Rule-based data processing method and device, electronic equipment and program product

Abstract

The invention discloses a rule-based data processing method, which comprises the following steps: extracting data from a plurality of data sources based on preset data source configuration, the data source configuration comprising connection parameters and access protocols of the data sources; mapping the extracted data to a preset feature variable, so that a corresponding relation is established between the feature variable and a data item of the extracted data; creating a plurality of rules through the rule configuration template, and constructing a rule flow tree by using the plurality of rules; performing rule processing on the data according to the rule flow tree to obtain a rule flow tree processing result; and generating data processing decision information according to a rule flow tree processing result. The invention further provides a related device, electronic equipment and a computer program product. According to the method disclosed by the invention, the problem that data acquisition and rule execution are separated in the prior art is solved, and the condition relationship and the execution sequence of a plurality of rules are defined by introducing the rule flow tree, so that complex business logic requirements can be better met.

Description

Technical Field

[0001] The present disclosure relates to the field of computer application technology, and in particular to a rule-based data processing method and device, an electronic device, and a program product. Background Art

[0002] Currently, in order to implement complex business logic, it is usually necessary to configure a large number of rules to process business data. As the number of rules increases, the difficulty of rule configuration and data processing is also increasing.

[0003] In response to this, the industry has proposed to use a rule process tree to orchestrate business rules and implement complex business logic by defining the conditional relationships and execution order between multiple rules.

[0004] However, in the existing technology, rule arrangement and the acquisition of business data by rules are separated from each other. Although the rule process tree can define the execution order between rules, there is a lack of a unified management mechanism for the acquisition of data required for rule execution. It is even necessary to wait for the rule arrangement to be completed before configuring the rules one by one. The process is cumbersome and inefficient. In actual business scenarios, different rules may need to extract the required data from multiple heterogeneous data sources, while existing solutions usually only support a single type of data source, which is difficult to meet the needs of complex businesses.

[0005] Therefore, there is an urgent need for an efficient data processing solution to meet the data processing requirements in complex business logic in complex business scenarios.

[0006] The content of this background technology description is only for facilitating understanding of the relevant technology in this field and is not regarded as an admission of the prior art. Summary of the invention

[0007] The embodiments of the present disclosure aim to provide a rule-based data processing method and apparatus, an electronic device, and a program product, which can alleviate or solve at least one of the technical problems mentioned above.

[0008] In a first aspect, an embodiment of the present disclosure provides a rule-based data processing method, which may include:

[0009] Extracting data from multiple data sources based on a preset data source configuration, wherein the data source configuration includes connection parameters and access protocols of the data sources;

[0010] Mapping the extracted data to preset feature variables so that a corresponding relationship is established between the feature variables and the data items of the extracted data;

[0011] One or more rules are created through a rule configuration template, and a rule process tree is constructed using the one or more rules, wherein the rule defines a conditional judgment logic based on a feature variable, and the rule process tree includes a start node, one or more rule nodes, and one or more control nodes, wherein the rule nodes correspond to the one or more rules respectively, and the connection relationship between the control node and the one or more rule nodes defines the conditional relationship and execution order of the one or more rules;

[0012] Execute rule processing on the data according to the rule process tree to obtain a rule process tree processing result;

[0013] Generate data processing decision information based on the processing results of the rule process tree.

[0014] Optionally, performing rule processing on the data according to the rule process tree to obtain a rule process tree processing result includes:

[0015] Starting from the start node of the rule flow tree, executing the rule nodes according to the structure of the rule flow tree; and

[0016] The rule execution result of the last executed rule node is used as the rule process tree processing result;

[0017] Where, for each rule node:

[0018] According to the corresponding relationship, the data item value corresponding to the characteristic variable required by the rule corresponding to the rule node is obtained,

[0019] Use the conditional judgment logic defined by the rule corresponding to the rule node to evaluate the value of the data item to obtain the node execution result,

[0020] According to the node execution result and the configuration properties of the rule node, decide whether to continue executing subsequent nodes or end the process.

[0021] Optionally, executing the rule nodes according to the structure of the rule flow tree includes: identifying parallel execution branches in the rule flow tree, and simultaneously executing the rule nodes in different parallel execution branches, wherein each of the parallel execution branches includes one or more of the rule nodes;

[0022] The method of taking the rule execution result of the last executed rule node as the rule flow tree processing result includes: aggregating the node execution results of the parallel execution branches to obtain a comprehensive rule flow tree processing result.

[0023] Optionally, mapping the extracted data to preset feature variables so that a corresponding relationship is established between the feature variables and data items of the extracted data includes:

[0024] Identifying data items in the extracted data that can be used for feature variable mapping;

[0025] A one-to-one correspondence is established between each of the characteristic variables and one of the data items.

[0026] Optionally, the creating one or more rules through a rule configuration template includes:

[0027] Selecting one or more of the feature variables in the rule configuration template;

[0028] Arithmetic operation conditions are set for the selected characteristic variables, and / or logical operation conditions are set for the selected characteristic variables.

[0029] Optionally, the creating one or more rules through a rule configuration template includes:

[0030] Providing multiple business rule templates for multiple businesses, the business rule templates include a basic configuration and an extended configuration, wherein the basic configuration includes a preset atomic rule library and a preset rule set library, the rule set library includes a rule set formed by combining multiple atomic rules, and the extended configuration includes a rule derivation logic for customizing new rules and a rule combination logic for combining existing rules;

[0031] Obtaining demand description information of a preset service, and generating adjustment suggestions for rules and execution logic corresponding to the preset service according to the demand description information of the preset service;

[0032] According to the rules corresponding to the preset business and the adjustment suggestions of the execution logic, the business rule template corresponding to the preset business is adjusted.

[0033] Optionally, the multiple businesses are multiple risk control businesses.

[0034] The obtaining of the requirement description information of the preset service, and determining the rules and execution logic corresponding to the preset service according to the requirement description information of the preset service, includes:

[0035] Parse the demand description information and determine the rule adjustment type, wherein the rule adjustment type includes: risk control rule recommendation, risk control rule replacement, risk control rule combination and risk control rule order adjustment, and the risk control rule includes at least one of anti-fraud rules, credit assessment rules, transaction limit rules and loan limit rules.

[0036] The risk control rules include: anti-fraud rules, credit assessment rules, and transaction or loan limit rules.

[0037] In a second aspect, an embodiment of the present disclosure provides a rule-based data processing method and apparatus, which may include:

[0038] An extraction unit configured to extract data from a plurality of data sources based on a preset data source configuration, wherein the data source configuration includes connection parameters and access protocols of the data sources;

[0039] a mapping unit configured to map the extracted data to a preset feature variable so that a corresponding relationship is established between the feature variable and a data item of the extracted data;

[0040] A creation unit, configured to create one or more rules through a rule configuration template, and use the one or more rules to construct a rule process tree, wherein the rule defines a conditional judgment logic based on a feature variable, and the rule process tree includes a start node, one or more rule nodes, and one or more control nodes, wherein the rule nodes correspond to the one or more rules respectively, and the connection relationship between the control node and the one or more rule nodes defines the conditional relationship and execution order of the one or more rules;

[0041] A processing unit, configured to perform rule processing on the data according to the rule process tree to obtain a rule process tree processing result;

[0042] The generating unit is configured to generate data processing decision information according to the processing result of the rule process tree.

[0043] In a third aspect, an embodiment of the present disclosure provides an electronic device, which may include: a processor and a memory storing a computer program, wherein the processor is configured to implement the method described in the first aspect when running the computer program.

[0044] In a fourth aspect, an embodiment of the present disclosure provides a program product, including a computer program, wherein when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0045] The rule-based data processing method provided by the embodiment of the present disclosure extracts data from multiple data sources based on a preset data source configuration, maps the extracted data to preset feature variables, performs rule processing on the feature variables based on the rule configuration and the rule process tree, and generates data processing decision information according to the rule processing results. The feature variables are used as a bridge between the data source and the rules, thereby achieving an organic combination of data acquisition and rule processing, overcoming the problem of separation between data acquisition and rule execution in the prior art, and introducing a rule process tree to define the conditional relationship and execution order of multiple rules, which can better adapt to complex business logic requirements.

[0046] Other optional features and technical effects of the embodiments of the present disclosure are partially described below, and partially can be understood by reading this document. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. The elements shown are not limited by the proportions shown in the accompanying drawings. The same or similar reference numerals in the accompanying drawings represent the same or similar elements, wherein:

[0048] Figure 1 One of the exemplary flow charts of the rule-based data processing method according to an embodiment of the present disclosure is shown;

[0049] Figure 2 A second exemplary flow chart of a rule-based data processing method according to an embodiment of the present disclosure is shown;

[0050] Figure 3 A third exemplary flowchart of a rule-based data processing method according to an embodiment of the present disclosure is shown;

[0051] Figure 4 A fourth exemplary flow chart of a rule-based data processing method according to an embodiment of the present disclosure is shown;

[0052] Figure 5 An exemplary structural diagram showing a rule-based data processing apparatus according to an embodiment of the present disclosure; and

[0053] Figure 6 An exemplary structural diagram of an electronic device capable of implementing the method according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present disclosure more clear, the present disclosure is further described in detail below in conjunction with specific implementation methods and drawings. Here, the illustrative implementation methods and descriptions of the present disclosure are used to explain the present disclosure, but are not intended to limit the present disclosure.

[0055] As used herein, the term "including" and its variations mean open inclusion, i.e., "including but not limited to". Unless otherwise stated, the term "or" means "and / or". The term "based on" means "based at least in part on". The terms "an example embodiment" and "an embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one some embodiments". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0056] The information, data, and signals involved in the embodiments of the present disclosure are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data comply with the relevant laws, regulations, and standards of relevant countries and regions.

[0057] In some embodiments of the present disclosure, Figure 1As shown, a rule-based data processing method is provided, which can be used for including but not limited to a business decision engine, a business rule processing system, and a risk control evaluation system. In some scenarios, the present disclosure is applied to an electronic device deployed with a business decision engine. The method provided in some embodiments of the present disclosure may include at least S110 to S150.

[0058] S110: extracting data from multiple data sources based on a preset data source configuration.

[0059] In the embodiments of the present disclosure, the data source includes but is not limited to a relational database, a non-relational database, a distributed database, a third-party HTTP service, and other data sources. In a specific example, the data source is a MySQL relational database. In another specific example, the data source is a MongoDB distributed database.

[0060] In some embodiments of the present disclosure, a corresponding data source configuration may be preset for each data source, and the data source configuration may at least include connection parameters and access protocols of the data source. In this embodiment, the connection parameters include but are not limited to the data source server address, port number, user name, password, minimum number of idle connections in the connection pool, and maximum number of connection pools, etc., and the access protocols include but are not limited to HTTP (Hypertext Transfer Protocol), HTTPS (Hypertext Transfer Protocol Secure), and JDBC (Java Database Connectivity), etc.

[0061] In a specific embodiment of the present disclosure, the data source is a MySQL database, and the connection parameters included in the preset configuration corresponding to the data source are: host:db.example.com, port:3306, database:example_db', username:admin, password:123456, and the access protocol is the JDBC protocol, wherein host is the host address, port is the port number, database is the database name, username is the user name, and password is the password.

[0062] In another specific example of the present disclosure, the data source is an HTTP service data source, and the connection parameters included in the preset configuration corresponding to the data source are: maximumPoolSize: 200, keepAliveDuration: 300000, authToken: tk_abc123def456, Key: sk_9d8e7f6g5h, and the access protocol is the HTTP protocol, wherein maximumPoolSize is the maximum connection pool size, keepAliveDuration is the connection retention time (ms), authToken is the authentication token, and Key is the key.

[0063] In the embodiments of the present disclosure, when data needs to be obtained from a data source, a connection with the data source can be established according to connection parameters in a preset data source configuration, and a database can be accessed according to a protocol in the data source configuration to achieve data extraction.

[0064] It should be understood that more types of data source configurations may be preset in the embodiments of the present disclosure, and the present disclosure does not limit the specific data source types and configuration parameters.

[0065] In some embodiments of the present disclosure, the data source configuration may further include data extraction instructions corresponding to the data source, and the data extraction instructions may define how to obtain specified data from different types of data sources connected to it. In this embodiment, with reference to the aforementioned specific embodiments, the data extraction instructions include but are not limited to MySQL data extraction instructions, HTTP data extraction instructions, etc.

[0066] In some embodiments of the present disclosure, a data source may include one or more types of data, and one or more data extraction instructions may be configured for the multiple types of data in the data source.

[0067] In this embodiment, the data extraction instruction may include but is not limited to parameters such as query type, query condition, estimated execution time, query mode, query number limit, and fields to be extracted. It should be understood that those skilled in the art can reasonably configure specific data extraction instructions according to their own needs, and the specific structure of the data extraction instruction disclosed in this disclosure is not limited.

[0068] In a specific embodiment, the data source is an HTTP database, and the extraction instruction 1 included in the data source configuration is {"url":"https: / / api.example.com / score","method":"POST","params":{"userId":"1234"}","queryType":"full"},"headers":{"Authorization":"Bearer tk_abc123def456"},"timeout":5000}, wherein url is the request address, method is the request method, params is the request parameter, userId is the user identifier, queryType is the query type, headers is the request header information, and timeout is the timeout period.

[0069] In another specific embodiment, the data source is a MySQL database, and the data extraction instruction 2 included in the data source configuration is: {"expectedTime":100,"queryType":"MULTIPLE","dataSource":"mysql_user_db","tableName":"user_info","condition":"age>#{minAge}AND city=#{cityName}","limit":1000,"fields":["id","name","age","city"]}, wherein expectedTime is the expected execution time, queryType is the query method, dataSource is the data source name, tableName is the table name, condition is the query condition, limit is the number of query items, and fields is a list of field names.

[0070] S120: Mapping the extracted data to preset feature variables, so that a corresponding relationship is established between the feature variables and the data items of the extracted data.

[0071] In the embodiment of the present disclosure, the characteristic variables may include a variety of preset business parameters. In one example, when performing a transaction risk assessment business, the characteristic variables may include user identification, transaction information, and historical behavior data. In another example, when performing a pricing assessment business, the characteristic variables may include height, weight, age, etc. In the embodiment of the present disclosure, the value of the characteristic variable may be obtained from the data source through a corresponding data extraction instruction, such as the data extraction instruction described in S110 above.

[0072] In an embodiment of the present disclosure, the mapping includes a process of associating a data item in the extracted data obtained by the data extraction instruction with a preset feature variable, wherein the extracted data may include an original data set obtained from a data source, and the data item includes a specific data field in the original data set. In this embodiment, the mapping can establish a corresponding relationship between the preset feature variable and the data item of the extracted data, and the corresponding relationship defines that the value of each feature variable can be obtained from a certain data item obtained by the data extraction instruction.

[0073] In some embodiments of the present disclosure, Figure 2 As shown, mapping the extracted data to preset feature variables so that a corresponding relationship is established between the feature variables and the data items of the extracted data may at least include S121 and S122.

[0074] S121: Identify data items in the extracted data that can be used for feature variable mapping.

[0075] In some embodiments of the present disclosure, data items that can be used to establish a mapping with preset feature variables can be identified in the data extracted by the data extraction instruction.

[0076] In a specific example, the preset feature variables include age and education level. The data "{"name":"A","age":25,"education":"bachelor's degree","household":"Beijing"}" can be obtained from the data extraction instruction "user_basic_info" query to identify and filter out the data item "age" that can be used to establish a mapping with the feature variable "age", and the data item "education" that can be used to establish a mapping with the feature variable "education level".

[0077] S122: Establish a one-to-one correspondence between each feature variable and a data item.

[0078] In some embodiments of the present disclosure, the correspondence between the feature variable and the data item of the extracted data can be stored in a local memory or a remote server in the form of a feature variable configuration. In one example, the feature variable configuration may include but is not limited to the feature variable name, the corresponding extraction instruction, and the corresponding data item.

[0079] In a specific example, referring to the specific embodiment in the aforementioned S121, the feature variable configuration A of the feature variable "age" is: "{"feature variable":"age","data source":{"data extraction instruction":"user_basic_info","data item":"age"}}". In this embodiment, when it is necessary to obtain the value of the feature variable "age", first determine the corresponding data extraction instruction "user_basic_info" according to the feature variable configuration A, then query and obtain the data item set from the data source according to the data extraction instruction, and finally locate the data item "age" from the data item set according to the feature variable configuration A, and assign the value of the data item "age" to the feature variable "age".

[0080] It should be understood that in the embodiments of the present disclosure, a certain feature variable may correspond to multiple feature variable configurations to achieve acquisition of data items from multiple data sources, and the present disclosure does not impose any limitation on this.

[0081] In the above embodiments of the present disclosure, the configuration and mapping steps of the feature variables of the embodiments of the present disclosure are described. The method of the embodiments of the present disclosure realizes the decoupling of the feature variables from the data source, and through the flexible feature variable configuration, it is possible to conveniently obtain the required feature variable values ​​from the parameters of multiple different data items.

[0082] In some embodiments of the present disclosure, the above-mentioned characteristic variables can be used as basic parameters for rule judgment, thereby constructing the conditional judgment logic of the business rule. As an explanation but not limitation, each business rule implements its business function by setting rule judgment conditions for one or more characteristic variables.

[0083] In some embodiments of the present disclosure, the rules may define conditional judgment logic based on feature variables, and any rule may include but is not limited to: input parameters (e.g., one or more feature variables), judgment logic (e.g., operations and judgments performed on feature variables), and output (e.g., pass / reject). In this embodiment, the conditional judgment logic includes but is not limited to arithmetic operations and logical operations performed on feature variables, etc., which will be described in detail below.

[0084] S130: Create one or more rules through a rule configuration template, and use the one or more rules to build a rule process tree.

[0085] In an embodiment of the present disclosure, a rule configuration template may be provided, and the rule configuration template may define basic elements required to create a rule.

[0086] In some embodiments of the present disclosure, the rule configuration template may include at least one of the following rule configurations: a rule identifier, an input parameter list, a conditional judgment logic expression, and an output result. In this embodiment, the rule identifier may be used to uniquely identify each rule, the input parameter list may be used to specify the feature variables required by the rule in the preset feature variables, the conditional judgment logic expression may be used to define the judgment logic of the rule, and the output result may include the judgment result (pass / reject) when the rule condition is met or not met and the output result value, etc., and the present disclosure does not limit this.

[0087] In some embodiments of the present disclosure, Figure 3 As shown, the step of creating one or more rules through a rule configuration template may at least include S131 and S132.

[0088] S131: Select one or more feature variables in the rule configuration template.

[0089] In the embodiment of the present disclosure, the rule configuration template may include a preset parameter list, and the preset parameter list may include one or more preset feature variables. In this embodiment, one or more preset feature variables may be selected in the rule configuration template to establish a new rule.

[0090] In some embodiments of the present disclosure, the characteristic variables in the preset parameter list are, for example, the mapped characteristic variables obtained in the aforementioned S120.

[0091] In a specific example of the present disclosure, in the rule configuration template, you can select user information feature variables such as "age" and "occupation" to construct access rules, select transaction information feature variables such as "transaction amount" and "transaction frequency" to construct transaction limit rules, select historical behavior feature variables such as "historical number of defaults" and "number of overdue days" to construct credit assessment rules, etc.

[0092] S132: Setting arithmetic operation conditions for the selected characteristic variables, and / or setting logical operation conditions for the selected characteristic variables.

[0093] In some embodiments of the present disclosure, as described above, the conditional judgment logic includes but is not limited to arithmetic operation conditions and logical operation conditions performed on feature variables.

[0094] In some embodiments of the present disclosure, the arithmetic operation conditions include but are not limited to comparison operations and range operations. In one example, the comparison operation may include: * greater than (>), * less than (<), * equal to (=), * greater than or equal to (>=), * less than or equal to (<=), * not equal to (!=), etc., and the range operation may include: * interval range (between...) and * set range (belongs to, does not belong to a certain set), etc. In other embodiments of the present disclosure, the logical operation conditions include but are not limited to: AND, OR, and NOT, etc.

[0095] In a specific example of the present disclosure, the conditional judgment logic of the "Work Experience Rule" setting is: "{"Operator":"AND","Condition":[{"Feature Variable":"Age","Operator":">","Threshold":18},{"Feature Variable":"Years of Work","Operator":">=","Reference Value":"3"}]}". It should be understood that the above embodiment only shows the case of selecting to set logical operation conditions. In other embodiments, it is also possible to select to set arithmetic operation conditions or to set arithmetic operation conditions and logical operation conditions at the same time. The present disclosure does not limit this.

[0096] In the embodiment of the present disclosure, multiple rules may be used to construct multiple rule nodes, and the multiple rule nodes may form a rule process tree. In this embodiment, the multiple rule nodes may correspond to multiple rules respectively, wherein the multiple rules are, for example, the multiple rules created by the rule configuration template in the aforementioned S130.

[0097] In some embodiments of the present disclosure, the rule flow tree may include multiple nodes, including but not limited to a start node, multiple rule nodes, and multiple control nodes. In this embodiment, the start node may be used to identify the entry position of the rule flow, and any rule flow tree may include a start node; the rule node may be used to encapsulate specific business rules; and the control node may be used to control the execution path of the rule flow.

[0098] In the embodiment of the present disclosure, different connection relationships may exist between the multiple nodes, and the connection relationship between the control node and the multiple rule nodes may define the conditional relationship and execution order of the multiple rules.

[0099] In some embodiments of the present disclosure, the connection relationship between the rule nodes may include a serial execution relationship. In one example, there is a directed connection "rule node 1→rule node 2" between rule node 1 (corresponding to rule 1) and rule node 2 (corresponding to rule 2), thereby determining that the execution order of rule 1 and rule 2 is "rule 1→rule 2". In another example, there is a directed connection between control node 3 and rule node 4 (corresponding to rule 4) and rule node 5 (corresponding to rule 5): rule node 4→control node 3, control node 3→rule node 5, thereby, control node 3 can control the conditional relationship between rule 4 and rule 5, for example, control node 3 can realize that rule 5 continues to be executed only when the output result of rule node 4 is rejection.

[0100] In some other embodiments of the present disclosure, the control node may include but is not limited to branch nodes, parallel nodes, merge nodes, etc., wherein the branch node can be used to implement conditional branch control, the parallel node is used to implement parallel execution control, and the merge node can be used to summarize the processing results of multiple branches executed in parallel.

[0101] In one example, there are directed connections between branch node 6 and rule node 7 (corresponding to rule 7), rule node 8 (corresponding to rule 8), and rule node 9 (corresponding to rule 9): rule node 7→branch node 6, control node 6→rule node 8, branch node 6→rule node 9, and control node 3 can determine which one of rule 8 or rule 9 to selectively execute according to the output result of rule node 7. In another example, there are directed connections between parallel node 10 and rule node 11 (corresponding to rule 11), and rule node 12 (corresponding to rule 12): parallel node 10→rule node 11, parallel node 10→rule node 12, and parallel node 10 can control whether to execute rule 8 and rule 9 in parallel.

[0102] It should be understood that in the embodiments of the present disclosure, the specific connection relationship between multiple nodes can be expanded or changed according to actual application requirements. Those skilled in the art can select appropriate node types and combinations based on actual conditions, and the present disclosure does not limit this.

[0103] In some embodiments of the present disclosure, the rule configuration template includes preset adjustable rule configuration templates corresponding to different business types, and the creation of one or more rules through the rule configuration template may also include the following C1, C2 and C3.

[0104] C1: Provide multiple business rule templates for multiple businesses, wherein the business rule templates include basic configuration and extended configuration.

[0105] In some embodiments of the present disclosure, the basic configuration may include a preset atomic rule library and a preset rule set library.

[0106] In this embodiment, the atomic rule library can store basic, indivisible atomic rules, which are the most basic judgment rules in the rule system. In one example, the atomic rules include: age judgment rules, asset judgment rules, etc.

[0107] In this embodiment, the rule set library may include a rule set formed by combining multiple atomic rules. In a specific example, the rule set includes, for example, a small loan approval rule set, and the small loan approval rule set includes an age judgment rule, an asset judgment rule, an income-liability ratio judgment rule, and a monthly payment ratio calculation rule. It should be understood that in other embodiments, the rule set may also include more atomic rules, and the present disclosure does not limit the number of atomic rules included in the rule set.

[0108] In some embodiments of the present disclosure, the extended configuration may include rule derivation logic for customizing new rules and rule combination logic for combining existing rules. In this embodiment, the rule derivation logic can be used to support the customization of new rules based on existing rules. In one example, the rule derivation logic includes but is not limited to parameter adjustment (e.g., modifying the judgment threshold of the rule), condition expansion (e.g., supplementing and adding new judgment conditions), and logic change (e.g., modifying the logic judgment symbol in the rule). In this embodiment, the rule combination logic supports flexible combination of existing atomic rules, wherein the combination logic may include but is not limited to serial combination, parallel combination, and conditional combination.

[0109] The method of the embodiment of the present disclosure realizes the customizability and scalability of the rules through a preset business rule template and combines the hierarchy of "basic configuration + extended configuration", thereby improving the flexibility of rule application.

[0110] C2: Obtaining demand description information of a preset service, and generating adjustment suggestions for rules and execution logic corresponding to the preset service according to the demand description information of the preset service.

[0111] In the disclosed embodiment, after providing the business rule template, the demand description information of the specific business scenario can also be obtained. As an explanation but not limitation, there are a large number of rules in the business rule template, but not all of them can be suitable for / meet the needs of specific businesses. For example, loan businesses can include: small loans, large loans, short-term loans, and long-term loans, etc. according to different segments.

[0112] In some embodiments of the present disclosure, the corresponding rules and adjustment suggestions for the execution logic can be generated according to the specific demand description information of the preset business. In some embodiments of the present disclosure, the adjustment suggestions are determined, for example, based on keyword recognition and / or semantic recognition of the specific demand description information. In this embodiment, the adjustment suggestions may include, but are not limited to: the position and priority adjustment of the rules in the rule process tree and the derivation or combination of rules to meet new business needs. In one example, for example, for a credit approval business scenario, according to the demand description: "Strengthen credit review of people in high-risk occupations", the keywords "high-risk occupations" and "strengthen" are identified and combined with semantic understanding, thereby generating adjustment suggestions: "Position adjustment: the position of the previous occupation type judgment rule; rule derivation: based on the existing occupation judgment rules, derive the subdivided occupational risk level rules."

[0113] In some embodiments of the present disclosure, the multiple businesses include multiple risk control businesses. In this embodiment, obtaining the demand description information of the preset business and determining the rules and execution logic corresponding to the preset business based on the demand description information of the preset business may include: C2': parsing the demand description information and determining the rule adjustment type.

[0114] In this embodiment, the rule adjustment type may include: risk control rule recommendation, risk control rule replacement, risk control rule combination and risk control rule order adjustment, etc. In this embodiment, the risk control rule includes at least one of an anti-fraud rule, a credit assessment rule, a transaction limit rule and a loan limit rule.

[0115] The method of the disclosed embodiment further combines the acquisition and analysis of demand description information to realize the identification of rule adjustment requirements, automatically generate rule adjustment suggestions, and improve the efficiency of rule adjustment.

[0116] C3: adjusting the business rule template corresponding to the preset business according to the adjustment suggestion of the rule and execution logic corresponding to the preset business.

[0117] In the embodiment of the present disclosure, adjusting the business rule template corresponding to the preset business may include but is not limited to adjusting the position and / or priority of the rule, adjusting the extension rule, etc.

[0118] In this embodiment, the position of the adjustment rule may include but is not limited to rule pre-position, rule post-position and rule parallelization, etc. The rule pre-position includes adjusting a certain rule to the front-end execution of the rule flow, the rule post-position includes adjusting a certain rule to the back-end execution of the rule flow, the rule parallelization includes adjusting multiple rules that were originally executed in series to be executed in parallel, and the priority of the adjustment rule may include but is not limited to priority increase / priority decrease. In one example, continuing with the above example, corresponding to the adjustment suggestion: "Position adjustment: position of the pre-position of the occupation type judgment rule", the occupation type judgment rule is adjusted from the position after the age judgment rule to the position before the age judgment rule. In another example, corresponding to the adjustment suggestion: "Position adjustment: position of the post-position of the anti-fraud rule", the anti-fraud rule can be adjusted from the position before the asset judgment rule to the position after the asset judgment rule. In another example, corresponding to the adjustment suggestion: "Execution order adjustment: parallelize the occupation type judgment rule and the income-liability ratio judgment rule", the execution order of the originally serial occupation type judgment rule and the income-liability ratio judgment rule is adjusted to parallel execution.

[0119] In this embodiment, the adjustment extension rules may include but are not limited to rule derivation and rule combination, wherein the rule derivation includes refining or extending the judgment conditions of the existing rules to create new independent rules; the rule combination includes combining into a new composite rule through certain combination logic (such as "and", "or", "weighting", etc.). In a specific example, continuing with the above example, corresponding to the adjustment suggestion: "Based on the existing occupational judgment rules, derive subdivided occupational risk level rules", the existing occupational judgment rule set is subjected to rule derivation. The existing occupational judgment rule set includes occupational type judgment rules and working years judgment rules. The original occupational type judgment rules divide occupational types into "manufacturing work, service industry work, and construction industry work". The rule is now derived: on the basis of the original occupational type, the nature of the service industry work is further subdivided into: professional and technical services: assessed as low risk, chain operation services: assessed as medium risk, and liquidity services: assessed as medium risk. In another specific example, corresponding to the adjustment suggestion: "Based on the existing device risk rules, IP address risk rules and login behavior rules, combine new anti-fraud risk rules", the "device risk rules", "IP address risk rules" and "login behavior rules" are connected using "AND" to form a new "anti-fraud risk rule".

[0120] At this time, a new "Industry Risk Characteristics Assessment Rule" is added to the professional judgment rule set to assess the risk characteristics of the applicant's industry, divide the industry into three risk levels: high, medium, and low, and provide additional judgment basis for business decisions.

[0121] The method of the disclosed embodiment can flexibly adjust the rule template according to specific business needs through a flexible business template provision and adjustment mechanism, ultimately achieving dynamic optimization of business rules, improving the efficiency of rule configuration, and providing flexible rule configuration support for different types of business scenarios.

[0122] S140: Execute rule processing on the data according to the rule process tree to obtain the rule process tree processing result.

[0123] In some embodiments of the present disclosure, Figure 4 As shown, the step of performing rule processing on data according to the rule process tree to obtain the rule process tree processing result may at least include S141 to S144.

[0124] S141: Starting from the start node of the rule process tree, executing the rule nodes according to the structure of the rule process tree.

[0125] In some embodiments of the present disclosure, the start node of the rule process tree may be directly connected to one or more rule nodes, thereby forming one or more parallel execution branches.

[0126] In some embodiments of the present disclosure, the start node of the rule process tree may be directly connected to one or more rule nodes, thereby forming one or more parallel execution branches. In this embodiment, when the start node is connected to multiple rule nodes, different branches where the multiple rule nodes are located can be executed in parallel, thereby improving the efficiency of rule execution.

[0127] In some embodiments of the present disclosure, starting from the start node of the rule process tree and executing the rule nodes according to the structure of the rule process tree may include: A1: identifying parallel execution branches in the rule process tree, and executing the rule nodes in different parallel execution branches at the same time. In this embodiment, the execution of the rule nodes of the multiple parallel execution branches does not interfere with each other, and the execution of a certain branch will not affect the execution process of other branches. As an explanation and not a limitation, through the design of parallel execution branches, system resources can be fully utilized and the efficiency of rule execution can be improved, which is particularly suitable for business scenarios containing multiple independent rule judgment logics.

[0128] In some embodiments of the present disclosure, for example, in the above 140, when executing the rule nodes according to the structure of the rule process tree, at least the following B1, B2 and B3 may be executed for each rule node.

[0129] B1: According to the corresponding relationship, obtain the data item value corresponding to the characteristic variable required by the rule corresponding to the rule node.

[0130] In some embodiments of the present disclosure, the multiple rules are, for example, the multiple rules created by the rule configuration template in the aforementioned S130, and the corresponding relationship is, for example, the corresponding relationship between the feature variable established in the aforementioned S122 and the data items that can be used for the feature variable. In one example, the feature variable described in the rule can be determined according to the required feature variables (list) included in the rule, and then the feature variable configuration corresponding to the feature variable is found, and data is extracted from the data source according to the data extraction instruction in the feature variable configuration and the data item value specified in the extracted data is obtained.

[0131] In a specific embodiment of the present disclosure, the input parameter list of the "age rule" node includes the characteristic variable "age", and the characteristic vector configuration A corresponding to the characteristic vector is: "{"characteristic variable":"age","data source":{"extraction instruction":"user_basic_info","data item":"age"}}", and the data "{"name":"A","age":25,"education":"undergraduate","household":"Beijing"}" is queried and obtained from the data source according to configuration A. Subsequently, the data item "age" corresponding to the characteristic variable "age" can be identified and filtered out from these four data items and the data item value 25 can be obtained.

[0132] In other embodiments of the present disclosure, the input parameters of a rule may also include multiple feature vectors, and the data item values ​​corresponding to the multiple feature variables may be obtained respectively. The present disclosure does not limit the number of feature vectors corresponding to a rule.

[0133] B2: Use the conditional judgment logic defined by the rule corresponding to the rule node to evaluate the value of the data item and obtain the node execution result.

[0134] In some embodiments of the present disclosure, the conditional judgment logic of the rule definition corresponding to the rule node can be read, and the data item value corresponding to the acquired feature variable is substituted into the conditional judgment logic, and the judgment logic is executed to obtain the execution result. In this embodiment, the execution result is, for example, the output result of the rule described in S130 above.

[0135] Referring to the specific embodiment in B1 above, in another specific embodiment of the present disclosure, the rule corresponding to the rule node 10 is the "age rule", and the conditional judgment logic and output result of the rule are: "{"conditional judgment":{"feature variable":"age","operator":"greater than or equal to","threshold":18},"output result":"pass / reject"}", and the acquired data item value 25 of the data item "age" is brought into the conditional judgment logic for judgment: 25>18, and the output result is passed, so the execution result of the node is passed.

[0136] B3: Determine whether to continue executing subsequent nodes or end the process based on the node execution result and the configuration properties of the rule node.

[0137] In some embodiments of the present disclosure, the rule node may further include configuration attributes, which may define the corresponding relationship between the rule node and the execution result and the subsequent processing method. In this embodiment, the subsequent processing method includes but is not limited to continuing the process and ending the process. In one example, the subsequent processing method corresponding to the execution result of "pass" may be set to continue the process, and the subsequent processing method corresponding to the execution result of "reject" may be set to end the process.

[0138] S142: The rule execution result of the last executed rule node is used as the rule flow tree processing result.

[0139] In the embodiment of the present disclosure, when there is no more executable rule node in the entire rule process tree from the start node, the process of the entire rule process tree is completed. In the embodiment of the present disclosure, the situation where there is no more executable rule node may include but is not limited to: all rule nodes have been executed or the rule node ends the process according to the configuration attribute.

[0140] In some embodiments of the present disclosure, the rule execution result of the last executed rule node includes but is not limited to "pass" and "reject".

[0141] In other embodiments of the present disclosure, as described above, rule nodes in different parallel execution branches can be executed simultaneously. In this embodiment, starting from the start node of the rule process tree, the rule nodes are executed according to the structure of the rule process tree, and may also include: A2: Summarizing the node execution results of the parallel execution branches to obtain a comprehensive rule process tree processing result.

[0142] In some embodiments of the present disclosure, the processing result of the comprehensive rule flow tree may include the node execution results of the last executed rule nodes of one or more parallel execution branches, and optionally include the node identifiers / rule names of these nodes. In a specific embodiment, the processing result of the comprehensive rule flow tree is "{"annuity rule":"pass","work experience rule":"pass"}".

[0143] In some embodiments of the present disclosure, the comprehensive rule process tree processing result may also include node execution results of all executed rule nodes of one or more parallel execution branches, which will not be repeated here.

[0144] S150: Generate data processing decision information according to the processing result of the rule process tree.

[0145] In the embodiments of the present disclosure, the data processing decision information may include multiple types of decision information, including but not limited to recruitment decision results, risk assessment results, etc. It should be understood that in other embodiments, other decision information may be preset for different specific scenarios, and the present disclosure does not limit this.

[0146] In some embodiments of the present disclosure, the data processing decision information may include multiple decision results, wherein each decision result may correspond to a different rule process tree processing result. In a specific example, the method of the embodiment of the present disclosure is applied to a recruitment decision business, and the decision result corresponding to the rule process tree processing result "pass" is "preliminary screening passed", and the decision result corresponding to the processing result "reject" is "preliminary screening failed". In another specific example, the method of the embodiment of the present disclosure is applied to a risk assessment business, and the decision result corresponding to the rule process tree processing result "pass" is "low transaction risk", and the decision result corresponding to the processing result "reject" is "high transaction risk".

[0147] In some embodiments of the present disclosure, the rule process tree processing result is, for example, the comprehensive rule process tree processing result described in A2 above. In this embodiment, generating data processing decision information according to the rule process tree processing result may also include: generating data processing decision information according to the comprehensive rule process tree processing result and a preset decision strategy.

[0148] In some embodiments of the present disclosure, the preset decision strategies include but are not limited to all pass strategies and any pass strategies. In this embodiment, the all pass strategy requires that when all execution results in the processing result of the comprehensive rule process tree are "passed", the data processing decision information is determined to be "passed", otherwise it is "rejected"; the any pass strategy requires that when the execution result of at least one parallel branch in the processing result of the comprehensive rule process tree is "passed", the data processing decision information is determined to be "passed", otherwise it is "rejected". It should be understood that in other embodiments of the present disclosure, more result summary strategies can be set, and the present disclosure does not limit this.

[0149] The rule-based data processing method provided by the embodiment of the present disclosure extracts data from multiple data sources based on a preset data source configuration, maps the extracted data to preset feature variables, performs rule processing on the feature variables based on the rule configuration and the rule process tree, and generates data processing decision information according to the rule processing results. The feature variables are used as a bridge between the data source and the rules, thereby achieving an organic combination of data acquisition and rule processing, overcoming the problem of separation between data acquisition and rule execution in the prior art, and introducing a rule process tree to define the conditional relationship and execution order of multiple rules, which can better adapt to complex business logic requirements.

[0150] In some embodiments of the present disclosure, Figure 5 As shown, a rule-based data processing device 500 is also provided, which may include: an extraction unit 510, a mapping unit 520, a creation unit 530, a processing unit 540, and a generation unit 550.

[0151] The extraction unit 510 is configured to extract data from multiple data sources based on a preset data source configuration, where the data source configuration includes connection parameters and access protocols of the data sources.

[0152] The mapping unit 520 is configured to map the extracted data to a preset feature variable, so that a corresponding relationship is established between the feature variable and the data item of the extracted data.

[0153] The creation unit 530 is configured to create multiple rules through a rule configuration template, and use multiple rules to build a rule process tree, wherein the rules define conditional judgment logic based on feature variables, and the rule process tree includes a start node, multiple rule nodes and multiple control nodes. The rule nodes correspond to multiple rules respectively, and the connection relationship between the control nodes and the nodes defines the conditional relationship and execution order of the multiple rules.

[0154] The processing unit 540 is configured to perform rule processing on the data according to the rule process tree to obtain a rule process tree processing result.

[0155] The generating unit 550 is configured to generate data processing decision information according to the processing result of the rule flow tree.

[0156] In the disclosed embodiment, Figure 5 The data processing device 500 is schematically shown in the figure, which does not mean that the data processing device 500 only includes Figure 5 Components shown.

[0157] In the embodiment of the present disclosure, the part / unit of the data processing device 500 can implement the functions of the method steps and sub-steps in any of the above embodiments of the present disclosure. At the same time, the method and steps of the method embodiment of the present disclosure can be combined with the part or unit of the device embodiment of the present disclosure without contradiction to obtain a new device embodiment, and vice versa.

[0158] In some embodiments of the present disclosure, Figure 6 As shown, a schematic diagram of an electronic device 600 that can implement the method of the disclosed embodiment is provided, and in some embodiments, more or fewer electronic devices than shown may be included. In some embodiments, it can be implemented using a single or multiple electronic devices. In some embodiments, it can be implemented using cloud or distributed electronic devices.

[0159] like Figure 6As shown, the electronic device 600 includes a processor 601, which can perform various appropriate operations and processes according to the programs and / or data stored in the read-only memory (ROM) 602 or the programs and / or data loaded from the storage part 608 to the random access memory (RAM) 603. CPU 601 can be a multi-core processor, or it can include multiple processors. In some embodiments, the processor 601 can include a general main processor and one or more special coprocessors, such as a graphics processing unit (GPU), a neural network processor (NPU), a digital signal processor (DSP), etc. In RAM 603, various programs and data required for the operation of the electronic device 600 are also stored. CPU 601, ROM 602 and RAM 603 are connected to each other via a bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0160] The processor and the memory are used together to execute the program stored in the memory. When the program is executed by the computer, the steps or functions of the methods described in the above embodiments can be implemented.

[0161] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, a touch screen, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage section 608 as needed. Figure 6 Only some components are schematically shown in the figure, which does not mean that the computer system 600 only includes Figure 6 Components shown.

[0162] The systems, devices, modules or units described in the above embodiments may be implemented by a computer or its associated components. The computer may be, for example, a mobile terminal, a smart phone, a personal computer, a laptop computer, a vehicle-mounted human-computer interaction device, a personal digital assistant, a media player, a navigation device, a game console, a tablet computer, a wearable device, a smart TV, an Internet of Things system, a smart home, an industrial computer, a server or a combination thereof.

[0163] Although not shown, in an embodiment of the present disclosure, a program product is provided, including a computer program, and when the computer program is executed by a processor, the method of any one of the embodiments of the present disclosure is implemented.

[0164] Although not shown, in an embodiment of the present disclosure, a storage medium is provided, the storage medium storing a computer program, the computer program being configured to implement any method of the embodiments of the present disclosure when executed.

[0165] The storage media in the embodiments of the present disclosure include permanent and non-permanent, removable and non-removable items that can be implemented by any method or technology to store information. Examples of storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0166] The methods, programs, systems, devices, etc. of the embodiments of the present disclosure may be executed or implemented in a single or multiple networked computers, or may be practiced in a distributed computing environment. In the embodiments of the present specification, in these distributed computing environments, tasks may be performed by remote processing devices connected via a communication network.

[0167] Those skilled in the art should understand that the embodiments of the present specification may be provided as methods, systems or computer program products. Therefore, those skilled in the art may imagine that the functional modules / units or controllers and related method steps described in the above embodiments may be implemented in software, hardware or a combination of software / hardware.

[0168] Unless explicitly stated, the actions or steps of the methods, programs, and embodiments of the present disclosure do not have to be performed in a specific order and can still achieve the desired results. In some implementations, multitasking and parallel processing are also possible or may be advantageous.

[0169] In this article, multiple embodiments of the present disclosure are described, but for the sake of brevity, the description of each embodiment is not exhaustive, and the same or similar features or parts between the embodiments may be omitted. In this article, "one embodiment", "some embodiments", "example", "specific example", or "some examples" are meant to be applicable to at least one embodiment or example according to the present disclosure, rather than all embodiments. The above terms do not necessarily mean to refer to the same embodiment or example. In the absence of contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples.

[0170] The exemplary systems and methods of the present disclosure have been specifically shown and described with reference to the above embodiments, which are merely examples of the best modes for implementing the present systems and methods. It will be appreciated by those skilled in the art that various changes may be made to the embodiments of the systems and methods described herein when implementing the present systems and / or methods without departing from the spirit and scope of the present disclosure as defined in the appended claims.

Claims

1. A rule-based data processing method, characterized in that: include: Extracting data from multiple data sources based on a preset data source configuration, wherein the data source configuration includes connection parameters and access protocols of the data sources; Mapping the extracted data to preset feature variables so that a corresponding relationship is established between the feature variables and the data items of the extracted data; One or more rules are created through a rule configuration template, and a rule process tree is constructed using the one or more rules, wherein the rule defines a conditional judgment logic based on a feature variable, and the rule process tree includes a start node, one or more rule nodes, and one or more control nodes, wherein the rule nodes correspond to the one or more rules respectively, and the connection relationship between the control node and the one or more rule nodes defines the conditional relationship and execution order of the one or more rules; Execute rule processing on the data according to the rule process tree to obtain a rule process tree processing result; Generate data processing decision information based on the processing results of the rule process tree.

2. The rule-based data processing method according to claim 1, characterized in that: The performing rule processing on the data according to the rule process tree to obtain the rule process tree processing result includes: Starting from the start node of the rule flow tree, executing the rule nodes according to the structure of the rule flow tree; and The rule execution result of the last executed rule node is used as the rule process tree processing result; Where, for each rule node: According to the corresponding relationship, obtaining the data item value corresponding to the characteristic variable required by the rule corresponding to the rule node; Use the conditional judgment logic defined by the rule corresponding to the rule node to evaluate the value of the data item to obtain a node execution result; According to the node execution result and the configuration properties of the rule node, decide whether to continue executing subsequent nodes or end the process.

3. The rule-based data processing method according to claim 2, characterized in that: The executing the rule nodes according to the structure of the rule flow tree comprises: identifying parallel execution branches in the rule flow tree, and simultaneously executing the rule nodes in different parallel execution branches, wherein each of the parallel execution branches comprises one or more rule nodes; The method of taking the rule execution result of the last executed rule node as the rule flow tree processing result includes: aggregating the node execution results of the parallel execution branches to obtain a comprehensive rule flow tree processing result.

4. The rule-based data processing method according to claim 1, characterized in that: The step of mapping the extracted data to a preset feature variable so that a corresponding relationship is established between the feature variable and a data item of the extracted data includes: Identifying data items in the extracted data that can be used for feature variable mapping; A one-to-one correspondence is established between each of the characteristic variables and one of the data items.

5. The rule-based data processing method according to claim 1, characterized in that: The step of creating one or more rules through a rule configuration template includes: Selecting one or more of the feature variables in the rule configuration template; Arithmetic operation conditions are set for the selected characteristic variables, and / or logical operation conditions are set for the selected characteristic variables.

6. The rule-based data processing method according to claim 1, characterized in that: The step of creating one or more rules through a rule configuration template includes: Providing multiple business rule templates for multiple businesses, the business rule templates include a basic configuration and an extended configuration, wherein the basic configuration includes a preset atomic rule library and a preset rule set library, the rule set library includes a rule set formed by combining multiple atomic rules, and the extended configuration includes a rule derivation logic for customizing new rules and a rule combination logic for combining existing rules; Obtaining demand description information of a preset service, and generating adjustment suggestions for rules and execution logic corresponding to the preset service according to the demand description information of the preset service; According to the rules corresponding to the preset business and the adjustment suggestions of the execution logic, the business rule template corresponding to the preset business is adjusted.

7. The rule-based data processing method according to claim 6, characterized in that: The multiple businesses are multiple risk control businesses. The obtaining of the requirement description information of the preset service, and determining the rules and execution logic corresponding to the preset service according to the requirement description information of the preset service, includes: Parse the demand description information and determine the rule adjustment type, wherein the rule adjustment type includes: risk control rule recommendation, risk control rule replacement, risk control rule combination and risk control rule order adjustment, and the risk control rule includes at least one of anti-fraud rules, credit assessment rules, transaction limit rules and loan limit rules.

8. A rule-based data processing method and device, characterized in that: include: An extraction unit configured to extract data from a plurality of data sources based on a preset data source configuration, wherein the data source configuration includes connection parameters and access protocols of the data sources; a mapping unit configured to map the extracted data to a preset feature variable so that a corresponding relationship is established between the feature variable and a data item of the extracted data; A creation unit, configured to create one or more rules through a rule configuration template, and construct a rule process tree using the one or more rules, wherein the rule defines a conditional judgment logic based on a feature variable, and the rule process tree includes a start node, one or more rule nodes, and one or more control nodes, wherein the rule nodes correspond to the one or more rules respectively, and the connection relationship between the control node and the node defines the conditional relationship and execution order of the one or more rules; A processing unit, configured to perform rule processing on the data according to the rule process tree to obtain a rule process tree processing result; The generating unit is configured to generate data processing decision information according to the processing result of the rule process tree.

9. An electronic device, characterized in that: include: A processor and a memory storing a computer program, wherein the processor is configured to implement the method according to any one of claims 1 to 7 when running the computer program.

10. A program product comprising a computer program, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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