to an embodiment of the present disclosure, a decision flow corresponding to a risk model in a risk assessment engine is used to perform risk assessment on the data to be assessed, and a risk assessment result is output, including: determining multiple assessment indicators in the data to be assessed; obtaining corresponding indicator data from business information related to the risk assessment based on the multiple assessment indicators; and performing risk assessment on the indicator data using the assessment strategy in the decision flow to obtain a risk assessment result.

[0125] According to the embodiments of the present disclosure, attribute analysis is performed on the data to be evaluated to determine multiple evaluation indicators. According to the data source corresponding to each evaluation indicator, the remaining indicator data corresponding to the evaluation object in the data to be evaluated is obtained. The data to be evaluated and the indicator data are both input into the risk assessment engine, and the risk assessment is performed using the assessment strategy in the decision flow. Among them, the risk model can be selected according to the type of indicator data to improve the accuracy of the risk assessment.

[0126] Figure 8 The following schematically shows an assessment flow chart applicable to the risk assessment method according to an embodiment of the present disclosure.

[0127] like Figure 8 As shown, the risk assessment mainly includes two systems, one is the risk assessment system 810, and the other is the customs clearance inspection system 820. The following takes this system as an example to describe an applicable assessment process.

[0128] According to the embodiments of the present disclosure, first, when a vehicle or person arrives at the port, the customs will obtain the basic information of the vehicle or person. The customs clearance inspection system submits the basic information of the vehicle or person to the risk assessment system for risk assessment. After receiving the risk assessment request, the real-time risk calculation engine 811 uses the risk indicator information library 812 to obtain relevant indicators and conduct risk assessment. At this time, the basis for the assessment mainly includes historical information, identity information, customs key inspection information, etc. After the risk assessment is completed, the risk assessment results will be fed back to the customs clearance inspection system. After the customs clearance inspection system obtains the risk assessment value, it determines whether inspection is required based on the risk value and the system's own rules and policies. If no inspection is required, the process ends;

[0129] According to the embodiment of the present disclosure, if inspection is required, personnel and equipment are used to inspect the vehicle or personnel. After the inspection, the inspection result of the vehicle or personnel is submitted. After obtaining the inspection result, the basic information of the vehicle or personnel, the current risk value and the inspection result and other data can be submitted to the risk assessment system to perform risk assessment.

[0130] According to an embodiment of the present disclosure, after the risk assessment system 810 completes the assessment, it will feed back the risk assessment results to the customs clearance inspection system 820. After the customs clearance inspection system 820 obtains the risk assessment value, it further determines whether it is necessary to continue the inspection based on the risk value and the system's own rules and policies. If it is not necessary to inspect, the process ends. If inspection is required, continue to the next step, and then repeat this process until inspection is no longer required. Among them, the situation where inspection is not required is that the last step of inspection has been reached, for example: manual inspection, or the risk assessment value is lower than the threshold. After the inspection is completed, the inspection results need to be fed back to the risk data indicator library 812, which can be used to improve the information library and can also be used to improve the risk assessment process.

[0131] According to the embodiments of the present disclosure, risk assessment can be requested multiple times, and different risk models can be requested each time. Even if the same risk model is used, the same result may not be returned each time, because the data is always changing and there will be some random values ​​in the risk model. The risk indicator information library is a general term, which does not mean that there is only one indicator information library, and any valid data source can be added or subtracted.

[0132] According to the embodiments of the present disclosure, the customs clearance inspection system obtains the risk assessment results of the assessment object from the risk assessment system by sending a risk assessment request, so as to determine the inspection plan during customs clearance, rationally utilize scarce inspection resources such as inspection equipment and personnel, and improve customs clearance efficiency.

[0133] Fig. 9 The structure block diagram of the engine configuration device according to the embodiment of the present disclosure is schematically shown.

[0134] like Fig. 9 As shown, the engine configuration device 900 includes a model building module 910 , a decision flow generation module 920 and an engine configuration module 930 .

[0135] Model building module 910 is used to build the risk model in the risk assessment engine based on business information related to risk assessment and a rule engine integrated with different decision-making tools.

[0136] The decision flow generation module 920 is used to integrate multiple indicator items defined in the risk model and the calculation rules for each indicator item in the decision tool according to the business logic in the business information, and generate a decision flow corresponding to the risk model, wherein the decision flow includes multiple decision nodes, and each decision node corresponds to an evaluation strategy determined according to the indicator items and the calculation rules.

[0137] The engine configuration module 930 is used to configure the decision flow into the risk assessment engine so as to perform risk assessment using the configured risk assessment engine.

[0138] According to an embodiment of the present disclosure, the decision flow generation module 920 includes a node definition submodule, a policy generation submodule and a policy flow generation submodule.

[0139] The node definition submodule is used to define multiple process nodes, wherein the multiple process nodes include a start node, a decision node and an end node.

[0140] The strategy generation submodule is used to integrate the indicator items with multiple calculation rules to obtain multiple evaluation strategies.

[0141] The strategy flow generation submodule is used to configure multiple evaluation strategies into corresponding decision nodes according to business logic, so as to orchestrate multiple process nodes and generate decision flows.

[0142] According to an embodiment of the present disclosure, the policy generation submodule includes a threshold determination unit and a policy generation unit.

[0143] The threshold determination unit is used to determine the indicator threshold corresponding to each indicator item.

[0144] The strategy generation unit is used to integrate multiple indicator thresholds according to the calculation logic in each calculation rule and the association relationship between multiple indicator items to obtain multiple evaluation strategies.

[0145] According to an embodiment of the present disclosure, the strategy flow generation submodule includes a node orchestration unit and a decision flow generation unit.

[0146] The node orchestration unit is used to orchestrate the decision nodes corresponding to different calculation rules based on business logic to obtain multiple decision branches, wherein the multiple decision branches can be calculated in parallel when performing risk assessment.

[0147] The decision flow generation unit is used to connect multiple decision branches with the start node and the corresponding end node respectively to obtain a decision flow.

[0148] According to an embodiment of the present disclosure, the engine configuration device 900 further includes a source determination module and a source configuration module.

[0149] The source determination module is used to determine the data source of each indicator item in the business information, wherein the data source is the connection information between the application system, and the application system is used to provide business information.

[0150] The source configuration module is used to configure the data source in the risk assessment engine to obtain the data sent by the application system based on the data source information.

[0151] According to an embodiment of the present disclosure, the engine configuration device 900 further includes a processing index configuration module and a calculation index configuration module.

[0152] The processing indicator configuration module is used to configure multiple data processing indicators in the risk assessment engine, where the data processing indicators are used to obtain the business data required for risk assessment. The business data includes database information and service interface information and service framework information used when performing risk assessment.

[0153] The calculation indicator configuration module is used to configure multiple data calculation indicators in the risk assessment engine according to the calculation logic in the calculation rules, wherein the data calculation indicators are used to operate on the results calculated by the assessment strategy when performing risk assessment.

[0154] Fig.10 The structure block diagram of the risk assessment device according to the embodiment of the present disclosure is schematically shown.

[0155] like Fig.10 As shown, the risk assessment device 1000 includes a request output module 1010 and a risk assessment module 1020 .

[0156] The request output module 1010 is used to input the to-be-assessed data in the risk assessment request into the configured risk assessment engine in response to the received risk assessment request.

[0157] The risk assessment module 1020 is used to perform risk assessment on the above-mentioned data to be assessed by utilizing the decision flow corresponding to the risk model in the above-mentioned risk assessment engine, and output the risk assessment result.

[0158] According to an embodiment of the present disclosure, the risk assessment module 1020 further includes an indicator determination submodule, a data acquisition submodule and a risk assessment submodule.

[0159] The indicator determination submodule is used to determine multiple evaluation indicators in the data to be evaluated.

[0160] The data acquisition submodule is used to obtain corresponding indicator data from business information related to risk assessment based on multiple evaluation indicators.

[0161] The risk assessment submodule is used to perform risk assessment on indicator data using the assessment strategy in the decision flow to obtain risk assessment results.

[0162] According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits, or at least part of the functions of any one of them can be implemented in one module. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be split into multiple modules for implementation. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be at least partially implemented as hardware circuits, such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems on chips, systems on substrates, systems on packages, application specific integrated circuits (ASICs), or can be implemented by hardware or firmware in any other reasonable way of integrating or packaging circuits, or can be implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, according to the embodiments of the present invention, one or more of the modules, submodules, units, and subunits can be at least partially implemented as computer program modules, and when the computer program modules are run, the corresponding functions can be performed.

[0163] For example, any of the model building module 910, the decision flow generation module 920, the engine configuration module 930, the request output module 1010, and the risk assessment module 1020 can be combined into one module / unit / sub-unit for implementation, or any of the modules / units / sub-units can be split into multiple modules / units / sub-units. Alternatively, at least part of the functions of one or more of these modules / units / sub-units can be combined with at least part of the functions of other modules / units / sub-units and implemented in one module / unit / sub-unit. According to an embodiment of the present disclosure, at least one of the model building module 910, the decision flow generation module 920 and the engine configuration module 930, the request output module 1010 and the risk assessment module 1020 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in an appropriate combination of any of them. Alternatively, at least one of the model building module 910, the decision flow generation module 920 and the engine configuration module 930, the request output module 1010 and the risk assessment module 1020 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be performed.

[0164] It should be noted that the engine configuration device and risk assessment device parts in the embodiments of the present disclosure correspond to the engine configuration method and risk assessment method parts in the embodiments of the present disclosure. The description of the engine configuration device and risk assessment device parts specifically refers to the engine configuration method and risk assessment method parts, which will not be repeated here.

[0165] Fig.11 A block diagram of an electronic device suitable for implementing an engine configuration method and a risk assessment method according to an embodiment of the present disclosure is schematically shown. Fig.11 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0166] like Fig.11As shown, the electronic device 1100 according to an embodiment of the present disclosure includes a processor 1101, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1102 or a program loaded from a storage part 1108 to a random access memory (RAM) 1103. The processor 1101 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1101 may also include an onboard memory for caching purposes. The processor 1101 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0167] In RAM 1103, various programs and data required for the operation of electronic device 1100 are stored. Processor 1101, ROM 1102 and RAM 1103 are connected to each other through bus 1104. Processor 1101 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 1102 and / or RAM 1103. It should be noted that the program can also be stored in one or more memories other than ROM 1102 and RAM 1103. Processor 1101 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0168] According to an embodiment of the present disclosure, the electronic device 1100 may further include an input / output (I / O) interface 1105, which is also connected to the bus 1104. The electronic device 1100 may further include one or more of the following components connected to the input / output (I / O) interface 1105: an input portion 1106 including a keyboard, a mouse, etc.; an output portion 1107 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 1108 including a hard disk, etc.; and a communication portion 1109 including a network interface card such as a LAN card, a modem, etc. The communication portion 1109 performs communication processing via a network such as the Internet. A drive 1110 is also connected to the input / output (I / O) interface 1105 as needed. A removable medium 1111, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1110 as needed, so that a computer program read therefrom is installed into the storage portion 1108 as needed.

[0169] According to an embodiment of the present disclosure, the method flow according to an embodiment of the present disclosure can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1109, and / or installed from the removable medium 1111. When the computer program is executed by the processor 1101, the above-mentioned functions defined in the system of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0170] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0171] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include, but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0172] For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 1102 and / or the RAM 1103 described above and / or one or more memories other than the ROM 1102 and the RAM 1103 .

[0173] An embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method provided by the embodiment of the present disclosure. When the computer program product runs on an electronic device, the program code is used to enable the electronic device to implement the engine configuration method and risk assessment method provided by the embodiment of the present disclosure.

[0174] When the computer program is executed by the processor 1101, the above functions defined in the system / device of the embodiment of the present disclosure are executed. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0175] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 1109, and / or installed from a removable medium 1111. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0176] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0177] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions. It can be understood by those skilled in the art that the features recorded in the various embodiments of the present disclosure can be combined and / or combined in a variety of ways, even if such a combination or combination is not explicitly recorded in the present disclosure. In particular, without departing from the spirit and teaching of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0178] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. An engine configuration method, comprising: Construct the risk model in the risk assessment engine based on business information related to risk assessment and a rule engine integrated with different decision-making tools; According to the business logic in the business information, multiple indicator items defined in the risk model and the calculation rules for each of the indicator items in the decision tool are integrated to generate a decision flow corresponding to the risk model, wherein the decision flow includes multiple decision nodes, and each of the decision nodes corresponds to an evaluation strategy determined according to the indicator items and the calculation rules; The decision flow is configured into the risk assessment engine to perform risk assessment using the configured risk assessment engine.

2. The method according to claim 1, wherein: The step of integrating the multiple indicator items defined in the risk model and the calculation rules for each of the indicator items in the decision tool according to the business logic in the business information to generate a decision flow corresponding to the risk model includes: Defining a plurality of process nodes, wherein the plurality of process nodes include a start node, the decision node and an end node; Integrate the indicator item with the plurality of calculation rules respectively to obtain a plurality of evaluation strategies; The plurality of evaluation strategies are configured into corresponding decision nodes according to the business logic, so as to orchestrate the plurality of process nodes and generate the decision flow.

3. The method according to claim 2, wherein: The indicator items are respectively integrated with the plurality of calculation rules to obtain a plurality of evaluation strategies, including: Determine an indicator threshold corresponding to each of the indicator items; According to the calculation logic in each of the calculation rules and the association relationship between the multiple indicator items, the multiple indicator thresholds are integrated to obtain the multiple evaluation strategies.

4. The method according to claim 2, wherein: The orchestrating of the plurality of process nodes to generate the decision flow includes: Based on the business logic, the decision nodes corresponding to the different calculation rules are arranged to obtain multiple decision branches, wherein the multiple decision branches can be calculated in parallel when performing risk assessment; The plurality of decision branches are respectively connected to the start node and the corresponding end node to obtain the decision flow.

5. The method according to claim 1, further comprising: Determine the data source of each indicator item in the business information, wherein the data source is the connection information between the application system and the application system, and the application system is used to provide the business information; The data source is configured in the risk assessment engine to obtain the data sent by the application system based on the data source information.

6. The method according to claim 1, further comprising: A plurality of data processing indicators are configured in the risk assessment engine, wherein the data processing indicators are used to obtain business data required for risk assessment, wherein the business data includes database information and service interface information and service framework information used in risk assessment; According to the calculation logic in the calculation rules, a plurality of data calculation indicators are configured in the risk assessment engine, wherein the data calculation indicators are used to operate on the results calculated by the assessment strategy when performing risk assessment.

7. A risk assessment method comprising: In response to the received risk assessment request, inputting the data to be assessed in the risk assessment request into the configured risk assessment engine according to any one of claims 1 to 6; The decision flow corresponding to the risk model in the risk assessment engine is used to perform risk assessment on the data to be assessed and output a risk assessment result.

8. The method according to claim 7, wherein: The step of utilizing the decision flow corresponding to the risk model in the risk assessment engine to perform risk assessment on the data to be assessed and outputting the risk assessment result includes: Determining multiple evaluation indicators in the data to be evaluated; According to the plurality of evaluation indicators, corresponding indicator data is obtained from business information related to risk evaluation; The risk assessment is performed on the indicator data using the assessment strategy in the decision flow to obtain the risk assessment result.

9. An engine configuration device, comprising: Model building module, which is used to build the risk model in the risk assessment engine based on business information related to risk assessment and a rule engine integrated with different decision-making tools; A decision flow generation module, used to integrate the multiple indicator items defined in the risk model and the calculation rules for each of the indicator items in the decision tool according to the business logic in the business information, and generate a decision flow corresponding to the risk model, wherein the decision flow includes multiple decision nodes, and each decision node corresponds to an evaluation strategy determined according to the indicator items and the calculation rules; as well as The engine configuration module is used to configure the decision flow into the risk assessment engine so as to perform risk assessment using the configured risk assessment engine.

10. An electronic device comprising: one or more processors; a memory for storing one or more programs, Wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 6 or claim 7 or claim 8.

11. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the method of any one of claims 1 to 6 or claim 7 or claim 8.

12. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6 or claim 7 or claim 8.

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