Data screening method and device, equipment and medium

Abstract

The invention discloses a data screening method, device, equipment and medium, a data calling request sent by a first calling party after calling a back-end interface is received, the data calling request comprises target rule information of a first marketing activity, the target rule information comprises a rule field and a screening condition, and the first marketing activity is screened according to the rule field and the screening condition. And generating a logic rule expression, calling a rule engine to execute the logic rule expression to obtain screening data corresponding to the logic rule expression, and returning the screening data to the first calling party to enable the first calling party to execute a business process corresponding to the first marketing activity based on the screening data. According to the method, the target rule information of the first marketing activity is determined, and then the logic rule expression is executed by calling the rule engine to obtain the screening data, so that the data screening efficiency is improved, and the expansibility and flexibility of a marketing system are improved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a data screening method, device, equipment and medium. Background Art

[0002] With the development of the big data environment, data is accumulated rapidly. It is very important to analyze the value contained in massive data and screen out valuable data. For example, in the e-commerce field, by analyzing the relevant data of users' browsing history and purchasing behavior, eligible user groups can be screened.

[0003] In the traditional way, hard coding is usually used to write the screening conditions directly in the code for screening. However, as the system becomes larger and larger, it is necessary to provide accurate user groups for different marketing activities. At this time, there are countless rule conditions, and the corresponding rule source data is also very large. If the hard coding method is still used, the underlying code must be modified and re-run, which will make the entire system difficult to maintain, affecting the scalability and flexibility of the system, resulting in low efficiency of data screening. Therefore, how to improve the efficiency of data screening has become a technical problem that needs to be solved urgently by those skilled in the art. Summary of the invention

[0004] Based on this, it is necessary to provide a data screening method, device, equipment and medium to improve the efficiency of data screening in response to the above technical problems.

[0005] A first aspect of an embodiment of the present application provides a data screening method, the data screening method comprising: Receiving a data call request sent by the first caller after calling the backend interface, wherein the data call request includes target rule information of the first marketing activity, and the target rule information includes rule fields and screening conditions; Generate a logical rule expression according to the rule field and the screening condition; Calling a rule engine to execute the logic rule expression to obtain screening data corresponding to the logic rule expression; The filtering data is returned to the first caller, so that the first caller executes the business process corresponding to the first marketing activity based on the filtering data.

[0006] A second aspect of an embodiment of the present application provides a data screening device, the data screening device comprising: A receiving module, configured to receive a data call request sent by the first caller after calling the backend interface, wherein the data call request includes target rule information of the first marketing activity, and the target rule information includes a rule field and a screening condition; A generating module, used for generating a logical rule expression according to the rule field and the screening condition; An acquisition module, used for calling a rule engine to execute the logic rule expression to obtain the screening data corresponding to the logic rule expression; An execution module is used to return the screening data to the first caller, so that the first caller executes the business process corresponding to the first marketing activity based on the screening data.

[0007] In a third aspect, an embodiment of the present invention provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the data screening method as described in the first aspect when executing the computer program.

[0008] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and wherein the computer program, when executed by a processor, implements the data screening method described in the first aspect.

[0009] In summary, the present application proposes a data screening method, apparatus, device and medium, which receives a data call request sent by a first caller after calling a backend interface, wherein the data call request includes target rule information of a first marketing campaign, and the target rule information includes rule fields and screening conditions. According to the rule fields and the screening conditions, a logical rule expression is generated, and a rule engine is called to execute the logical rule expression to obtain screening data corresponding to the logical rule expression, and the screening data is returned to the first caller, so that the first caller executes the business process corresponding to the first marketing campaign based on the screening data. The present application improves the efficiency of data screening by determining the target rule information of the first marketing campaign, and then calls the rule engine to execute the logical rule expression to obtain the screening data, thereby increasing the scalability and flexibility of the marketing system. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0011] Figure 1 is a flow chart of a data screening method provided in one embodiment of the present invention; Figure 2 is another flow chart of a data screening method provided in one embodiment of the present invention; Figure 3 is a structural schematic diagram of a data screening device provided in one embodiment of the present invention; Figure 4 It is a structural schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0013] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0014] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0015] As used in the present specification and the appended claims, the term “if” may be interpreted as “when” or “uponce” or “in response to determining”, depending on the context. Similarly, the phrases “if it is determined” or “if matched to [described condition or event]” may be interpreted as meaning “upon determination” or “in response to determination” or “uponce matched to [described condition or event]” or “in response to matching to [described condition or event]”, depending on the context.

[0016] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0017] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0018] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0019] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0020] Figure 1 is a flow chart of a data screening method according to an exemplary embodiment of the present application. Figure 1 As shown, the data screening method is implemented through the following steps.

[0021] S101: receiving a data call request sent by a first caller after calling a backend interface, wherein the data call request includes target rule information of a first marketing activity, and the target rule information includes rule fields and screening conditions.

[0022] In some embodiments, the first caller generally refers to the client that initiates the data request or a group of qualified users, customers or other target objects, which mainly depends on the specific business needs of the activity, and this application does not make any restrictions on this. A back-end interface is set in the system, and the system calls the back-end interface to receive the data call request sent by the first caller to the back-end interface, so that the system can obtain the required data or perform specific operations according to the data call request. The back-end interface is an API interface. After receiving the data call request sent by the first caller, this API interface should parse the data call request sent from the first caller by writing code in the system, and extract the target rule information of the first marketing activity. Since the target rule information includes rule fields and filtering conditions, the system verifies the received target rule information of the first marketing activity to ensure that the format of the rule fields and filtering conditions is correct and legal. Then, the corresponding operation is performed according to the target rule information, such as querying the database, applying the rule engine, etc. The system then performs the rule matching operation according to the received rule fields and filtering conditions to find the qualified data or records. Finally, according to the result of rule matching, a corresponding data response is generated, and finally returned to the first caller through the API interface, so that the first caller can execute the business process corresponding to the first marketing activity based on the filtered data, for example, sending targeted promotional information or adjusting the advertising delivery strategy based on the filtered qualified user groups.

[0023] As an optional embodiment, the target rule information also includes the field name, field type, field query strategy and field data source of the rule field.

[0024] In the embodiment of the present application, in the process of dynamically configured rule engine, it is necessary to configure the rule fields and screening conditions in the target rule information, wherein the screening conditions are different screening conditions required according to different marketing activities or business scenarios, and the ability to configure screening conditions is provided so that the system can be dynamically adjusted according to the specific situation to ensure flexibility. The rule fields may need to use different data fields for screening according to different business scenarios. By flexibly configuring the rule fields, the system can adapt to various business needs without hard-coding fixed fields. By managing the fields from the field name, field value type and field enumeration value, field processing, field query and field data source, etc., the dynamically configured crowd screening rule engine adopts a highly configurable architecture, which ensures the flexibility, adaptability, configurability, user-friendliness, execution efficiency and accuracy of the system. The field name refers to the name of each field, and the field type refers to the different input types that different fields may require, such as text, number, date, etc. By configuring the field input type, the system can adapt to different data types to ensure that the rules entered by the user are legal. Field enumeration values ​​refer to the predefined range of values ​​for some fields, such as product type, region, etc. By configuring the enumeration values ​​supported by the fields, the system can limit the selection range of users when configuring rules, reduce errors and improve the accuracy of configuration. Field processing, field query and field data source refer to the system's processing, query and processing of fields. For example: Field name: User age is between 18 and 50, field selection: User age Field type: Numeric type, field enumeration: None, filter condition: <50, >18 or rule name: Loan not settled, field selection: IOU status, field type: Enumeration, field enumeration: Success, Failure, Overdue, Settled, filter condition: != Settled, the administrator can use the system visualization page to arrange and combine the above core fields to obtain a set of rules that meet their expectations. This highly configurable method ensures the adaptability and flexibility of the system in different business scenarios.

[0025] It should be noted that the rule field may also include other fields, which may be specifically set according to actual needs, and this embodiment does not impose any limitation on this.

[0026] As an optional embodiment, the first caller configures target rule information of the first marketing campaign in the following manner, including: Receiving a custom configuration rule input by a user, and extracting an option identifier from the first marketing activity, wherein the custom configuration rule is rule information of a plurality of different combinations; Acquire preset option configuration data corresponding to the option identifier; Performing availability check on the custom configuration rule; If the custom configuration rule passes the availability check, the target rule information of the first marketing activity is configured according to the preset option configuration data and the custom configuration rule.

[0027] In the embodiment of the present application, since there are some configuration elements in the marketing activities that have only limited configuration options, in order to simplify the configuration operation of the configuration personnel, several configuration options are set for such configuration elements for the configuration personnel to choose, and the configuration client only needs to record the option identifier corresponding to the configuration option selected by the configuration personnel when configuring such configuration elements. However, for some configuration elements in the marketing activities, the configuration options are not limited, so it is necessary to set a custom configuration rule for the configuration personnel in the configuration client to receive user input, extract the option identifier from the first marketing activity, and then obtain the preset option configuration data corresponding to the option identifier, and perform an availability check on the custom configuration rule to verify whether the custom configuration rule matches the first marketing activity. If it meets the requirements, the server can configure the target rule information of the first marketing activity according to the preset option configuration data and the custom configuration rule through the availability check, that is, the system administrator selects and configures the rules applicable to the activity through the interface of the rule engine, such as which users meet the participation conditions. All rules exist in the form of configuration and can be managed through the UI or configuration files. This method allows the rules to be dynamically modified without changing the underlying code.

[0028] As an optional embodiment, after configuring the target rule information of the first marketing activity, the following steps are included: receiving updated rule information of the first marketing activity; According to the update rule information, the target rule information of the first marketing activity in the rule database is updated to obtain an updated target update rule.

[0029] In the embodiment of the present application, an interface or page for receiving updated rule information is established so that the person in charge or relevant personnel of the first marketing activity can input updated rule information, including rule fields and screening conditions that need to be updated. The received updated rule information is parsed in the back-end system to extract the rule fields and screening conditions that need to be updated. Based on the parsed updated rule information, the target rule information of the first marketing activity in the rule database is updated. After the update is completed, an updated target rule is generated based on the updated rule information to ensure the correctness and completeness of the rule information.

[0030] The updated target rules are verified and tested to ensure that the updated rules meet business requirements and can be used normally. It can be seen that the system allows new rules to be loaded or existing rules to be updated at runtime without redeploying the system, which avoids the rigidity problem in the hard-coding method and improves the flexibility and responsiveness of the system.

[0031] In an embodiment of the present application, by receiving a data call request sent by the first caller, and processing and responding according to the target rule information, matching and operation of the target rule information are achieved, so that the data in the marketing activities can be subsequently screened, thereby improving the timeliness of subsequent screening of data and increasing the scalability and flexibility of the marketing system.

[0032] S102: Generate a logic rule expression according to the rule field and the screening condition.

[0033] In the embodiment of the present application, the logic rule expression refers to a combination of numbers, operators, number grouping symbols (brackets), free variables and bound variables, etc., in a meaningful arrangement method that can obtain numerical values, wherein the expression refers to a meaningful formula that connects data of the same type (such as constants, variables, functions, etc.) with operators according to certain rules. It can be seen that after responding to the data call request, the system can obtain the request parameters and the request type corresponding to the request parameters from the data call request, and then determine the target rule information of the current first marketing activity according to the request parameters and the request type corresponding to the request parameters. Since the target rule information includes rule fields and filtering conditions, the corresponding logic rule expression can be generated by combining the rule fields and filtering conditions, and then the filtering data corresponding to the logic rule expression can be obtained.

[0034] As an optional embodiment, a logical rule expression is generated according to the rule field and the screening condition, including: Constructing a function and a parameter list based on the rule fields and the filter conditions; A logical regular expression is generated based on the function and the parameter list.

[0035] Specifically, functions can be operators, including arithmetic operators (such as +, -, *, / , etc.), comparison operators (such as >, <, =, etc.), logical operators (such as &&, ||, !, etc.), and other operators. They can also include calculation functions (such as avg(), max(), len(), concat(), datediff(), etc.). The parameter list is an array structure, which is the parameter of the function. Each element in the array can be the simplest structure (containing parameter type and parameter value) or a complex structure (the calculated value of the function and parameter list). Simple structures need to identify the parameter type, whether it is a constant or a variable, and the parameter value is the value of the specific parameter. For example, constants can be 123, 2021-09-13, etc., and variables can be abc, col-x, etc.; complex structures can recursively nest functions and parameter lists in multiple layers to express complex expressions.

[0036] In the scheme of the present application, the judgment conditions of the rules can be described by logical rule expressions. In order to unify the judgment conditions of the rules, the logical rule expressions in the present application can be specifically embodied in the form of a set of functions and parameter lists. By dividing the logical rule expression into functions and parameters, and then substituting the parameters into the corresponding functions, the logical rule expression for rule judgment processing can be obtained. The rule fields and screening conditions construct functions and parameter lists, and the functions in the logical rule expressions can be specifically constructed according to the rule fields such as functions and operators and the branch conditions, loop conditions and other conditional contents in the screening conditions, and the parameter lists in the logical rule expressions can be constructed according to the rule fields such as input variables. The logical rule expression can be obtained by combining the two. The logical rule expression here is embodied in the form of a combination of functions and parameter lists. In this embodiment, by dividing the logical operation expression into two parts, functions and parameters, the expressions of different structures can be effectively formatted in a unified manner, thereby realizing structured expression for the expression and improving the accuracy and efficiency of subsequent screening data.

[0037] In one embodiment, the logical rule expression can be combined with conditional statements, logical operators and rule fields to implement logical judgment and conditional screening of the rule. For example, the rule fields are: age; gender; income. Screening conditions: age is greater than or equal to 18 years old; gender is female; income is greater than 50,000 yuan. Based on the above rule fields and screening conditions, a simple logical rule expression can be generated: IF age>=18 AND gender='female' AND income>50000 THEN Output: "Meets the requirements of the rules" ELSE Output: "Does not meet the requirements of the rules" It can be seen that this logical rule expression indicates that when the data meets the conditions (age greater than or equal to 18 years old, gender is female, income is greater than 50,000 yuan), the output is "meets the rule requirements"; otherwise, the output is "does not meet the rule requirements". According to actual needs and rule complexity, different symbols, conditional statements and logical operators can be used to generate suitable logical rule expressions. If the rules are more complex, the logical operation functions provided in the rule engine or programming language can be used to build and execute rule expressions.

[0038] In another embodiment, the present application may use a multi-layer recursive nesting approach to generate a logical rule expression with a consistent format, thereby achieving structured expression of various complex expressions.

[0039] S103: calling a rule engine to execute the logic rule expression to obtain screening data corresponding to the logic rule expression.

[0040] In the embodiment of the present application, after obtaining the logical rule expression, in order to calculate the rule more effectively, the system executes the logical rule expression by calling the rule engine, thereby obtaining the screening data corresponding to the logical rule expression, so that the logical rule expression can flexibly configure parameters, and then can quickly screen and obtain data screening results under different business types, thereby saving time and labor costs. After the rule engine executes all rule conditions, it generates a data set that meets the conditions according to the matching of the conditions, and then the data can be output in a specific format, such as a data table, a file, an API response, etc., so that other systems or business processes can use it. The scheme of the present application is applied to the field of marketing big data, and is specifically applied to the screening scene in marketing big data. At this time, the user can select the rule field, screening conditions, data sources and other information on the operation interface of the terminal, and then the server executes the logical rule expression through the rule engine to generate the screening results. Therefore, the rule engine will parse these configured rules and convert them into executable logic. Different marketing activities can load different rule combinations for screening as needed.

[0041] Among them, the data source can be one or more. When there is only one data source, the logical rule expression can be executed in the corresponding data source through the rule engine to filter the data in the data source and obtain the filtering result. If there are multiple data sources, it is necessary to first take out the required data specified by the logical rule expression from the data source and then process it, so as to realize the rule calculation process in the multi-data source scenario. When selecting the data source, ensure that the required technical field data can be accessed by the rule engine. And it involves data cleaning, conversion and integration.

[0042] For each field, there may be different data source access strategies and expected field query strategies, so we access and abstract the data source, and different fields can have their own unique processing strategies. Specifically, when users need to use the rule engine to filter crowd data, they can set the rule fields and filtering conditions and other information on the operation interface on the terminal, and select the corresponding data source to form the rule configuration information, and then submit these rule configuration information to the server for rule configuration processing. The server will obtain data from the data source specified by the data source information, and perform corresponding rule calculations to obtain the filtered data results.

[0043] In the embodiment of the present application, since there are multiple logical rule expressions, there are different screening strategies. For different screening strategies, we use a thread pool to perform data screening on multiple fields in parallel, and finally assemble the results of multiple thread processing and return them to the terminal. The thread pool is a mechanism for managing and reusing threads, which can improve the performance and efficiency of multi-threaded applications. The thread pool contains a set of pre-created threads that can be reused to perform tasks without creating and destroying threads each time. In this way, the present application improves the efficiency of parallel data processing and ensures the diversification and accuracy of data screening. In addition, the screened screening results can be returned to the terminal afterwards. Because this result may be a group of qualified users, customers or other target objects, depending on the specific business needs of the activity.

[0044] In the embodiment of the present application, a mature rule engine is set up to uniformly manage all filtering rules, and then the rule engine is called to execute the logical rule expression to obtain the filtering data corresponding to the logical rule expression, thereby achieving complete decoupling of fields and filtering conditions, which not only increases the flexibility and scalability of the marketing tool system, but also completely separates the main process of marketing activities and data screening, improves the system response speed, and thus improves the efficiency of data screening.

[0045] S104: Return the filtered data to the first caller, so that the first caller executes the business process corresponding to the first marketing activity based on the filtered data.

[0046] In an embodiment of the present application, after obtaining the screening data corresponding to the logical rule expression, the screened data is sorted and formatted according to the data format expected by the first caller to ensure that the data can be correctly parsed and processed by the first caller, and the processed screening data is returned to the first caller by setting an API interface or other means. When returning the data, the screening data itself and related metadata information may be included. In ensuring that the data transmission process is secure and reliable, encrypted transmission or other security measures may be used to prevent data leakage or tampering, thereby ensuring that the data can be smoothly transmitted and the first caller can execute the business process corresponding to the first marketing activity based on the screening data, which can effectively ensure the accuracy of the data screening process.

[0047] As an optional embodiment, after returning the filtered data to the first caller, the method further includes: Determine a data storage identifier of the filtered data based on a preset snowflake algorithm; The screening data with the data storage identifier is stored in the screening result table.

[0048] In the embodiment of the present application, the Snowflake algorithm is a distributed unique ID generation algorithm that can generate a globally unique and ordered ID. The core idea of ​​the Snowflake algorithm is to divide a 64-bit ID into multiple parts, each part represents different information, and is composed of a timestamp (41 bits), a work machine ID (10 bits) and a serial number (12 bits). Each data storage requirement corresponds to a corresponding data According to the storage identifier, the data storage identifier is an identification number for calibrating the data storage location, which can be calibrated with a specific number or a special letter, and the embodiment of the present application is not limited. The present application is based on a preset snowflake algorithm to determine the data storage identifier of the filtered data, and stores the filtered data with the data storage identifier in the filtering result table of the rule engine, so as to facilitate the subsequent query of the data filtering results.

[0049] In summary, the present application proposes a data screening method, apparatus, device and medium, which receives a data call request sent by a first caller after calling a backend interface, wherein the data call request includes target rule information of a first marketing campaign, and the target rule information includes rule fields and screening conditions. According to the rule fields and the screening conditions, a logical rule expression is generated, and a rule engine is called to execute the logical rule expression to obtain screening data corresponding to the logical rule expression, and the screening data is returned to the first caller, so that the first caller executes the business process corresponding to the first marketing campaign based on the screening data. The present application improves the efficiency of data screening by determining the target rule information of the first marketing campaign, and then calls the rule engine to execute the logical rule expression to obtain the screening data, thereby increasing the scalability and flexibility of the marketing system.

[0050] Figure 2 is a flow chart of another data screening method according to an exemplary embodiment of the present application. Figure 2 As shown, the data screening method includes the following steps: S201: receiving a data query request sent by a second caller after calling a backend interface, wherein the data query request includes activity content information of a second marketing activity; S202: Determine, based on the activity content information, whether there is second target rule information matching the activity content information in a rule database, wherein the rule database stores target rule information of the first marketing activity sent by the first caller and other rule information; S203: if the rule database contains second target rule information matching the activity content information, generating a second logic rule expression according to the second target rule information; S204: Calling a logic rule to execute the second logic rule expression to obtain screening data corresponding to the second logic rule expression; S205: Returning the filtered data to the second caller, so that the second caller executes the business process corresponding to the second marketing activity based on the filtered data; S206: If the second target rule information matching the activity content information does not exist in the rule database, new target rule information corresponding to the activity content information is generated, and the rule database is updated using the new target rule information.

[0051] In the embodiment of the present application, the second caller may also generally refer to the client that initiates the data request or a group of qualified users, customers or other target objects, which mainly depends on the specific business needs of the activity, and the present application does not make any restrictions on this. In the present application, the first caller and the second caller are different clients or users. Through the API backend interface, the data query request sent by the second caller is received and parsed, and the data query request sent by the second caller can also be received through other communication methods, such as message queues, etc., and the present application does not make any restrictions on this. The data query request includes the activity content information of the second marketing activity, and the activity content information of the second marketing activity generally includes the activity name, activity time, activity location, activity purpose, activity content, target audience group, budget and resources, preferential methods and participation methods. For example, the activity name: Summer Promotion, the activity content: Purchase designated goods for more than 200 yuan to receive a beautiful gift, the quantity is limited, first come first served. This information helps to ensure the smooth progress of the activity and provides a basis for subsequent screening. Activities can be clustered into corresponding clusters based on the activity name, activity time, activity location, activity purpose, activity content, target audience, budget and resources, preferential methods and participation methods of the activity content information, such as insurance, financial management, fund and health marketing activities, so as to facilitate reasonable screening with appropriate rule information for different types of activities.

[0052] In marketing tools, when a marketing campaign needs to screen the crowd according to different campaign types and rules, a mature rule engine is needed to uniformly manage all crowd screening rules. The rule is a combination of conditions and source data. Since the rule database stores the target rule information and other rule information of the first marketing campaign sent by the first caller, it is necessary to determine whether there is a second target rule information matching the campaign content information in the rule database based on the campaign content information. If so, an appropriate logical rule expression is generated based on the second target rule information. This can be a specific rule engine language or rule expression, so that the generated second logical rule expression is executed using the rule engine or a custom rule execution engine to match the screening data. According to the execution result of the second logical rule expression, qualified screening data is obtained and returned to the second caller so that the second caller can execute the business process of the second marketing activity. If it does not exist, new target rule information corresponding to the activity content information is generated, and the rule database is updated using the new target rule information. The generation method of the new target rule information is not limited. For example, the activity content information can be processed using a Seq2Seq algorithm (a variant of a convolutional neural network) to obtain new target rule information corresponding to the activity content information, and the target rule information can be added to the rule database to complete the update of the rule database, so that the updated rule database has flexibility and response speed, thereby improving the efficiency and effectiveness of rule management.

[0053] It should be noted that this process involves multiple links such as data communication, rule management, and logic processing. The specific implementation method may be adjusted according to different system architectures and business requirements.

[0054] This application determines the second target rule information that matches the activity content information from the rule database according to the activity content information, and then executes the second logical rule expression by calling the rule engine to obtain the screening data for subsequent marketing activities, such as sending targeted promotional information or adjusting advertising delivery strategies. The system will also adjust the rules based on the feedback of the marketing activities, thereby improving the efficiency and accuracy of data screening and increasing the scalability and flexibility of the marketing system.

[0055] See also Figure 3 , Figure 3 This is a schematic diagram of the structure of a data screening device provided by an embodiment of the present invention. Figure 1 as well as Figure 1 For the convenience of explanation, only the parts related to this embodiment are shown. Figure 3 The data screening device 30 includes: a receiving module 31, a generating module 32, an acquiring module 33, and an executing module 34.

[0056] The receiving module 31 is used to receive a data call request sent by the first caller after calling the backend interface, wherein the data call request includes target rule information of the first marketing activity, and the target rule information includes rule fields and screening conditions; A generating module 32, for generating a logic rule expression according to the rule field and the screening condition; An acquisition module 33 is used to call a rule engine to execute the logic rule expression to obtain the screening data corresponding to the logic rule expression; The execution module 34 is used to return the screening data to the first caller, so that the first caller executes the business process corresponding to the first marketing activity based on the screening data.

[0057] In the embodiment of the present application, the data screening device 30 is also used for: Receiving a data query request sent by the second caller after calling the backend interface, wherein the data query request includes activity content information of the second marketing activity; Determining, based on the activity content information, whether there is second target rule information matching the activity content information in a rule database, wherein the rule database stores target rule information of the first marketing activity sent by the first caller and other rule information; If the rule database contains second target rule information matching the activity content information, generating a second logic rule expression according to the second target rule information; Calling a rule engine to execute the second logical rule expression to obtain screening data corresponding to the second logical rule expression; Returning the filtered data to the second caller, so that the second caller executes the business process corresponding to the second marketing activity based on the filtered data; If the second target rule information matching the activity content information does not exist in the rule database, new target rule information corresponding to the activity content information is generated, and the rule database is updated using the new target rule information.

[0058] In the embodiment of the present application, the receiving module 31 is specifically used for: The target rule information also includes the field name, field type, field query strategy and field data source of the rule field.

[0059] In the embodiment of the present application, the receiving module 31 is further used for: The first caller configures target rule information of the first marketing activity in the following manner, including: Receiving a custom configuration rule input by a user, and extracting an option identifier from the first marketing activity, wherein the custom configuration rule is rule information of a plurality of different combinations; Acquire preset option configuration data corresponding to the option identifier; Performing availability check on the custom configuration rule; If the custom configuration rule passes the availability check, the target rule information of the first marketing activity is configured according to the preset option configuration data and the custom configuration rule.

[0060] In the embodiment of the present application, the receiving module 31 is further used for: receiving updated rule information of the first marketing activity; According to the update rule information, the target rule information of the first marketing activity in the rule database is updated to obtain an updated target update rule.

[0061] In the embodiment of the present application, the generating module 32 is specifically used for: Constructing a function and a parameter list based on the rule fields and the filter conditions; A logical regular expression is generated based on the function and the parameter list.

[0062] In the embodiment of the present application, the execution module 34 is specifically used to: Determine a data storage identifier of the filtered data based on a preset snowflake algorithm; The screening data with the data storage identifier is stored in the screening result table.

[0063] It should be noted that the information interaction, execution process and other contents between the above-mentioned units are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0064] Figure 4 Schematic diagram of the structure of a computer device provided by an embodiment of the present invention. Figure 4 As shown, the computer device of this embodiment includes: at least one processor ( Figure 4 Only one is shown in the figure), a memory, and a computer program stored in the memory and executable on at least one processor, wherein when the processor executes the computer program, the steps in any of the above-mentioned data screening embodiments are implemented.

[0065] The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that Figure 4These are merely examples of computer devices and do not constitute limitations on the computer devices. The computer devices may include more or fewer components than those shown in the figure, or a combination of certain components, or different components. For example, they may also include a network interface, a display screen, and an input device.

[0066] In one embodiment, a computer-readable storage medium is provided. When the instructions in the computer-readable storage medium are executed by a processor in a computer device, the computer device can perform the steps of any embodiment of a data screening disclosed in the present invention, which will not be repeated here. The computer-readable storage medium can be non-volatile or volatile.

[0067] The processor may be a CPU, or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0068] The memory includes a readable storage medium, an internal memory, etc., wherein the internal memory may be the memory of a computer device, and the internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of a computer device, and in other embodiments, it may also be an external storage device of the computer device, for example, a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), etc. equipped on the computer device. Further, the memory may also include both an internal storage unit of the computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of a computer program, etc. The memory may also be used to temporarily store data that has been output or is to be output.

[0069] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0070] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the above-mentioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0071] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A data screening method, characterized in that: include: Receiving a data call request sent by the first caller after calling the backend interface, wherein the data call request includes target rule information of the first marketing activity, and the target rule information includes rule fields and screening conditions; Generate a logical rule expression according to the rule field and the screening condition; Calling a rule engine to execute the logic rule expression to obtain screening data corresponding to the logic rule expression; The filtering data is returned to the first caller, so that the first caller executes the business process corresponding to the first marketing activity based on the filtering data.

2. The method according to claim 1, characterized in that The target rule information also includes the field name, field type, field query strategy and field data source of the rule field.

3. The method according to claim 1, characterized in that The first caller configures target rule information of the first marketing activity in the following manner, including: Receiving a custom configuration rule input by a user, and extracting an option identifier from the first marketing activity, wherein the custom configuration rule is rule information of a plurality of different combinations; Acquire preset option configuration data corresponding to the option identifier; Performing availability check on the custom configuration rule; If the custom configuration rule passes the availability check, the target rule information of the first marketing activity is configured according to the preset option configuration data and the custom configuration rule.

4. The method according to claim 3, characterized in that After configuring the target rule information of the first marketing activity, the method includes: receiving updated rule information of the first marketing activity; According to the update rule information, the target rule information of the first marketing activity in the rule database is updated to obtain an updated target update rule.

5. The method according to claim 1, characterized in that The method further comprises: Receiving a data query request sent by the second caller after calling the backend interface, wherein the data query request includes activity content information of the second marketing activity; Determining, based on the activity content information, whether there is second target rule information matching the activity content information in a rule database, wherein the rule database stores target rule information of the first marketing activity sent by the first caller and other rule information; If the rule database contains second target rule information matching the activity content information, generating a second logic rule expression according to the second target rule information; Calling the rule engine to execute the second logical rule expression to obtain screening data corresponding to the second logical rule expression; Returning the filtered data to the second caller, so that the second caller executes the business process corresponding to the second marketing activity based on the filtered data; If the second target rule information matching the activity content information does not exist in the rule database, new target rule information corresponding to the activity content information is generated, and the rule database is updated using the new target rule information.

6. The method according to claim 1, characterized in that The step of generating a logical rule expression according to the rule field and the screening condition includes: Constructing a function and a parameter list based on the rule fields and the filter conditions; A logical regular expression is generated based on the function and the parameter list.

7. The method according to claim 1, characterized in that After returning the filtered data to the first caller, the method further comprises: Determine a data storage identifier of the filtered data based on a preset snowflake algorithm; The screening data with the data storage identifier is stored in the screening result table.

8. A data screening device, characterized in that: include: A receiving module, configured to receive a data call request sent by the first caller after calling the backend interface, wherein the data call request includes target rule information of the first marketing activity, and the target rule information includes a rule field and a screening condition; A generating module, used for generating a logical rule expression according to the rule field and the screening condition; An acquisition module, used for calling a rule engine to execute the logic rule expression to obtain the screening data corresponding to the logic rule expression; An execution module is used to return the screening data to the first caller, so that the first caller executes the business process corresponding to the first marketing activity based on the screening data.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the data screening method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the data screening method according to any one of claims 1 to 7 are implemented.

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